VEHICLE DETECTION AND CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGES

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "VEHICLE DETECTION AND CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGES"

Transcription

1 VEHICLE DETECTION AND CLASSIFICATION FROM HIGH RESOLUTION SATELLITE IMAGES Lizy Araham a, *, M.Sasikumar a Dept. o Electronics & Communication Engineering, Lal Bahadur Shastri Institute o Technology or Women, Trivandrum, Kerala, India Lal Bahadur Shastri Centre or Science and Technology, Trivandrum, Kerala, India Commission VI, WG VI/4 KEY WORDS: Region o Interest, Satellite Imaging Corporation, Bright vehicles, Otsu s threshold, SPOT-5, Connected Component Laeling, Cars, Trucks. ABSTRACT: In the past decades satellite imagery has een used successully or weather orecasting, geographical and geological applications. Low resolution satellite images are suicient or these sorts o applications. But the technological developments in the ield o satellite imaging provide high resolution sensors which expands its ield o application. Thus the High Resolution Satellite Imagery (HRSI) proved to e a suitale alternative to aerial photogrammetric data to provide a new data source or oject detection. Since the traic rates in developing countries are enormously increasing, vehicle detection rom satellite data will e a etter choice or automating such systems. In this work, a novel technique or vehicle detection rom the images otained rom high resolution sensors is proposed. Though we are using high resolution images, vehicles are seen only as tiny spots, diicult to distinguish rom the ackground. But we are ale to otain a detection rate not less than 0.9. Thereater we classiy the detected vehicles into cars and trucks and ind the count o them. 1. INTRODUCTION Eorts to extract inormation rom imagery have een in place ever since the irst photographic images were acquired. Low resolution satellite images such as that otained rom LANDSAT, MODIS and AVHRR sensors provide only a vague idea aout the scenes and will not provide any inormation aout the ojects ound in the scene. Images otained rom these types o sensors can e used only or weather orecasting or meteorological applications. For eature extraction and oject detection prolems high resolution images is a asic requirement. High resolution satellites like CARTOSAT, IKONOS, QuickBird, SPOT and WorldView provide detailed inormation aout the ojects as that o aerial images. The panchromatic and o QuickBird images reaches up to 60cm resolution which is as good as aerial images. Recently launched (August 13, 2014) WorldView-3 provides commercially availale panchromatic imagery o 0.31 m resolution. Though the availaility o high resolution satellite images accelerate the process o oject detection and automate such applications, vehicle detection rom satellite images are still a challenging task. This is ecause, even in high spatial resolution imagery, vehicles are seen as minute spots which are unidentiiale rom the oreground regions to detect. Classiying the detected vehicles is more serious as it is sometimes unale to distinguish large and small vehicles using naked eye itsel even rom high resolution images. Seeing the reerence images in our work, this prolem is clearly identiied. Earlier many researches have een perormed on vehicle detection in aerial imagery (Hinz, S., 2005; Schlosser, C., Reiterger, J. & Hinz, S., 2003; Zhao, T., & Nevatia, R.., 2001) and later on the work is extended or satellite imagery. But the methods used or aerial imagery can t e directly applied or satellite images since vehicles are more vivid in aerial images. Fig.1 shows an aerial image o 0.15m resolution. Figures 2, 3 and 4 are satellite images o resolutions 0.8m, 0.61m and 0.46m respectively otained rom dierent sensors. It is clear that even y using high resolution satellite images, vehicles are diicult to distinguish rom the scene. The most distinguished works in this ield include morphological transormations or the classiication o pixels into vehicle and non-vehicle targets. The work done y Jin, X. and Davis, C.H. (2007) uses a morphological shared-weight neural network (MSNN) to learn an implicit vehicle model and classiy pixels into vehicles and non-vehicles. A vehicle image ase lirary was uilt y collecting a numer o cars manually rom test images. The process is time consuming ecause o neural networks and there is an extra urden o creating an image lirary. Zheng, H., and Li, L. (2007) suggested another morphology ased algorithm using 0.6 meter resolution QuickBird panchromatic images to detect vehicles. Zheng, H., Pan, L. and Li, L. (2006) used similar approach as that o Jin, X. and Davis, C.H., ut with increased accuracy. Recent works in this area includes adaptive oosting classiication technique (Leitlo, J., Hinz, S. & Stilla, U., 2010) or the updation o weights and an area correlation method (Liu, W., Yamazaki, F. & Vu, T. T., 2011) to detect vehicles rom satellite images in which very good accuracy is achieved ut the classiication stage is not included. The most recent work in this ield is y Zheng,Z. et al. (2013) where the quality percentage reaches 92%, ut with aerial images o very high resolution having 0.15m. The method uses top-hat and ot-hat transormations successively with Otsu s thresholding or etter accuracy. * Corresponding author. doi: /isprsannals-ii

2 ISPRS Annals o the Photogrammetry, Remote Sensing and Spatial Inormation Sciences, Volume II-1, 2014 The specialty o our work is, vehicles are detected even rom satellite images o 2.5m resolution with acceptale accuracy. Both right and dark vehicles are detected using the proposed method and then it is classiied as cars and trucks. Results presented in the work reveals that the detection percentage reaches 90% irrespective o the poor quality o vehicles in satellite images. Figure 4: WorldView-2 RGB Image o Eiel Tower, Paris, France (0.46m) 2. METHODOLOGY Figure 1: Highway image o o 0.15 m resolution [courtsey: Zheng,Z. et al. (2013)] The method or vehicle detection consists o ive steps: Image pre-processing is the irst stage where a satellite image is converted into a greyscale image. Secondly, region o interest having roadways alone is extracted rom the road area image. Next, or the segmentation process, multiple thresholding which depends on the statistical properties o the image is chosen or inding right vehicles. Detecting dark vehicles rom the extracted segment is the third stage. Classiication o vehicles and inding their count are the inal stage o the automated approach. The algorithms developed were implemented, tested and analyzed using MATLAB sotware. The test images are otained rom Satellite Imaging Corporation, Texas, USA which is the oicial Value Added Reseller (VAR) o imaging and geospatial data products. The simpliied lock diagram o the system is shown in ig. 5. Figure 2: IKONOS 0.8m Natural Color Image o Nakuru, Kenya (taken on Feruary 2008) Figure 5: Overall Flow o the System 2.1 Pre-processing the Input Image Figure 3: Quickird(0.61m) RGB Image o Duai, UAE (taken on Feruary 2010) Satellite images are otained as panchromatic (greyscale), natural colour (RGB) and multispectral ands. No preprocessing stage is needed or panchromatic images. For applying the vehicle detection algorithm or satellite natural colour images, it is irst converted to greyscale. Multispectral images have 4 ands where the ourth and is Near Inra-Red (NIR). This and is not used in our algorithm and can e discarded and inally converted to greyscale image. doi: /isprsannals-ii

3 2.2 Region o Interest (ROI) Extraction Most o the road segments may not e straight in the concerned satellite image. Thereore, eore going directly to the region o interest segmentation we have to rotate the image in such a way that the road segment should e 0 0 (ig.18) with respect to the horizontal plane. Ater that selection o desired region o interest is done. The procedure is as ollows: (i)rotating the image (a)enter the angle o rotation. i we are rotating it in the clockwise direction, the angle o rotation is negative and or anti-clockwise direction it will e positive. () Convert the value rom cell string to string. (c) Convert the value rom string to numeric. (d)display the rotated image. (ii) Selecting the co-ordinates o the ROI (a) Select the (x,y) coordinates o the upper let corner point o the ROI. ()Select the (x,y) coordinates o the lower right corner point o the ROI. (c)merge the array x and y. (d)convert the values rom numeric to string. (e)display the selected ROI co-ordinates. (iii) Region o interest Extraction (a)sutract x co-ordinates to ind width ()Sutract y co-ordinates to ind height (c)crop the image using the otained width and height o the image. (d)display the segmented region which is the ROI. The igure shown elow (ig.6) is a SPOT-5 panchromatic image o 2.5m resolution which is a highway in Oklahoma City. Figure 7: Selecting the Coordinates o the Image Figure 8: Displaying the Coordinates o the Image Figure 9: Region o Interest or Vehicle Detection The process will extract the road segment alone reducing the time required or vehicle detection process. But i the entire process have to e ully automated the road segments must e extracted without any human intervention. Many works are proposed or road detection rom satellite images (Mokhtarzade,M. & Zoej, M. J. V., 2007; Movaghati. S, Moghaddamjoo.A & Tavakoli.A,, 2010). The authors have also done a work on ully automatic road network extraction using uzzy inerence system (L.Araham and M. Sasikumar, 2011). 2.3 Automatic Road Detection using FIS Figure 6: SPOT-5 Panchromatic Image (2.5m) As the road segment seen in the image is parallel to the horizontal plane, the angle o rotation is taken as 0 0. The upper let and lower right corners o the ROI selected are shown in ig.7. The ROIs are selected y mouse clicking using MATLAB. The (x,y) co-ordinates o the selected region is given in ig.8. The width and height o the region are calculated y sutracting the corresponding co-ordinates. Ater otaining the width and height o the region, the ROI is displayed in ig. 9. Ater evaluating a numer o satellite images and their mean and standard deviation, 11 rules are ormulated or decision making in order to develop a uzzy inerence system (FIS): a. I (mean is low) and (stddev is low) and (hough is not line) then (output is not road). I (mean is low) and (stddev is low) and (hough is line) then (output is road unlikely) c. I (mean is low) and (stddev is high) and (hough is not line) then (output is not road) d. I (mean is low) and (stddev is high) and (hough is line) then (output is not road) e. I (mean is average) and (stddev is low) and (hough is not line) then (output is not road). I (mean is average) and (stddev is low) and (hough is line) then (output is road) g. I (mean is average) and (stddev is high) and (hough is not line) then (output is not road) h. I (mean is average) and (stddev is high) and (hough is line) then (output is road unlikely) i. I (mean is high) and (stddev is low) and (hough is not line) then (output is not road) doi: /isprsannals-ii

4 j. I (mean is high) and (stddev is low) and (hough is line) then (output is road unlikely) k. I (mean is high) and (stddev is high) and (hough is line) then (output is road unlikely) The linguistic variales generated using MATLAB are shown elow: matrix M 2 is constructed using the maximum intensity pixel rom each row o M 1. The threshold T 1 is mean o this 1D matrix M 2. T 2 is the minimum value in the matrix M 2. For increasing the percentage o accuracy a third threshold T 3 is calculated which is the mean o irst two thresholds T 1 and T 2. The procedure is rieed in igure 14. Figure 10: Input linguistic variale- mean Figure 14: Finding Threshold Values Figure 11: Input linguistic variale- standard deviation The concept is ased on the act that on a highway, right vehicles have maximum intensity levels than any other ojects. Thereore we are considering the maximum intensity in each row o M 1 to calculate M 2 and therey the three thresholds T 1, T 2 and T 3. Thresholds T 1, T 2, and T 3 are used to convert the test image to three dierent inary images Image-1, Image-2 and Image-3. Fig.15 shows the three thresholded images or calculated threshold values o 200, 149 and 175 or T 1, T 2 and T 3 respectively. Figure 12: Input linguistic variale- hough The FIS output or ig.6 is given elow: (a) Image-1 (T 1 =200) Figure 13: Road Detected Image 2.4 Multiple Thresholding or inding Bright Vehicles Most o the cases, the intensity values o right vehicles are greater than the intensities o the ackground. Using this concept we can use a ixed threshold and pixels higher than this particular threshold corresponds to right vehicles. But some ojects or regions on roads, such as lane markers and road dividers may have similar intensity values as that o right vehicles. Also, each right vehicle may not have same range o intensity ecause o the images taken at dierent times due to sun elevation and azimuth angles and sensor angles. So it is etter to use more than a single threshold. But, as the numer o threshold values increases we get as many inary images, increasing the time taken or vehicle detection process. Thereore, to identiy only the vehicles and to avoid the detection o irrelevant ojects, three dierent thresholds T 1, T 2, and T 3 are used in this work. The third threshold T 3 is ixed as the comination o irst two thresholds T 1 and T 2 or getting more accurate results. In order to ind the three threshold values, consider the two dimensional matrix o image intensities M 1. A one dimensional () Image-2 (T 2 =149) (c)image-3 (T 3 =175) Figure 15: Thresholded Images From ig.15, it is understood that many irrelevant ojects are included in the thresholded images. Also some vehicles are common in the resultant images. In order to remove the irrelevant ojects and to extract the common ojects, the logical AND operation is perormed among the inary images as given in eqn. (1), (2) and (3). The new inary images are shown in ig.16. New Image-1 = itwise AND [Image-1, Image-2] (1) doi: /isprsannals-ii

5 New Image-2 = itwise AND [Image-1, Image-3] (2) New Image-3 = itwise AND [Image-2, Image-3] (3) (a) New Image-1 () New Image-2 (c) New Image-3 and ackground spreads is at its minimum. This can e achieved y inding a threshold with the maximum etween class variance and minimum within class variance. For this, the method check all pixel values in the image using equations 5 and 6 to ind out which one is est to classiy oreground and ackground regions, so that oreground regions are clearly distinguished rom the scene depending on the quality o the image. The within class variance is simply the sum o the two variances multiplied y their associated weights. Between class variance is the dierence etween the total variance (sum o ackground and oreground variances) o the image and within class variance Within Class Variance σ w = W σ + W σ (5) Between Class Variance σb = σ σw 2 2 = W( µ µ ) + W ( µ µ ) (where µ =W µ + W µ ) 2 = W W ( µ µ ) where weight, mean and variance o the image are represented y w, µ and σ 2. The oreground and ackground regions are represented as and. The detected dark vehicles are shown in ig.18. (6) Figure 16: New Segmented Images The inal right vehicle detected image (ig.17) is otained as the logical OR operation perormed among the new images (eqn.4) since the three intermediate images have dierent vehicles ecause o the three thresholds. As seen rom the images, some o the vehicles are common which is merged in this operation. Final Bright Vehicle Image = itwise OR [New Image-1, New Image-2, New Image-3] (4) Figure 18: Dark Vehicle Detected Image To avoid considering shadow o vehicles as dark vehicles, the right and dark vehicle detected images are added using a logical OR operation (eqn. 7). This results in comining right vehicles and their shadows together. The inal vehicle detected image is shown in ig. 19. Vehicle Detected Image = itwise OR [Bright Vehicle Detected Image, Otsu s Thresholded Image] (7) Figure 17: Bright Vehicle Detected Image 2.5 Otsu s Thresholding or inding Dark Vehicles For the detection o dark vehicles, the Otsu s threshold (Otsu, N., 1979) is used. Beore applying the Otsu s threshold, a sliding neighorhood operation is applied to the test image (Aurdal, L, Eikvil, L., Koren, H., Hanssen, J.U., Johansen, K. & Holden, M., 2007). In this method a 3-y-3 neighorhood o each and every pixel is selected. The neary pixel is replaced y the minimum intensity value o this 3x3 window. The result is a darker pixel compared to the earlier one. This operation is ollowed y Otsu s thresholding to get the resultant dark vehicle detected image. Otsu's thresholding method involves iterating through all the possile threshold values and calculating a measure o spread or the pixel levels each side o the threshold, i.e. the pixels that either alls in oreground or ackground. The aim is to ind the threshold value where the sum o oreground Figure 19: Resultant Vehicle Detected Image 2.6 Vehicle Classiication & Count Beore moving directly to the vehicle classiication stage, a morphological dilation operation is perormed as some vehicles may get splitted into parts ater segmentation operation. Dilation will comine these parts into a single vehicle, which increase the detection percentage o the vehicle detection algorithm. The structuring element used or the process is deined as eqn. (8): doi: /isprsannals-ii

6 SE = Vehicles are commonly rectangular in shape; we can t see an irregular shaped or circular shaped vehicle. Thereore the aove structuring element is suicient or our detection algorithm. From the detected vehicles, some o the parameters which are ale to classiy them as cars and trucks are calculated. In our work, width, height, and area o the detected vehicles are considered or the classiication stage. For that connected component laeling is perormed on the dilated image. First, taking into account o all the connected components in the reerence image, the average o each o these parameters is computed. Then, the three parameters or each and every detected vehicle are compared with the average values. I width, height, and area o the vehicle is greater than the average values it is considered as a truck, or else it is a car. The algorithm is given elow: 1) Dilate Vehicle Detected Image using structuring element SE. 2) Perorm Connected Component Laeling using 4- connected neighorhood. 3) Compute area, major axis (width) and minor axis (height) o laeled regions. 4) Otain mean o these parameters. 5) Check whether area greater than mean area and Major axis length greater than mean major axis length and Minor axis length greater than mean minor axis length. Yes Increment truck count y 1 No Increment car count y 1 6) Conversion rom numeric to string or car and truck count. 7) Display the count in the message ox. The ollowing igures (ig.20) show the car and truck counts or the vehicle detected image. (8) count o the vehicles; this is, y visually inspecting the region under study. The inerred results are given in tale 1. For all the reerence images, it is seen that, though the cars and trucks are not vivid even in the actual image, the results show that the detection rate is more than 90%. It is noted that or the very high resolution aerial image, since the vehicles are clearly seen, even y taking the entire image as ROI, the method is ale to detect and classiy vehicles with a detection rate o Figure21: IKONOS Panchromatic Image (1m) & ROI Figure 20: Car & Truck Counts o the ROI in the Reerence Image 3. EXPERIMENTAL RESULTS The results otained or other two panchromatic IKONOS (1m) and SPOT-5 (2.5m) images o highways in San Jose, CA and Barcelona, Spain are given elow. The ROI or IKONOS image is with an angle o rotation o and or SPOT-5 image is with an angle o rotation o The experimental method is also veriied with very high resolution aerial and satellite natural colour (RGB) images given in section I. The aerial image is taken as a whole as ROI since only road segment is shown in the image. The angle o rotation o the image is taken as 0 0. Part o the road segment is taken as ROI or other three satellite images with angle o rotations -36 0, and respectively. To measure the perormance o the algorithm, the results otained are compared with the manual Figure22: Fig.17 with an Angle o Rotation Figure 23: Car & Truck Counts o the ROI doi: /isprsannals-ii

7 Figure 28: Car & Truck Counts o the ROI Figure24: SPOT-5 Panchromatic Image (2.5m) & ROI Figure 25: Car & Truck Counts o the ROI Figure29: Quickird Greyscale Image (0.61m) & ROI Figure 26: Car & Truck Counts o Fig.1 (0.15m) Figure 30: Car & Truck Counts o the ROI Figure27: IKONOS Grayscale Image (0.8m) & ROI Figure 31: WorldView-2 Grayscale Image (0.46m) & ROI doi: /isprsannals-ii

8 Jin, X., and Davis, C.H., Vehicle detection rom high resolution satellite imagery using morphological shared-weight neural networks, International Journal o Image and Vision Computing, vol.25, no.9, pp , Figure 32: Car & Truck Counts o the ROI Zheng, H., and Li, L., An artiicial immune approach or vehicle detection rom high resolution space imagery, International Journal o Computer Science and Network Security, Vol.7, no.2, pp.67 72, Zheng, H., Pan, L. and Li, L., A morphological neural network approach or vehicle detection rom high resolution satellite imagery, Lecture Notes in Computer Science (I. King, J. Wang, L. Chan, and D.L. Wang, editors), Vol. 4233, Springer, pp Leitlo, J., Hinz, S., and Stilla, U., Vehicle Detection in Very High Resolution Satellite Images o City Areas, IEEE Trans. on Geoscience and Remote Sensing, vol.48, no.7, pp , Tale 1. Perormance Evaluation 4. CONCLUSION In this paper, a multistep algorithm is designed or detecting vehicles rom satellite images o dierent resolutions. The method also classiies and counts the numer cars and trucks in the image. The proposed method is ale to detect exact numer o cars and trucks even rom satellite images lower than 1m resolution in which vehicles are identiied as some noisy white spots. But roads having high density traic, there may e chances or increased error percentage since vehicles are very much closer in those cases. Also in this work vehicles are classiied only as cars and trucks. More numer o classes can e included like cars, small trucks, large trucks and uses. Availaility o very high resolution hyper spectral data will make evolutionary changes in the ield o eature extraction and can e used as a source to rectiy aove mentioned prolems. Further research should ocus on these areas and experiment with maximum resources availale to extract even the minute details rom satellite images. REFERENCES Hinz, S., Detection o vehicles and vehicle queues or road monitoring using high resolution aerial images, Proceedings o 9th World Multiconerence on Systemics, Cyernetics and Inormatics, Orlando, Florida, USA, pp.1-4, July, Schlosser, C., Reiterger, J. and Hinz, S., Automatic car detection in high resolution uran scenes ased on an adaptive 3D-model, Proceedings o the ISPRS Joint Workshop on Remote Sensing and Data Fusion over Uran Areas, Berlin, Germany, pp , Liu, W., Yamazaki, F., Vu, T. T., Automated Vehicle Extraction and Speed Determination From QuickBird Satellite Images, IEEE Journal o Selected Topics in Appl. Earth Oservations and Remote Sensing, vol.4, no.1, pp.1-8, Zheng,Z., Zhou,G., Wang,Y., Liu,Y., Li,X., Wang,X. and Jiang,L., A Novel Vehicle Detection Method With High Resolution Highway Aerial Image, IEEE Journal o Selected Topics in Appl. Earth Oservations and Remote Sensing, vol.6, no.6, pp , Mokhtarzade,M. and Zoej, M. J. V., Road detection rom high-resolution satellite images using artiicial neural networks", International Journal o Applied Earth Oservation and Geoinormation 9:32 40, Movaghati. S, Moghaddamjoo.A and Tavakoli.A, Road Extraction From Satellite Images Using Particle Filtering and Extended Kalman Filtering, IEEE Trans. Geosci. Remote Sens., vol. 48, no. 7, pp , July Lizy Araham and M. Sasikumar, Uran Area Road Network Extraction rom Satellite Images using Fuzzy Inerence System, Proc. International Conerence on Computational Intelligence and Computing Research (ICCIC 2011), Decemer 15-18, 2011, Kanyakumari, India, pp Otsu, N., "A threshold selection method rom graylevel histograms," IEEE Transactions. on Sys. Man Cyer., vol. 9, no. 1, pp , Aurdal, L, Eikvil, L., Koren, H., Hanssen, J.U., Johansen, K. and Holden, M. Road Traic Snapshot, Report -1015, Norwegian Computing Center, Oslo, Norway Zhao, T., and Nevatia, R., Car detection in low resolution aerial images, Proceedings o the IEEE International Conerence on Computer Vision, Vancouver, Canada, pp , July, doi: /isprsannals-ii

A RAPID METHOD FOR WATER TARGET EXTRACTION IN SAR IMAGE

A RAPID METHOD FOR WATER TARGET EXTRACTION IN SAR IMAGE A RAPI METHO FOR WATER TARGET EXTRACTION IN SAR IMAGE WANG ong a, QIN Ping a ept. o Surveying and Geo-inormatic Tongji University, Shanghai 200092, China. - wangdong75@gmail.com ept. o Inormation, East

More information

Traffic Monitoring using Very High Resolution Satellite Imagery

Traffic Monitoring using Very High Resolution Satellite Imagery Traffic Monitoring using Very High Resolution Satellite Imagery Siri Øyen Larsen, Hans Koren, and Rune Solberg Abstract Very high resolution satellite images allow automated monitoring of road traffic

More information

Classification-based vehicle detection in highresolution

Classification-based vehicle detection in highresolution Classification-based vehicle detection in highresolution satellite images Line Eikvil, Lars Aurdal and Hans Koren Norwegian Computing Center P.O. Box 114, Blindern NO-0314 Oslo, Norway Tel: +47 22 85 25

More information

MAPPING ROAD TRAFFIC CONDITIONS USING HIGH RESOLUTION SATELLITE IMAGES

MAPPING ROAD TRAFFIC CONDITIONS USING HIGH RESOLUTION SATELLITE IMAGES MAPPING ROAD TRAFFIC CONDITIONS USING HIGH RESOLUTION SATELLITE IMAGES S. Ø. Larsen a, *, J. Amlien a, H. Koren a, R. Solberg a a Norwegian Computing Center, P.O.Box 114 Blindern, 0314 Oslo, Norway - siri.oyen.larsen@nr.no,

More information

AUTOMATIC CROWD ANALYSIS FROM VERY HIGH RESOLUTION SATELLITE IMAGES

AUTOMATIC CROWD ANALYSIS FROM VERY HIGH RESOLUTION SATELLITE IMAGES In: Stilla U et al (Eds) PIA11. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 38 (3/W22) AUTOMATIC CROWD ANALYSIS FROM VERY HIGH RESOLUTION SATELLITE IMAGES

More information

Evaluation of the Use of High-Resolution Satellite Imagery in Transportation Applications

Evaluation of the Use of High-Resolution Satellite Imagery in Transportation Applications Evaluation of the Use of High-Resolution Satellite Imagery in Transportation Applications Final Report Prepared by: Rocio Alba-Flores Department of Electrical and Computer Engineering University of Minnesota

More information

Generation of Cloud-free Imagery Using Landsat-8

Generation of Cloud-free Imagery Using Landsat-8 Generation of Cloud-free Imagery Using Landsat-8 Byeonghee Kim 1, Youkyung Han 2, Yonghyun Kim 3, Yongil Kim 4 Department of Civil and Environmental Engineering, Seoul National University (SNU), Seoul,

More information

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features

Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with Panchromatic Textural Features Remote Sensing and Geoinformation Lena Halounová, Editor not only for Scientific Cooperation EARSeL, 2011 Multiscale Object-Based Classification of Satellite Images Merging Multispectral Information with

More information

Information Contents of High Resolution Satellite Images

Information Contents of High Resolution Satellite Images Information Contents of High Resolution Satellite Images H. Topan, G. Büyüksalih Zonguldak Karelmas University K. Jacobsen University of Hannover, Germany Keywords: satellite images, mapping, resolution,

More information

Background image generation by preserving lighting condition of outdoor scenes

Background image generation by preserving lighting condition of outdoor scenes Availale online at www.sciencedirect.com Procedia Social and Behavioral Sciences 2 (2010) 129 136 Security Camera Network, Privacy Protection and Community Safety Background image generation y preserving

More information

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS

WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS Nguyen Dinh Duong Department of Environmental Information Study and Analysis, Institute of Geography, 18 Hoang Quoc Viet Rd.,

More information

Vehicle lateral dynamics control

Vehicle lateral dynamics control Applied and Computational Mechanics 1 (7) 11-1 Vehicle lateral dynamics control V. Drobný a, *, M. Valášek b a TÜV SÜD Auto CZ s.r.o., Novodvorská 99/13, 1 1 Praha, Czech Republic b Department o Mechanics,

More information

PIXEL-LEVEL IMAGE FUSION USING BROVEY TRANSFORME AND WAVELET TRANSFORM

PIXEL-LEVEL IMAGE FUSION USING BROVEY TRANSFORME AND WAVELET TRANSFORM PIXEL-LEVEL IMAGE FUSION USING BROVEY TRANSFORME AND WAVELET TRANSFORM Rohan Ashok Mandhare 1, Pragati Upadhyay 2,Sudha Gupta 3 ME Student, K.J.SOMIYA College of Engineering, Vidyavihar, Mumbai, Maharashtra,

More information

Spectral Response for DigitalGlobe Earth Imaging Instruments

Spectral Response for DigitalGlobe Earth Imaging Instruments Spectral Response for DigitalGlobe Earth Imaging Instruments IKONOS The IKONOS satellite carries a high resolution panchromatic band covering most of the silicon response and four lower resolution spectral

More information

A Novel Approach for Change Detection in Remote Sensing Image Based on Saliency Map

A Novel Approach for Change Detection in Remote Sensing Image Based on Saliency Map A Novel Approach for Change Detection in Remote Sensing Image Based on Saliency Map Minghui Tian, Shouhong Wan, Lihua Yue Department of Computer Science and Technology, University of Science and Technology

More information

DEVELOPMENT OF A SUPERVISED SOFTWARE TOOL FOR AUTOMATED DETERMINATION OF OPTIMAL SEGMENTATION PARAMETERS FOR ECOGNITION

DEVELOPMENT OF A SUPERVISED SOFTWARE TOOL FOR AUTOMATED DETERMINATION OF OPTIMAL SEGMENTATION PARAMETERS FOR ECOGNITION DEVELOPMENT OF A SUPERVISED SOFTWARE TOOL FOR AUTOMATED DETERMINATION OF OPTIMAL SEGMENTATION PARAMETERS FOR ECOGNITION Y. Zhang* a, T. Maxwell, H. Tong, V. Dey a University of New Brunswick, Geodesy &

More information

PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS LANDSAT 7 IMAGES AND INITIAL SOLUTIONS

PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS LANDSAT 7 IMAGES AND INITIAL SOLUTIONS ISPRS SIPT IGU UCI CIG ACSG Table of contents Table des matières Authors index Index des auteurs Search Recherches Exit Sortir PROBLEMS IN THE FUSION OF COMMERCIAL HIGH-RESOLUTION SATELLITE AS WELL AS

More information

Measuring the 2D Vector Aspect of Momentum Using Only One Dimension. Andrew Ferstl and Nathan Moore Winona State University, Winona, MN

Measuring the 2D Vector Aspect of Momentum Using Only One Dimension. Andrew Ferstl and Nathan Moore Winona State University, Winona, MN Measuring the D Vector Aspect o Momentum Using Only One Dimension Andrew Ferstl and Nathan Moore Winona State University, Winona, MN Without the use o cameras to record D motion and an appropriate analysis

More information

COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS

COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS COMPARISON OF OBJECT BASED AND PIXEL BASED CLASSIFICATION OF HIGH RESOLUTION SATELLITE IMAGES USING ARTIFICIAL NEURAL NETWORKS B.K. Mohan and S. N. Ladha Centre for Studies in Resources Engineering IIT

More information

3198 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 8, AUGUST 2010 0196-2892/$26.00 2010 IEEE

3198 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 8, AUGUST 2010 0196-2892/$26.00 2010 IEEE 3198 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 48, NO. 8, AUGUST 2010 Rule-Based Classification of a Very High Resolution Image in an Urban Environment Using Multispectral Segmentation Guided

More information

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA

A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA A PHOTOGRAMMETRIC APPRAOCH FOR AUTOMATIC TRAFFIC ASSESSMENT USING CONVENTIONAL CCTV CAMERA N. Zarrinpanjeh a, F. Dadrassjavan b, H. Fattahi c * a Islamic Azad University of Qazvin - nzarrin@qiau.ac.ir

More information

Automatic Traffic Estimation Using Image Processing

Automatic Traffic Estimation Using Image Processing Automatic Traffic Estimation Using Image Processing Pejman Niksaz Science &Research Branch, Azad University of Yazd, Iran Pezhman_1366@yahoo.com Abstract As we know the population of city and number of

More information

AUTOMATIC BUILDING DETECTION BASED ON SUPERVISED CLASSIFICATION USING HIGH RESOLUTION GOOGLE EARTH IMAGES

AUTOMATIC BUILDING DETECTION BASED ON SUPERVISED CLASSIFICATION USING HIGH RESOLUTION GOOGLE EARTH IMAGES AUTOMATIC BUILDING DETECTION BASED ON SUPERVISED CLASSIFICATION USING HIGH RESOLUTION GOOGLE EARTH IMAGES Salar Ghaffarian, Saman Ghaffarian Department of Geomatics Engineering, Hacettepe University, 06800

More information

2D GEOMETRIC SHAPE AND COLOR RECOGNITION USING DIGITAL IMAGE PROCESSING

2D GEOMETRIC SHAPE AND COLOR RECOGNITION USING DIGITAL IMAGE PROCESSING 2D GEOMETRIC SHAPE AND COLOR RECOGNITION USING DIGITAL IMAGE PROCESSING Sanket Rege 1, Rajendra Memane 2, Mihir Phatak 3, Parag Agarwal 4 UG Student, Dept. of E&TC Engineering, PVG s COET, Pune, Maharashtra,

More information

Environmental Remote Sensing GEOG 2021

Environmental Remote Sensing GEOG 2021 Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class

More information

The Utilization of Satellite Images to Identify Tree Endangering Transmission Lines

The Utilization of Satellite Images to Identify Tree Endangering Transmission Lines The Utilization of Satellite Images to Identify Tree Endangering Transmission Lines Y. Kobayashi M. S. Moeller G. G. Karady G. T. Heydt R. G. Olsen Project Tele-Seminar March 18, 2008 3/10/2008 1 Introduction

More information

Manufacturing and Fractional Cell Formation Using the Modified Binary Digit Grouping Algorithm

Manufacturing and Fractional Cell Formation Using the Modified Binary Digit Grouping Algorithm Proceedings o the 2014 International Conerence on Industrial Engineering and Operations Management Bali, Indonesia, January 7 9, 2014 Manuacturing and Fractional Cell Formation Using the Modiied Binary

More information

Handwritten Character Recognition from Bank Cheque

Handwritten Character Recognition from Bank Cheque International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-4, Special Issue-1 E-ISSN: 2347-2693 Handwritten Character Recognition from Bank Cheque Siddhartha Banerjee*

More information

Stochastic Method for De-shadowing and Objects Retrieval from High Resolution Images

Stochastic Method for De-shadowing and Objects Retrieval from High Resolution Images Stochastic Method for De-shadowing and Objects Retrieval from High Resolution Images Mrs.G.Devi *1, Dr.S.Kaliappan 2, Mrs.Malini #3 * Asst.Professor,Department of Civil, Anna University of Technology Tirunelveli

More information

!!! Technical Notes : The One-click Installation & The AXIS Internet Dynamic DNS Service. Table of contents

!!! Technical Notes : The One-click Installation & The AXIS Internet Dynamic DNS Service. Table of contents Technical Notes: One-click Installation & The AXIS Internet Dynamic DNS Service Rev: 1.1. Updated 2004-06-01 1 Table o contents The main objective o the One-click Installation...3 Technical description

More information

Outline. Multitemporal high-resolution image classification

Outline. Multitemporal high-resolution image classification IGARSS-2011 Vancouver, Canada, July 24-29, 29, 2011 Multitemporal Region-Based Classification of High-Resolution Images by Markov Random Fields and Multiscale Segmentation Gabriele Moser Sebastiano B.

More information

OST-Tree: An Access Method for Obfuscating Spatio-Temporal Data in Location Based Services

OST-Tree: An Access Method for Obfuscating Spatio-Temporal Data in Location Based Services OST-Tree: An Access Method or Ouscating Spatio-Temporal Data in Location Based Services Quoc Cuong TO, Tran Khanh DANG * Faculty o Computer Science & Engineering, HCMUT Ho Chi Minh City, Vietnam {qcuong,

More information

Signature Region of Interest using Auto cropping

Signature Region of Interest using Auto cropping ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 1 Signature Region of Interest using Auto cropping Bassam Al-Mahadeen 1, Mokhled S. AlTarawneh 2 and Islam H. AlTarawneh 2 1 Math. And Computer Department,

More information

A FRAMEWORK FOR AUTOMATIC FUNCTION POINT COUNTING

A FRAMEWORK FOR AUTOMATIC FUNCTION POINT COUNTING A FRAMEWORK FOR AUTOMATIC FUNCTION POINT COUNTING FROM SOURCE CODE Vinh T. Ho and Alain Abran Sotware Engineering Management Research Laboratory Université du Québec à Montréal (Canada) vho@lrgl.uqam.ca

More information

AMONG the region-based approaches for segmentation,

AMONG the region-based approaches for segmentation, 40 IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL. 3, NO. 1, JANUARY 2006 Classification of Remote Sensing Images From Urban Areas Using a Fuzzy Possibilistic Model Jocelyn Chanussot, Senior Member, IEEE,

More information

RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY

RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY M. Erdogan, H.H. Maras, A. Yilmaz, Ö.T. Özerbil General Command of Mapping 06100 Dikimevi, Ankara, TURKEY - (mustafa.erdogan;

More information

A MPCP-Based Centralized Rate Control Method for Mobile Stations in FiWi Access Networks

A MPCP-Based Centralized Rate Control Method for Mobile Stations in FiWi Access Networks A MPCP-Based Centralized Rate Control Method or Mobile Stations in FiWi Access Networks 215 IEEE. Personal use o this material is permitted. Permission rom IEEE must be obtained or all other uses, in any

More information

An Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network

An Energy-Based Vehicle Tracking System using Principal Component Analysis and Unsupervised ART Network Proceedings of the 8th WSEAS Int. Conf. on ARTIFICIAL INTELLIGENCE, KNOWLEDGE ENGINEERING & DATA BASES (AIKED '9) ISSN: 179-519 435 ISBN: 978-96-474-51-2 An Energy-Based Vehicle Tracking System using Principal

More information

Pixel-based and object-oriented change detection analysis using high-resolution imagery

Pixel-based and object-oriented change detection analysis using high-resolution imagery Pixel-based and object-oriented change detection analysis using high-resolution imagery Institute for Mine-Surveying and Geodesy TU Bergakademie Freiberg D-09599 Freiberg, Germany imgard.niemeyer@tu-freiberg.de

More information

Vision based Vehicle Tracking using a high angle camera

Vision based Vehicle Tracking using a high angle camera Vision based Vehicle Tracking using a high angle camera Raúl Ignacio Ramos García Dule Shu gramos@clemson.edu dshu@clemson.edu Abstract A vehicle tracking and grouping algorithm is presented in this work

More information

A System for Capturing High Resolution Images

A System for Capturing High Resolution Images A System for Capturing High Resolution Images G.Voyatzis, G.Angelopoulos, A.Bors and I.Pitas Department of Informatics University of Thessaloniki BOX 451, 54006 Thessaloniki GREECE e-mail: pitas@zeus.csd.auth.gr

More information

Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors

Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors Speed Detection for Moving Objects from Digital Aerial Camera and QuickBird Sensors September 2007 Fumio Yamazaki 1, Wen Liu 1, T. Thuy Vu 2 1. Graduate School of Engineering, Chiba University, Japan.

More information

'Developments and benefits of hydrographic surveying using multispectral imagery in the coastal zone

'Developments and benefits of hydrographic surveying using multispectral imagery in the coastal zone Abstract With the recent launch of enhanced high-resolution commercial satellites, available imagery has improved from four-bands to eight-band multispectral. Simultaneously developments in remote sensing

More information

Object-Oriented Approach of Information Extraction from High Resolution Satellite Imagery

Object-Oriented Approach of Information Extraction from High Resolution Satellite Imagery IOSR Journal of Computer Engineering (IOSR-JCE) e-issn: 2278-0661,p-ISSN: 2278-8727, Volume 17, Issue 3, Ver. IV (May Jun. 2015), PP 47-52 www.iosrjournals.org Object-Oriented Approach of Information Extraction

More information

Satellite Orbits. Satellites & Orbits. Satellite Orbits. Geo-stationary Orbit. Satellite Orbits. Prof. D. Nagesh Kumar.

Satellite Orbits. Satellites & Orbits. Satellite Orbits. Geo-stationary Orbit. Satellite Orbits. Prof. D. Nagesh Kumar. Satellites & Orbits Prof. D. Nagesh Kumar Dept. of Civil Engg. IISc, Bangalore 560 012, India URL: http://www.civil.iisc.ernet.in/~nagesh Orbit will be elliptical or near circular Time taken by a satellite

More information

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches

Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches Modelling, Extraction and Description of Intrinsic Cues of High Resolution Satellite Images: Independent Component Analysis based approaches PhD Thesis by Payam Birjandi Director: Prof. Mihai Datcu Problematic

More information

Reducing multiclass to binary by coupling probability estimates

Reducing multiclass to binary by coupling probability estimates Reducing multiclass to inary y coupling proaility estimates Bianca Zadrozny Department of Computer Science and Engineering University of California, San Diego La Jolla, CA 92093-0114 zadrozny@cs.ucsd.edu

More information

Automatic Change Detection in Very High Resolution Images with Pulse-Coupled Neural Networks

Automatic Change Detection in Very High Resolution Images with Pulse-Coupled Neural Networks 1 Automatic Change Detection in Very High Resolution Images with Pulse-Coupled Neural Networks Fabio Pacifici, Student Member, IEEE, and Fabio Del Frate, Member, IEEE Abstract A novel approach based on

More information

An Assessment of the Effectiveness of Segmentation Methods on Classification Performance

An Assessment of the Effectiveness of Segmentation Methods on Classification Performance An Assessment of the Effectiveness of Segmentation Methods on Classification Performance Merve Yildiz 1, Taskin Kavzoglu 2, Ismail Colkesen 3, Emrehan K. Sahin Gebze Institute of Technology, Department

More information

( ) 2 = 9x 2 +12x + 4 or 8x 2 " y 2 +12x + 4 = 0; (b) Solution: (a) x 2 + y 2 = 3x + 2 " $ x 2 + y 2 = 1 2

( ) 2 = 9x 2 +12x + 4 or 8x 2  y 2 +12x + 4 = 0; (b) Solution: (a) x 2 + y 2 = 3x + 2  $ x 2 + y 2 = 1 2 Conic Sections (Conics) Conic sections are the curves formed when a plane intersects the surface of a right cylindrical doule cone. An example of a doule cone is the 3-dimensional graph of the equation

More information

AN AMBIENT AIR QUALITY MONITORING NETWORK FOR BUENOS AIRES CITY

AN AMBIENT AIR QUALITY MONITORING NETWORK FOR BUENOS AIRES CITY AN AMBIENT AIR QUALITY MONITORING NETWORK FOR BUENOS AIRES CITY Laura Venegas and Nicolás Mazzeo National Scientific and Technological Research Council (CONICET) Department of Atmospheric and Oceanic Sciences.

More information

Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery

Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery WorldView-2 is the first commercial high-resolution satellite to provide eight spectral sensors in the visible to near-infrared

More information

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value

Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode Value IJSTE - International Journal of Science Technology & Engineering Volume 1 Issue 10 April 2015 ISSN (online): 2349-784X Image Estimation Algorithm for Out of Focus and Blur Images to Retrieve the Barcode

More information

Neural Network based Vehicle Classification for Intelligent Traffic Control

Neural Network based Vehicle Classification for Intelligent Traffic Control Neural Network based Vehicle Classification for Intelligent Traffic Control Saeid Fazli 1, Shahram Mohammadi 2, Morteza Rahmani 3 1,2,3 Electrical Engineering Department, Zanjan University, Zanjan, IRAN

More information

The Design and Implementation of a Solar Tracking Generating Power System

The Design and Implementation of a Solar Tracking Generating Power System The Design and Implementation o a Solar Tracking Generating Power System Y. J. Huang, Member, IAENG, T. C. Kuo, Member, IAENG, C. Y. Chen, C. H. Chang, P. C. Wu, and T. H. Wu Abstract A solar tracking

More information

CHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression

CHAPTER 13 SIMPLE LINEAR REGRESSION. Opening Example. Simple Regression. Linear Regression Opening Example CHAPTER 13 SIMPLE LINEAR REGREION SIMPLE LINEAR REGREION! Simple Regression! Linear Regression Simple Regression Definition A regression model is a mathematical equation that descries the

More information

Analecta Vol. 8, No. 2 ISSN 2064-7964

Analecta Vol. 8, No. 2 ISSN 2064-7964 EXPERIMENTAL APPLICATIONS OF ARTIFICIAL NEURAL NETWORKS IN ENGINEERING PROCESSING SYSTEM S. Dadvandipour Institute of Information Engineering, University of Miskolc, Egyetemváros, 3515, Miskolc, Hungary,

More information

INTRODUCTION REMOTE SENSING

INTRODUCTION REMOTE SENSING INTRODUCTION REMOTE SENSING dr.ir. Jan Clevers Centre for Geo-Information Dept. Environmental Sciences Wageningen UR Remote Sensing --> RS Sensor at a distance EARTH OBSERVATION EM energy Earth RS is a

More information

Classification of High-Resolution Remotely Sensed Image by Combining Spectral, Structural and Semantic Features Using SVM Approach

Classification of High-Resolution Remotely Sensed Image by Combining Spectral, Structural and Semantic Features Using SVM Approach Classification of High-Resolution Remotely Sensed Image by Combining Spectral, Structural and Semantic Features Using SVM Approach I Saranya.K, II Poomani@Punitha.M I PG Student, II Assistant Professor

More information

Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule

Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule Research On The Classification Of High Resolution Image Based On Object-oriented And Class Rule Li Chaokui a,b, Fang Wen a,b, Dong Xiaojiao a,b a National-Local Joint Engineering Laboratory of Geo-Spatial

More information

Tracking Moving Objects In Video Sequences Yiwei Wang, Robert E. Van Dyck, and John F. Doherty Department of Electrical Engineering The Pennsylvania State University University Park, PA16802 Abstract{Object

More information

Automatic Extraction of Signatures from Bank Cheques and other Documents

Automatic Extraction of Signatures from Bank Cheques and other Documents Automatic Extraction of Signatures from Bank Cheques and other Documents Vamsi Krishna Madasu *, Mohd. Hafizuddin Mohd. Yusof, M. Hanmandlu ß, Kurt Kubik * *Intelligent Real-Time Imaging and Sensing group,

More information

Extraction of Satellite Image using Particle Swarm Optimization

Extraction of Satellite Image using Particle Swarm Optimization Extraction of Satellite Image using Particle Swarm Optimization Er.Harish Kundra Assistant Professor & Head Rayat Institute of Engineering & IT, Railmajra, Punjab,India. Dr. V.K.Panchal Director, DTRL,DRDO,

More information

Automatic Labeling of Lane Markings for Autonomous Vehicles

Automatic Labeling of Lane Markings for Autonomous Vehicles Automatic Labeling of Lane Markings for Autonomous Vehicles Jeffrey Kiske Stanford University 450 Serra Mall, Stanford, CA 94305 jkiske@stanford.edu 1. Introduction As autonomous vehicles become more popular,

More information

Prediction of Population in Urban Areas by Using High Resolution Satellite Images

Prediction of Population in Urban Areas by Using High Resolution Satellite Images Prediction of Population in Urban Areas by Using High Resolution Satellite Images Erener, A. Geodesy and Photogrammetry Engineering SU Konya, Turkey erener@metu.edu.tr Düzgün, HSB. Geodetic and Geographic

More information

Map World Forum Hyderabad, India Introduction: High and very high resolution space images: GIS Development

Map World Forum Hyderabad, India Introduction: High and very high resolution space images: GIS Development Very high resolution satellite images - competition to aerial images Dr. Karsten Jacobsen Leibniz University Hannover, Germany jacobsen@ipi.uni-hannover.de Introduction: Very high resolution images taken

More information

SEMANTIC LABELLING OF URBAN POINT CLOUD DATA

SEMANTIC LABELLING OF URBAN POINT CLOUD DATA SEMANTIC LABELLING OF URBAN POINT CLOUD DATA A.M.Ramiya a, Rama Rao Nidamanuri a, R Krishnan a Dept. of Earth and Space Science, Indian Institute of Space Science and Technology, Thiruvananthapuram,Kerala

More information

Suggestion of a New Brain Reaction Index for the EEG Signal Identification and Analysis

Suggestion of a New Brain Reaction Index for the EEG Signal Identification and Analysis , pp.123-132 http://dx.doi.org/10.14257/ijbsbt.2014.6.4.12 Suggestion o a ew Brain Reaction Index or the EEG Signal Identiication and Analysis Jungeun Lim 1, Bohyeok Seo 2 and Soonyong Chun * 1 School

More information

8. Hardware Acceleration and Coprocessing

8. Hardware Acceleration and Coprocessing July 2011 ED51006-1.2 8. Hardware Acceleration and ED51006-1.2 This chapter discusses how you can use hardware accelerators and coprocessing to create more eicient, higher throughput designs in OPC Builder.

More information

Static Environment Recognition Using Omni-camera from a Moving Vehicle

Static Environment Recognition Using Omni-camera from a Moving Vehicle Static Environment Recognition Using Omni-camera from a Moving Vehicle Teruko Yata, Chuck Thorpe Frank Dellaert The Robotics Institute Carnegie Mellon University Pittsburgh, PA 15213 USA College of Computing

More information

Using quantum computing to realize the Fourier Transform in computer vision applications

Using quantum computing to realize the Fourier Transform in computer vision applications Using quantum computing to realize the Fourier Transorm in computer vision applications Renato O. Violin and José H. Saito Computing Department Federal University o São Carlos {renato_violin, saito }@dc.uscar.br

More information

Digital image processing

Digital image processing 746A27 Remote Sensing and GIS Lecture 4 Digital image processing Chandan Roy Guest Lecturer Department of Computer and Information Science Linköping University Digital Image Processing Most of the common

More information

Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras

Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras Locating and Decoding EAN-13 Barcodes from Images Captured by Digital Cameras W3A.5 Douglas Chai and Florian Hock Visual Information Processing Research Group School of Engineering and Mathematics Edith

More information

Proceedings - AutoCarto Columbus, Ohio, USA - September 16-18, Rajinder S. Nagi

Proceedings - AutoCarto Columbus, Ohio, USA - September 16-18, Rajinder S. Nagi Maintaining detail and color definition when integrating color and grayscale rasters using No Alteration of Grayscale or Intensity (NAGI) fusion method Rajinder S. Nagi ABSTRACT: Integrating color raster

More information

COASTLINE AUTOMATED DETECTION AND MULTI- RESOLUTION EVALUATION USING SATELLITE IMAGES

COASTLINE AUTOMATED DETECTION AND MULTI- RESOLUTION EVALUATION USING SATELLITE IMAGES COASTLINE AUTOMATED DETECTION AND MULTI- RESOLUTION EVALUATION USING SATELLITE IMAGES Ruiz, L.A., Pardo, J.E., Almonacid, J., Rodríguez, B. Department of Cartographic Engineering, Geodesy and Photogrammetry

More information

SAMPLE MIDTERM QUESTIONS

SAMPLE MIDTERM QUESTIONS Geography 309 Sample MidTerm Questions Page 1 SAMPLE MIDTERM QUESTIONS Textbook Questions Chapter 1 Questions 4, 5, 6, Chapter 2 Questions 4, 7, 10 Chapter 4 Questions 8, 9 Chapter 10 Questions 1, 4, 7

More information

RESEARCH ON THE BUILDING SHADOW EXTRACTION AND ELIMINATION METHOD

RESEARCH ON THE BUILDING SHADOW EXTRACTION AND ELIMINATION METHOD RESEARCH ON THE BUILDING SHADOW EXTRACTION AND ELIMINATION METHOD GUO Hai-tao*, Zhang Yan, Lu Jun, Jin Guo-wang Zhengzhou Institute of Surveying and Mapping,66 Mid-Longhai Road, Zhengzhou, China, 450052

More information

The Design and Implementation of Traffic Accident Identification System Based on Video

The Design and Implementation of Traffic Accident Identification System Based on Video 3rd International Conference on Multimedia Technology(ICMT 2013) The Design and Implementation of Traffic Accident Identification System Based on Video Chenwei Xiang 1, Tuo Wang 2 Abstract: With the rapid

More information

SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007

SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007 SPOT Satellite Earth Observation System Presentation to the JACIE Civil Commercial Imagery Evaluation Workshop March 2007 Topics Presented Quick summary of system characteristics Formosat-2 Satellite Archive

More information

Andrea Bondì, Irene D Urso, Matteo Ombrelli e Paolo Telaroli (Thetis S.p.A.) Luisa Sterponi e Cesar Urrutia (Spacedat S.r.l.) Water Calesso (Marco

Andrea Bondì, Irene D Urso, Matteo Ombrelli e Paolo Telaroli (Thetis S.p.A.) Luisa Sterponi e Cesar Urrutia (Spacedat S.r.l.) Water Calesso (Marco Generation of a digital elevation model of the Wadi Lebda basin, Leptis Magna - Lybia Andrea Bondì, Irene D Urso, Matteo Ombrelli e Paolo Telaroli (Thetis S.p.A.) Luisa Sterponi e Cesar Urrutia (Spacedat

More information

Leaf recognition for plant classification using GLCM and PCA methods

Leaf recognition for plant classification using GLCM and PCA methods Oriental Journal of Computer Science & Technology Vol. 3(1), 31-36 (2010) Leaf recognition for plant classification using GLCM and PCA methods ABDOLVAHAB EHSANIRAD and SHARATH KUMAR Y. H. Department of

More information

Laser Gesture Recognition for Human Machine Interaction

Laser Gesture Recognition for Human Machine Interaction International Journal of Computer Sciences and Engineering Open Access Research Paper Volume-04, Issue-04 E-ISSN: 2347-2693 Laser Gesture Recognition for Human Machine Interaction Umang Keniya 1*, Sarthak

More information

PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY

PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY PHOTOGRAMMETRIC TECHNIQUES FOR MEASUREMENTS IN WOODWORKING INDUSTRY V. Knyaz a, *, Yu. Visilter, S. Zheltov a State Research Institute for Aviation System (GosNIIAS), 7, Victorenko str., Moscow, Russia

More information

Estimating Spatial Measures of Roadway Network Usage from Remotely Sensed Data

Estimating Spatial Measures of Roadway Network Usage from Remotely Sensed Data Estimating Spatial Measures of Roadway Network Usage from Remotely Sensed Data Benjamin Coifman Assistant Professor, -Department of Civil and Environmental Engineering and Geodetic Science -Department

More information

A DA Serial Multiplier Technique based on 32- Tap FIR Filter for Audio Application

A DA Serial Multiplier Technique based on 32- Tap FIR Filter for Audio Application A DA Serial Multiplier Technique ased on 32- Tap FIR Filter for Audio Application K Balraj 1, Ashish Raman 2, Dinesh Chand Gupta 3 Department of ECE Department of ECE Department of ECE Dr. B.R. Amedkar

More information

Face detection is a process of localizing and extracting the face region from the

Face detection is a process of localizing and extracting the face region from the Chapter 4 FACE NORMALIZATION 4.1 INTRODUCTION Face detection is a process of localizing and extracting the face region from the background. The detected face varies in rotation, brightness, size, etc.

More information

15 Polygons. 15.1 Angle Facts. Example 1. Solution. Example 2. Solution

15 Polygons. 15.1 Angle Facts. Example 1. Solution. Example 2. Solution 15 Polygons MEP Y8 Practice Book B 15.1 Angle Facts In this section we revise some asic work with angles, and egin y using the three rules listed elow: The angles at a point add up to 360, e.g. a c a +

More information

Image Formation in Man and Machines. Image formation - 1

Image Formation in Man and Machines. Image formation - 1 Image Formation in Man and Machines Image ormation - 1 Overview v Pinhole camera v Reraction o light v Thin-lens equation v Optical power and accommodation v Image irradiance and scene radiance v Human

More information

High Resolution Digital Surface Models and Orthoimages for Telecom Network Planning

High Resolution Digital Surface Models and Orthoimages for Telecom Network Planning Renouard, Lehmann 241 High Resolution Digital Surface Models and Orthoimages for Telecom Network Planning LAURENT RENOUARD, S ophia Antipolis FRANK LEHMANN, Berlin ABSTRACT DLR of Germany and ISTAR of

More information

REAL TIME 3D FUSION OF IMAGERY AND MOBILE LIDAR INTRODUCTION

REAL TIME 3D FUSION OF IMAGERY AND MOBILE LIDAR INTRODUCTION REAL TIME 3D FUSION OF IMAGERY AND MOBILE LIDAR Paul Mrstik, Vice President Technology Kresimir Kusevic, R&D Engineer Terrapoint Inc. 140-1 Antares Dr. Ottawa, Ontario K2E 8C4 Canada paul.mrstik@terrapoint.com

More information

Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People

Learning Spatio-Temporal Topology of a Multi-Camera Network by Tracking Multiple People Learning Spatio-Temporal Topology o a Multi-Camera Network by Tracking Multiple People Yunyoung Nam, Junghun Ryu, Yoo-Joo Choi, and We-Duke Cho Abstract This paper presents a novel approach or representing

More information

Street Detection with Asymmetric Haar Features

Street Detection with Asymmetric Haar Features Street Detection with Asymmetric Haar Features Geovany A. Ramirez and Olac Fuentes Computer Science Department, University of Texas at El Paso garamirez@miners.utep.edu, ofuentes@utep.edu Abstract. We

More information

ECE 533 Project Report Ashish Dhawan Aditi R. Ganesan

ECE 533 Project Report Ashish Dhawan Aditi R. Ganesan Handwritten Signature Verification ECE 533 Project Report by Ashish Dhawan Aditi R. Ganesan Contents 1. Abstract 3. 2. Introduction 4. 3. Approach 6. 4. Pre-processing 8. 5. Feature Extraction 9. 6. Verification

More information

How Landsat Images are Made

How Landsat Images are Made How Landsat Images are Made Presentation by: NASA s Landsat Education and Public Outreach team June 2006 1 More than just a pretty picture Landsat makes pretty weird looking maps, and it isn t always easy

More information

Ring grave detection in high resolution satellite images of agricultural land

Ring grave detection in high resolution satellite images of agricultural land Ring grave detection in high resolution satellite images of agricultural land Siri Øyen Larsen, Øivind Due Trier, Ragnar Bang Huseby, and Rune Solberg, Norwegian Computing Center Collaborators: The Norwegian

More information

Detection of shaded roads in high spatial resolution images of urban areas. Thiago Statella 1. Erivaldo Antônio da Silva 2

Detection of shaded roads in high spatial resolution images of urban areas. Thiago Statella 1. Erivaldo Antônio da Silva 2 Detection of shaded roads in high spatial resolution images of urban areas Thiago Statella 1 Federal Institute for Education, Science and Technology of Mato Grosso, Zulmira Canavarros street n 95, Cuiabá,

More information

The Scientific Data Mining Process

The Scientific Data Mining Process Chapter 4 The Scientific Data Mining Process When I use a word, Humpty Dumpty said, in rather a scornful tone, it means just what I choose it to mean neither more nor less. Lewis Carroll [87, p. 214] In

More information

EDGE-PRESERVING SMOOTHING OF HIGH-RESOLUTION IMAGES WITH A PARTIAL MULTIFRACTAL RECONSTRUCTION SCHEME

EDGE-PRESERVING SMOOTHING OF HIGH-RESOLUTION IMAGES WITH A PARTIAL MULTIFRACTAL RECONSTRUCTION SCHEME EDGE-PRESERVING SMOOTHING OF HIGH-RESOLUTION IMAGES WITH A PARTIAL MULTIFRACTAL RECONSTRUCTION SCHEME Jacopo Grazzini, Antonio Turiel and Hussein Yahia Air Project Departament de Física Fondamental INRIA

More information

Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems

Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems Algorithms 2013, 6, 762-781; doi:10.3390/a6040762 Article OPEN ACCESS algorithms ISSN 1999-4893 www.mdpi.com/journal/algorithms Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based

More information

Damage Detection from Landsat-7 Satellite Images for the 2001 Gujarat, India Earthquake

Damage Detection from Landsat-7 Satellite Images for the 2001 Gujarat, India Earthquake Damage Detection from Landsat-7 Satellite Images for the 2001 Gujarat, India Earthquake Yalkun YUSUF 1), Masashi MATSUOKA 1), Fumio YAMAZAKI 1) Earthquake Disaster Mitigation Research Center National Research

More information