Feasibility of using multi-band imageries captured by Cropcam Unmanned. Aerial Vehicle autopilot for land cover mapping

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1 Dec. 2009, Volume 3, No.12 (Serial No.25) Journal of Materials Science and Engineering, ISSN , USA Feasibility of using multi-band imageries captured by Cropcam Unmanned Aerial Vehicle autopilot for land cover mapping Lim Hwee-San, Mohd Zubir Mat Jafri, Khiruddin Abdullah, Faez Hassan, Nasirun Mohd Saleh (School of Physics, University Science Malaysia, Penang 11800, Malaysia) Abstract: The potential of using a CropCam Unmanned Aerial Vehicle (UAV) autopilot for acquiring digital data for remote sensing application were investigated. The aim was to indicate the feasibility of high spatial resolution imageries taken from a low altitude airborne platform for land cover mapping. A Pentax digital camera, model Optio A40, was used to capture images from an elevation of 1050 feet on board the CropCam UAV autopilot. The technique overcomes the problem of the difficulty in obtaining cloud-free scenes in the Equatorial region from a satellite platform. Supervised classification technique (Maximum Likelihood, ML, Minimum Distance-to-Mean, MDM, and Parallelepiped, P) was applied to the digital camera spectral bands (red, green and blue) to extract the thematic information from the acquired scenes. The accuracy of each classification map produced was validated using the reference data sets consisting of a large number of samples collected per category. The results produced high degree of accuracy. The study revealed that the ML classifier produced better result. The use of a digital camera as a sensor on board the CropCam UAV autopilot as platform in remote sensing studies to acquire useful information for planning and development of a small area of coverage. Key words: UAV; digital camera; land cover; supervised classification 1. Introduction Remote sensing can be used for several purposes. In the past few years, there has been a growing interest in the use of remote-sensing systems for regular monitoring of the earth s surface [1]. The increasing availability of remote-sensing images, acquired periodically by satellite sensors on the same geographical area, make it extremely interesting to Corresponding author: Lim Hwee-San (1977- ), Ph.D; research field: environmental remote sensing. develop the monitoring systems capable of automatically producing and regularly updating land-cover maps of the considered site [2]. Airborne remote sensing was selected in this present study because of several reasons. First was that the airborne images can provides higher spatial resolution for mapping a small study area. Second was that the airborne data acquisition can be carried out according to our planned surveys. It s not like the satellite data was fixed on time of satellite overpass the study area only. Third, for airborne remote sensing, atmospheric correction no need to apply to the analysis data because atmospheric correction only increased R 2 value and RMS significantly for turbidity, this was an advantage since one step of the retrieval process can be eliminated [3]. Land cover is a fundamental variable that impacts on and links many parts of the human and physical environments [4]. The production of the thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing [4]. Remote sensing can be used for several purposes. High-resolution imagery in the form of aerial photography has been available for many years. Our objective was to evaluate high-resolution digital camera imagery in a variety of applications involving land use and land cover mapping. Our study areas were located in the equatorial region where the sky is often covered by clouds. Therefore satellite remote sensing, especially for sensing in the visible and infrared regions is often impossible. Our objective was to develop a protocol to 26

2 overcome such problems by using an airborne technique. The advantages of an airborne technique include (1) flights can be scheduled for the desired time and location, and (2) the aircraft can be flown below cloud cover and acquire high spatial resolution images. However airborne remote sensing techniques are not widely used in environmental applications as they are associated with high operational costs. In this study, we reduced the costs by using a conventional digital camera and hiring a light aircraft. The sensor used in this study was a normal digital camera model Pentax Optio A40, and a light Unmanned Aerial Vehicle (UAV) autopilot was used as a platform to capture the digital images. Supervised classification of remote-sensing images has been widely used as a powerful means to extract various kinds of information concerning earth environment. The objective of this study is to investigate the potential of using a Cropcam Unmanned Aerial Vehicle (UAV) autopilot for acquiring digital data for remote sensing application especially for land cover mapping. The traditional method of collecting data for planning is surveying samples at field. Remote sensing technique is a useful method for classifying the image. With process data available, a quick decision about the area can made. The objective for present study was to investigate the feasibility of high spatial resolution imageries taken by using a Pentax Optio A40 digital camera for land cover mapping from a light Unmanned Aerial Vehicle (UAV) autopilot. Maximum Likelihood classifier was found to produce the best accuracy in this study. Many researchers choose the Maximum Likelihood method in their studies [5-9]. The monitoring task can be accomplished by supervised classification techniques, which have been proven to be effective categorization tools [2]. The accuracy assessment of the classified images also has been done in this study. 2. Study area and data acquisition The flying field site over Penang Island, Malaysia was chosen as the study area. The study area is located between latitude 5º 39 N to 5º 41 N and longitude 100 o 20 E to 100 o 24 E (Fig. 1). The study area was located at the north of Peninsular Malaysia. A digital camera, Pentax Optio A40 (Fig. 2) was used to capture digital images from a UAV (Fig. 3) at 2000 ft of altitude. The digital images were captured during the flight between 5 p.m. to 7 p.m. on 9 January Fig. 2 Digital Camera- Pentax optio A40. (Source: [10]) Fig. 3 CropCam Unmanned Aerial Vehicle (UAV) autopilot. (Source: [11]) 3. Remote platform and sensor In this study, a Cropcam Unmanned Aerial Vehicle (UAV) autopilot was flown above the study areas at an average altitude of 2000 feet above sea level during image acquisitions. The technical specification for the UAV is shown in Table 1. The CropCam is a revolutionary mini agriculture plane that will change the way you manage your crops, fields or any part of your agricultural operation, by providing high resolution GPS based digital images for precision 27

3 agriculture. Hand launched, the CropCam includes a miniature autopilot, camera, GPS and software to provide images on demand. The CropCam is a radio controlled model glider plane equipped with a Trimble GPS, a miniature autopilot and digital camera. In this study, a PENTAX Optio A40 was used as a sensor to capture the remote sensed digital imagery. The technical specification for the PENTAX Optio A40 is shown in Table 2. Table 1 The technical specification for the CropCam Length 4 feet Wing span 8 feet Weight 6 pounds Engine 0.15 cu in / Axi Brushless Fuel tank 6 oz / Lithium Polymer Batteries Altitude feet in Canada(Can be adjusted to meet regulation or application) Flight duration 20 minutes Camera Pentax Digital Optio s5i, s6, A10 (Source: Ref. [11]). Table 2 The technical specification for the Pentax Digital Optio A40 lens / zoom 37 mm -111 mm (3x) (dig 6x) focus / macro auto manual/35cm/6cm metering mode multi-segment, center-weighted, spot aperture auto/f2.8 - F5.4 white balance auto, 4 presets, manual shutter Auto/4s - 1/2000 s exposure +/- 2EV in 1/3EV steps flash / mode internal - 5 modes / 6 cm-7.1 m viewfinder lcd screen iso rating 50, 100, 200, 400, 800, 1600, 3200i / / image size / / / / image format jpeg (exif2.2) image compression lag/cycle times Good/better/best remote control self-timer 2 s, 10 s video options mpeg-4, with sound , (30 fps) audio options wav mono connectivity usb 2.0 (high speed), A/V (NTSC/PAL) storage platform 22MB internal, SD, SDHC extra features optical image stabilization Pict Bridge compliant (Source:Ref. [21]). Digital cameras have been used in many researchers in remote sensing application [12-18]. A Canon Camera EOS 1D Mark II 8 MPixel digital camera was used for traffic monitoring [19]. Kodak DCS460c was used for vegetation analysis and small area mapping respectively [17-18]. A Nikon Coolpix 885, CP885 was used for water quality measurements in Galway Bay [20]. Hand launched and automatic from take off to landing, the CropCam provides high resolution GPS based images on demand. Simply stand at one corner of the field and hand launch the 6 pound CropCam plane. The powerful miniature autopilot and Trimble GPS, does the rest navigating in a pattern over the field. Both the CropCam and the camera perform automatically to take GPS based digital imagery. Each individual image is GPS based with latitude, longitude and altitude. 4. Methodology All image-processing analysis were carried out using PCI Geomatica 10.1 image processing software at the School of Physics, Universiti Sains Malaysia (USM). Thirty colour digital imageries of the flying site were selected for land cover classification. Sample of the colour imageries were shown in Fig. 4. The multispectral digital imageries were acquired in three visible bands (3-bands: red, green and blue). The size of each raw high spatial resolution colour image was 4000 pixels by 3000 lines. A total of twenty four digital images were then mosaiced together to obtain a bigger coverage area (Fig. 5). The mosaic image was separated into three bands, (red, green and blue bands) for multispectral analysis using PTGui Pro software. PTGui is panoramic stitching software for Windows and Mac OSX. Originally developed as a Graphical User Interface for Panorama Tools (hence the name), PTGui now is a full featured photo stitching application. 28

4 (a) (b) Use PTGui to stitch any number of photos into a panoramic image. Some benefits of PTGui, when compared to other stitching software: PTGui can stitch multiple rows of images Create 360 degree cylindrical panoramas, flat partial panoramas and even spherical degree panoramas No need to keep the camera level: PTGui can stitch rotated and tilted images Virtually unlimited output size: create Gigapixel panoramas from hundreds of images Layered output allows full control over the final stitched result PTGui stitches most panoramas fully automatically, but at the same time provides full manual control over every single parameter. This enables stitching of difficult scenes, where other programs fail Full 16 bit workflow for best image quality (Source:Ref [22]). (c) (d) Fig. 4 Raw images used in this study Fig. 5 The mosaic image used for land cover classification A total of thirty digital imageries scene were captured in absolutely clear skies on January 9 th, The aim of the classification analysis is to categorize all of the pixels in the digital camera imagery into land cover classes. Fig. 6 illustrates the classification analysis flow chart. Basically, the process can be divided into three simple steps, the pre-processing, data classification and output. For the first step of pre-processing, the mosaic digital image was chosen for land cover mapping. The size of the mosaic colour 29

5 digital image used in this study is pixels by 9956 lines. For the second step of data classification, the digital images were processed using PCI Geomatica version 10.1 digital image processing software. Data Some sample training sites were choose Supervised classification was performed to the digital i Accuracy assessment Export classified map Fig. 6 Flow chart for data processing of the images Supervised classifications operate in three basic steps: training, classification and accuracy assessment. Training sites were needed for supervised classification. Selection of training areas in this study was based on the colour image. The areas were established using polygons. They are delineated by spectrally homogeneous sub areas, which have, class name given. They are four classes in this study. Once the training sites and classes were assigned, the images were then classified using the three supervised classification methods (Maximum Likelihood, Minimum Distance-to-Mean, and Parallelepiped). Accuracy assessment was made to the image after the images were classified. Many methods of accuracy assessment have been discussed in remote sensing literatures. Three measures of accuracy were tested in this study, namely overall accuracy, error matrix and Kappa coefficient. The Kappa statistic is a statistical method of assessing accuracy that takes into account the chance of random agreement. 5. Data analysis and results A total of 100 points was randomly generated in this study. The standard supervised classifier was performed to the mosaic image such as the Maximum Likelihood, Minimum Distance-to-mean and Parallelepiped. The mosaic image was classified into four classes which are tree, water, grass/paddy field and open land. Kappa coefficient and overall accuracy results of the three classification methods are shown in Table 3. The overall accuracy is expressed as a percentage of the test-pixels successfully assigned to the correct classes. Table 3 The overall classification accuracy and Kappa coefficient Classification method Overall classification Kappa accuracy (%) coefficient Maximum Likelihood Minimum Distance-to-Mean Parallelepiped Parallelepiped with Maximum Likelihood as tie breaker Fig. 7 The classified image obtained using Maximum Likelihood classifier (Green=Forest, Blue=Water, Orange=Land and red=urban) In this study, Parallelepiped with Maximum Likelihood as tie breaker classifier produced the highest degree of accuracy with overall accuracy of 82.32%, the Maximum Likelihood classifier produced overall accuracy of 72.39%, Minimum Distance-to-Mean classifier gave overall classification accuracy of 65.62%, and Parallelepiped classifier resulted in the overall classification accuracy of 45.36%. A classified image using Maximum Likelihood classifier is shown in Fig

6 6. Conclusions From the classified map, Maximum Likelihood method gives a good result for land cover mapping. This analysis has demonstrated the use of a Pentax camera, model Optio A40, to capture images from an elevation of 2000 feet on board a Cropcam Unmanned Aerial Vehicle (UAV) autopilot in studying the land cover mapping. This study showed that the normal digital camera can give an alternative way to provide useful data for land cover mapping. References: [1] Bruzzone L. and Prieto D. F.. A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images. Pattern Recognition Letters, 2002, 23: [2] Bruzzone L., Cossu R. and Vernazza G.. Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images. Information Fusion, 2002, 3: [3] Koponen S., Pulliainen J., Kallio K., et al. Lake water quality classification with airborne hyperspectral spectrometer and simulated MERIS data. Remote Sensing of Environment, 2002, 79: [4] Foody G. M.. Status of land cover classification accuracy assessment. Remote Sensing and Environment, 2002, 80: [5] Saura S. and Miguel-Ayanz J. S.. Forest cover mapping in Central Spain with IRS-WIFS images and multi-extent textual-contextual measures. International Journal of Remote Sensing, 2002, 23(3): [6] Pal S. R. and Mohanty P. K.. Use of IRS-1B data for change detection in water quality and vegetation of Chika lagoon, east coast of India. International Journal of Remote Sensing, 2002, 23(6): [7] Donoghue D. N. M. and Mironnet N.. Development of an integrated geographical information system prototype for coastal habitat monitoring. Computers and Geosciences, 2002, 28: [8] Thiemann S. and Kaufmann H.. Determination of chlorophyll content and trophic state of lakes using field spectrometer and IRS-1C satellite data in the mecklenburg lake district, Germany. Remote Sensing of Environment, 2000, 73: [9] Guerschman J. P., Paruelo J. M., Di Bella C., et al. Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data. International Journal of Remote Sensing, 2003, 24(17): [10] Cameras. co. uk. Available at specs/pentax-optio-a40.cfm. [11] CropCam Available at brochure-cropcam.pdf. [12] Ellis J. M. and Dodd H.. Using airborne digital cameras for environmental applications. [Available at]. com/pdf-articles/airborne-digital-apps2.pdf. [13] Nakada R. and Chikatsu H.. Generating 3D Model of Meguro Residence using digital armature camera. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2003, 34(5): W10. Available at photogrammetry.ethz.ch/tarasp_workshop/papers/nakada.pdf. [14] Levesque J. and King D. J.. Airborne digital camera image semivariance for evaluation of forest structural damage at an acid mine site. Remote Sensing of Environment, 1999, 68: [15] Heier H. and Hinz A.. A digital airborne camera system for photogrammetry and thematic applications. ipi.uni-hannover.de/html/publikationen/1999/isprs-works hop/cd/pdf-papers/heier.pdf. [Available at] [16] White M. A., Asner G. P., Nemani R. R., et al. Measuring fractional cover and leaf area index in arid ecosystems: digital camera, radiation transmittance, and laser altimetry methods. Remote Sensing of Environment, 2000, 74: [17] Mason S., Ruther H. and Smit J.. Investigation of the Kodak DCS460 digital camera for small-area mapping. ISPRS Journal of Photogrammetry & Remote Sensing, 1997, 52: [18] Dean C., Warner T. A. and Mcgraw J. B.. Suitability of the DCS460c colour digital camera for quantitative remote sensing analysis of vegetation. ISPRS Journal of Photogrammetry and Remote Sensing, 2000, 55: [19] Reinartz P., Lachaise M., Schmeer E., et al. Traffic monitoring with serial images from airborne cameras. ISPRS Journal of Photogrammetry and Remote Sensing, 2006, 61(3-4): [20] Goddijn L. M. and White M.. Using a digital camera for water quality measurements in Galway Bay. Estuarine, Coastal and Shelf Science, 2006, 66(3-4): [21] DCViews. Available at _pentax/ a40.htm [22] PTGui. Available at (Edited by Taylor C. and Donna T.) 31

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