SENSING IMAGES. School of Remote Sensing and Information Engineering, Wuhan University, 129# Luoyu Road, Wuhan,

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1 International Archive of the Photogrammetry, Remote Sening and Spatial Information Science, Volume X-/W, 3 8th International Sympoium on Spatial Data Quality, 3 May - June 3, Hong Kong COUD DETECTION METHOD BASED ON FEATURE EXTRACTION IN REMOTE SENSING IMAGES YU Changhui a, *, Yuan Yuan b,miao Mining b, Zhu Menglu a a School of Remote Sening and Information Engineering, Wuhan Univerity, 9# uoyu Road, Wuhan, China,ych@whu.edu.cn b School of Printing and Packaging, Wuhan Univerity,9# uoyu Road, Wuhan, China yuany@whu.edu.cn KEY WORDS: Remote Sening Image, Cloud Detection, Cloudine, Feature Extraction, Claification, Dicriminant Model ABSTRACT: In remote ening image, the exitence of the cloud ha a great impact on the image quality and ubequent image proceing, a the image covered with cloud contain little ueful information. Therefore, the detection and recognition of cloud i one of the maor problem in the application of remote ening image. Preent there are two categorie of method to cloud detection. One i etting pectrum threhold baed on the characteritic of the cloud to ditinguih them. However, the intability and uncertainty of the practical cloud make thi kind of method complexity and weak adaptability. The other method adopt the feature in the image to identify the cloud. Since there will be ignificant overlap in ome feature of the cloud and ground, the detection reult i highly dependent on the effectivene of the feature. Thi paper preented a cloud detection method baed on feature extraction for remote ening image. At firt, find out effective feature through training pattern, the feature are elected from gray, frequency and texture domain. The different feature in the three domain of the training ample are calculated. Through the reult of tatitical analyi of all the feature, the ueful feature are picked up to form a feature et. In concrete, the et include three feature vector, repectively, the gray feature vector contituted of average gray, variance, firt-order difference, entropy and hitogram, the frequency feature vector contituted of DCT high frequency coefficient and wavelet high frequency coefficient, and the texture feature vector contituted of the hybrid entropy and difference of the gray-gradient co-occurrence matrix and the image fractal dimenion. Secondly, a thumbnail will be obtained by down ampling the original image and it feature of gray, frequency and texture are computed. at but not leat, the cloud region will be udged by the comparion between the actual feature value and the threhold determined by the ample training proce. Experimental reult how that the cloud and ground obect can be eparated efficiently, and our method can implement rapid cloud detection and cloudine calculation. * YU Changhui, 976, aociate profeor of School of Remote Sening and Information Engineering,Wuhan Univerity, maor in remote ening image proce and geographic information cience. 73

2 International Archive of the Photogrammetry, Remote Sening and Spatial Information Science, Volume X-/W, 3 8th International Sympoium on Spatial Data Quality, 3 May - June 3, Hong Kong. INTRODUCTION Cloud cover about 5% of the earth' urface. When acquiring remote ening image, a large amount of cloud are recorded in the image at the ame time. The exitence of cloud in image not only affect the image quality but alo make it difficult to extract accurate geo-patial information from remote ening image. In addition, due to the variou thicknee of cloud in nature it i difficult to extract the boundary between cloudy region and cloudle region and to label certain pixel a cloudy in digital image. Therefore, how to detect and identify the cloud of image ha become the maor difficulty to be olved in the proce of image quality evaluation. Preent there are two categorie of method to cloud detection.the firt category refer to multiple threhold method. The method baed on the radiation characteritic of cloud and normally applied to the image generated by multipectral enor. In image the main difference between cloud and obect are their reflectance and temperature. By comparing the pre-etted threhold of different propertie and the actual value obtained from multichannel, the cloudy region in the image can be detected. However, the intability and uncertainty of the cloud make thi kind of method complexity and ha weak adaptability. Another cla called feature extraction method. the cloud can be detected baed on the principle that cloud and obect ha different patial characteritic and o ha different image feature. But there will be ignificant overlap in ome feature of the cloud and ground obect, So the detection reult i highly dependent on the effectivene of the feature. How to elect the effective feature i key to the dicrimination of cloudy and clear region. Due to the limitation of current imaging equipment, the technology of acquiring multipectral information of remote ening image i not mature enough; conequently, it lack wide applicability for the cloud detection with multiple threhold method, while the method baed on feature extraction i becoming the maor reearch direction. (a)clear image (b)cloudy image Figure. remote ening image. COUD DETECTION METHOD BASED ON FEATURE EXTRACTION IN REMOTE SENSING IMAGES. Cloud Detection Method Baed On Feature Extraction in Remote Sening Image Thi paper preented a cloud detection method baed on feature extraction in remote ening image. The method eparated the cloudy region from the image by extracting multiple dimenional feature parameter to fully decribe the different propertie of the cloudy and clear area. Firtly uing the training image the method extract the feature vector to contruct the claifier baed on the K-mean algorithm. Then the detected image are divided into a lot of ub-image with ame ize. Through the feature vector of ub-image and uing the claifier the ub-image are claified a cloudy image et and clear image et. Finally the method can give the proportion of cloudy in the detected image by compute the percentage of cloudy ub-image to the whole ub-image. The workflow of the method are illutrated in figure. There are two key procedure in the algorithm. One i the election of effective image feature et. Another i the contruction of claifier. g r a y-cale feature Training ample Feature extraction frequency feature Image claifier Claification model for cloudy and clear area texture feature Cloud et Original cloud image Image egmentation Feature Extraction o f u b -im a ge Image claification Cloud percentage Clear et Figure. Workflow of the cloud detection method baed on feature extraction in remote ening image. Feature Extraction The feature parameter et are important to contruct the dicriminant model of cloudy and clear image. Generally peaking, the ideal feature et are uppoed to atify the following principle imultaneouly: () Effectivene: every feature hould reflect the propertie of the target to be recognized and claified to ome extent; () Weak correlation: the correlation among different feature need to be maller in order to reduce the mitake rate of claification; (3) Integrality: the feature et ought to embody all the propertie of the target. Through the obervation of remote ening image of variou type, there are obviou difference between cloudy and cloudle area in their gray-cale feature, frequency feature and texture feature. So baed three feature vector the method make up a feature pace for remote ening image... Gray-cale feature vector The gray information i the elementary tatitic of the gray ditribution in the image. The brightne of the cloudy area i uually larger than that of the clear area. In the meanwhile, there exit ome ditinction in repect of gray ditribution and change. Therefore, it i poible to dicriminate the cloudy image from the clear image roughly. By experimental analyi, the elected gray-cale feature include the following four apect. )Gray ray-cale average Gray-cale average embodie the overall gray level of image. The image with more cloud alway ha a larger gray-cale average. The expreion i a follow: 74

3 International Archive of the Photogrammetry, Remote Sening and Spatial Information Science, Volume X-/W, 3 8th International Sympoium on Spatial Data Quality, 3 May - June 3, Hong Kong where ( i M = N f mean f /( M N ) () = f, = gray value M, N = image ize i, = image coordinate )Gray variance Gray variance i the meaurement of the gray ditribution uniformity for the overall image. The expreion i a follow: ( f f ) ( M N ) M = N f var mean / () = 3)Firt order difference The firt order difference decribe the intenity of gray-cale change, and the expreion i a follow: M N x= y= N M y= x= f ) Dx = f + f ( i, Dy = f + Dx Dy f diff = + M N where Dx, Dy denote the one-dimenional firt difference in x- cale and y-cale 4)Hitogram entropy Hitogram entropy comprehenively reflect the gray ditribution and the order. It expreion repreented a follow: fent where Hit [ g] [ g]( i) log Hit[ g]( i) (3) = Hit (4) i = hitogram of image.. Frequency feature vector )High-frequency coefficient of Fourier tranform The equation of the elected tranformation function i a follow: 7 7 ( i + ) uπ ( i + ) vπ F( u, v) = C( u) C( v) f co co (5) 4 = 6 6 When, v = u, C ( u) C( v) ; Other, ( u) C( v) = = C. )High-frequency coefficient of wavelet tranform The Hal wavelet of Daubechie wavelet i elected in the paper to compute the high-frequency coefficient of wavelet tranform and the expreion i:, X <.5 ϕ ( X ) =,.5 X < (6), other The correponding caling function i: ϕk, K K + X < + K + K + + other ( X ) = ϕ( X K) =, X <, k =,,,...,,..3 Texture feature vector Baed on the ubective udgment of the human viual ytem, the geometrical texture of cloud in the remote ening image are imple than thoe of the obect. Moreover, the edge of the cloudy area tend to be fuzzy, mooth and change lowly, and the edge of the obect are often harp and have large gradient. A a reult, the texture information and gradient information of image can be taken into conideration for the cloud detection. The texture information of image i obtained by gray level cooccurrence matrix, which extract the texture feature with conditional probability and reflect the amplitude information for the direction, interval and changing magnitude in the image. The gradient information of image ued to inpect the the part where the gray value ump, uch a the edge and the groove. With the integrated information of both gray-cale and gradient, the texture feature can be extracted for the ditinction of cloud from urface. In the paper, the quadratic tatitic of gray level-gradient cooccurrence matrix are choen a the texture feature parameter of image, which contain gradient mean quare error, hybrid entropy and homogeneity. Beide, the property of fractal dimenion i applied to upplementary decribe the irregularitie of the remote ening image. The firt tep i to get the gray level co-occurrence matrix H from the image, and perform normalization proceing to get the normalization H ˆ i, for the calculation of the quadratic tatitic. matrix ( ) )Gradient mean quare error Gradient mean quare error reflect the entire gray change of image. Generally, the gray change of texture in the area covered by cloud are comparatively uniform and mooth. where (7) g ( ) ˆ T = Tavg H (8) = T avg = average gradient of image g T = ˆ avg H (9) = 75

4 International Archive of the Photogrammetry, Remote Sening and Spatial Information Science, Volume X-/W, 3 8th International Sympoium on Spatial Data Quality, 3 May - June 3, Hong Kong where g = maximum gray cale of image = minimum gray cale of image i, = image coordinate ( i H ˆ, = normalized gray level co-occurrence matrix )Hybrid entropy Hybrid entropy indicate the complexitie or heterogeneitie of texture in the image and expree the amount of information of image. Since cloud have relatively imple texture and uniform ditribution, the hybrid entropie of mot cloudy image are theoretically maller than thoe of clear image. T = g = Hˆ lg Hˆ () 3)Homogeneity Homogeneity refer to the concentration degree of the large value to the main diagonal of a quare matrix, which embodie the homogeneity of variance and local change of texture of image. The math expreion i a follow: g ˆ ( i H T = () = + 4)Fractal dimenion Fractal dimenion i ued to meaure the irregular degree or roughne of image texture. The larger the fractal dimenion i, the rougher the urface of image are. Baed on the fractional Brownian movement model, the fractal dimenion value are calculated. The mathematical decription are a follow: n Suppoe X R, f ( X ) refer to a real random function of X. If there exit a contant H (< H <), which make the following expreion F( t) a ditribution function irrelevant to X and X, uch f ( X ) can be referred to a the fractal Brown function. ( X + X ) f ( X ) f F ( t) = Pt < t X () Where H i called elf-imilar parameter. The expreion of the fractal dimenion of the image i a follow: D = n + H (3).3 Feature claification of remote ening image A mentioned in the above dicuion, the key problem of the cloud detection baed on feature extraction lie in the election of uitable feature and the contruction of claification model baed on the feature pace. The accuracy of cloud detection largely depend on precie of the dicriminant model. After obtaining the feature pace of the remote ening image, a fat and effective method, K-mean unupervied claification method, i adopted to perform training proce and the cla clutering center are determined for each cla. The procedure are decribed a follow: ) Determine the initial feature center Ck for each cluter. Divide the ample of remote ening image into k clae (the ize of k depend on the number of feature elected), and randomly chooe ample a the initial cluter center from each cla, C k = { Ck, Ck,..., Ckl}, where l refer to the number of feature of each cluter center; ) Compute the ditance between each ample xi and the feature center l C, ( ) k d ki = = Cki xi, the ample will be orted into the cla with minimum d ki baed on the minimum ditance recognition criterion; 3) Recalculate the average value for all the ample of each cluter, and et them a the new cluter center C k, Ck = xi, xi Ck, where N i the ample N number belonging to the correponding center; 4) Repeat tep and tep 3, and carry out iterative computation until every cluter center top changing or change little; 5) The final cluter center are the reult of image claification, namely the dicriminant model of cloudy and clear image. In the pecific cloud detection method, k i, which mean all the image are claified into either cloudy cla or clear image cla. The reult of image claification i the two cluter center and their correponding feature value. 3. EXPERIMENTS AND RESUTS The cloud detection method of remote ening image in the paper i compoed of two phae: training tage for cloud ample and detection tage for tet image. In the experiment, 4 training ample obtained by the firt civil high-definition urvey atellite ZY 3 with patial reolution pixel are ued, and 3 ZY 3 atellite image in 8-bit BMP format with patial reolution 4 4 pixel are choen a the tet image. Each tet image i divided into the ub-image with the ize 3 3, thu every tet image i made up with 4 ubimage. Both training ample and tet image include two type of et: cloudy cla and clear cla. The cloudy cla poee image under different cloud condition, uch a thin cloud, cirru cloud, cumulu, etc. The clear cla cover a variety of image with divere geographic character, uch a ocean, mountain, farmland, city and deert. The workflow of the experiment i explained in detail a follow: ) Training ample. Baed on the feature extracted from the training ample, employ K-mean unupervied claification algorithm to get the dicriminant model for cloud detection; ) Segmenting image. Divide the tet image into ub-image and count the number of ub-image a m; 3) Feature exaction. Extract the feature of all the ub-image; 4) Cloud udgment. Ue the dicriminant model to ditinguih the cla the ub-image belong to. If the ub-image i determined a the cloudy image, label it a and et it gray value a, which mean that the correponding region in the tet image turn black. Otherwie, if the ub-image i determined a the clear image, label i a and et it gray value a 55, which mean that the correponding region in the tet image turn white; 76

5 International Archive of the Photogrammetry, Remote Sening and Spatial Information Science, Volume X-/W, 3 8th International Sympoium on Spatial Data Quality, 3 May - June 3, Hong Kong 5) Cloudine calculation. After finihing udging all the ubimage, count the number of ub-image with label a n, and the cloudine of the image i the ratio between n and m, that i cloud=m/n. Meanwhile, the detected image i available to output. We evaluate the cloud detection reult uing people ubective comparion and udgment. For the 3 tet image contain 37 ub-image, the final reult howed that 876 of them were miudged, among which cloudy ub-image were claified a clear and 864 clear ub-image were orted into the cloudy et. The preciion rate of the cloud detection method wa about 9%. There are everal reaon accounting for the mitake udgment. One i that there exit mixing area of feature in the proce of claification, which bring difficultie in the identification of cloud. Another reaon might be that the ize of ub-image i too mall. With inufficient data information, the feature extracted i not ignificant enough to decribe the propertie of image, thu leading to the wrong udgment. However, if the ize of ub-image i too large, ome of them might only contain few cloud which can hardly reflected by the feature parameter and caue the miclaification. ky area effectively. Uing the method the cloud can be fat detected. For many cloud free image which hare the imilar feature with the cloudy image, uch a great deert, image with fog and now, large area of moke, etc. the probability of miudgment for the cloud detection method baed on feature exaction i relatively high. Currently, a poible effective way worth tudying i to acquire a erie of dynamic continuou image. Through the change of two continuou image, the cloud can be detected effectively. REFERENCES Haruma Ihida, Takahi Y. Nakaima, 9. Development of an unbiaed cloud detection algorithm for a paceborne multipectral imager. Journal of Geophyical Reearch, 4, pp. -6. Calbo, Joep, Jeff Sabburg, 8. Feature extraction from whole-ky ground-baed image for cloud-type recognition. Journal of Atmopheric and Oceanic Technology, 5(), pp. 34. Zhou Quan,. The Claification of Remote Sening Cloud Image Baed on Multicale Feature. Univerity of Science and Technology of China. Mark S.Nixon, Alberto S.Aguado,. Feature extraction & image proceing. Publihing Houe of Electronic Indutry, pp. -3. (a)tet image Xia Dehen, Jin Sheng, Wang Jian, 999. Fractal Dimenion and GGCM Meteorology Cloud Picture Recognition. Journal of Naning Univerity of Science and Technology, 3(4), pp (b)detected image Cao Qiong, Zheng Hong, i Xinghan, 7. A Method for Detecting Cloud in Satellite Remote Sening Image Baed on Texture. Acta Aeronautica Et Atronautica Sinica, 8(3), pp (c)tet image P. Tzoumanika, A. Kazantzidi, A.F. Bai, et al, 3. Cloud Detection and Claification with the Ue of Whole-Sky Ground-Baed Image. Springer Atmopheric Science, pp (d)detected image Bao Jian, i Xiaorun, 8. Unupervied claification of remote image uing K-mean algorithm. Mechanical and Electrical Engineering Magazine, 5(3), pp (e)tet image (f)detected image ACKNOWEDGEMENTS The paper i upported by State Baic Reearch Development Program ( 863 Program ) (AAA35) and Maor State Baic Reearch Development Program ( 973 Program ) (9CB7395). (g)tet image (h)detected image Figure 3. Cloud detection reult of ome tet image S 4. CONCUSION CONCUSIONS The experiment reult indicate that the propoed method i capable of reaonable dicrimination between cloudy and clear- 77