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

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1 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 of China, Anhui, Hefei , P.R. China { } Abstract Detecting change of remote sensing images is very important for some applications such as tracking of moving objects and motion estimation. Traditional work on change detection has largely been based on segmentation approaches of a single feature. It excessively depends on the threshold of the single feature to determine whether the change of spectral information is caused by the change of object. The results of traditional change detection approaches can easily be affected by noise, blur, contrast level and brightness level. To overcome the deficiency, we improve the Itti visual saliency model and propose an effective and robust approach based on saliency map to detect real changed regions between two remote sensing images of a given scene acquired at different times. The results of the experiments indicate that our approach is very robust to noise, contrast level and brightness level. Keywords--- Change detection, Remote sensing image, Saliency map, Visual saliency model. 1. Introduction Change detection in remote sensing images is the most important research area of the remote sensing technology and application [1]. It finds important applications within different contexts, ranging from video surveillance to video coding, tracking of moving objects, and motion estimation [2]. Usually change detection in remote sensing involves the analysis of two registered, aerial or satellite multi-spectral images from the same geographical area obtained at two different times. Such an analysis aims at identifying changes that have occurred in the same geographical area between the two times considered. There are lots of change detection methods, which can be divided into two categories [3], [16], [17]: the supervised approach and the unsupervised approach. The former is based on supervised classification methods, which require a learning set with multi-temporal ground truth while the latter perform change detection without relying on any additional information. As the generation of a learning set is usually a difficult and expensive task, the use of unsupervised methods is of great interest in many applications in which a learning set is not available. In remote sensing images not only the object change can result in the change the spectral information, the change of season, sun angle, sensor attitude and atmosphere condition can also cause the change of spectral information. And there are always noise and blur in remote sensing images. A series of remote sensing images may have different contrast level or brightness level even if they are the same scene. Though a great deal of effort has been expended on change detection in remote sensing images, traditional work on unsupervised change detection has largely been based on statistical segment approaches of a single feature [1], [4], [5], [6]. It excessively depends on the critical threshold of the single feature to determine whether the change of spectral information is caused by the change of object [15]. And usually setting an appropriate threshold automatically is very difficult. The deficiency of statistical segment detection approaches is that the detection result can easily be affected by noise, blur, contrast level and intensity level [20]. So finding an effective and robust approach for change detection in remote sensing images is necessary and urgent. In this paper, we introduce a visual saliency model for change detection and propose a novel approach to detect real changed regions in two remote sensing image of a given scene acquired at different times. Our visual saliency model proposed in this paper is based on a bottom-up visual saliency model which was proposed by Itti in 1998 [7]. Comparing with traditional threshold segmentation method based on

2 statistic, our approach does not need to obtain the changed regions by computing any optimal threshold complexly. The detection result of our approach only depends on the final saliency map. Our approach represents good in low-contrast experiments, noise experiments and blurs experiments. The rest of the paper is organized as the follows. In the next section, the bottom-up visual saliency model which was proposed by Itti in 1998 is introduced. In section 3 the change detection algorithm based on Itti model is presented. Extraction of multi-features, computation of the saliency map for each diff-image and multi-features saliency maps combination are described in detail. Section 4 presents experimental results for our detection approach. Conclusions are drawn in Section 5. suitable to introduce the model and improve it to detect change in remote sensing images. 2. Visual Saliency Model Figure 2 Our Visual Saliency Model Figure 1 Itti Visual Saliency Model The visual saliency model used in this paper is based on the Itti visual saliency model which is part of Itti visual attention model [7]. Itti visual attention model is used for rapid scene analysis and visual salient region search. It builds up a second biologically-plausible architecture to explain human visual attention [8]. In Itti model, multi-scale features are combined into a single topographical saliency map. And the purpose of the saliency map is to represent the local conspicuity at every location in the visual field by a scalar quantity and to guide the selection of attended locations based on the spatial distribution of saliency [7]. Different spatial locations compete for saliency within each map, such that only locations which locally stand out from their surround can persist. A combination of the feature maps provides bottom-up input to the saliency map, modeled as a dynamical neural network. The experiments prove that Itti model is very robust to noise and blur [7], [9], [10]. So it is For low computational complexity, all feature maps are computed and combined in the same scale in our visual saliency model. Because most remote sensing images are grayscale images, we choose texture [12] instead of colors as a feature to describe the input image. And a global saliency measurement is proposed instead of center-surround differences. This global saliency measurement is simpler than the computation of center-surround differences. It describes how different every location of the input image is from the average saliency value in this image. Comparing with Itti saliency model, our model is more suitable for our change detection model. As Itti model, our saliency model also represents a complete account of bottom-up saliency and provides a parallel method for saliency map computation [7]. 3. Detection Algorithm In this paper, we focus on one of the most widely used types of unsupervised change-detection techniques, which are based on the so-called difference image [17], [18]. Comparing with traditional approaches, our approach is an automatic and unsupervised approach in which multi-features are extracted, analyzed and fused into a single saliency map. And it does not need any threshold to determine whether the change of spectral information is caused by the change of object. It is believed that the salient

3 regions in the final saliency map of the diff-image are just the real changed regions which we want to detect. Our experiments will prove this later. Figure 3 Our Detect Model Based on our saliency model, the detection is divided into three stages as Figure 3. The first stage is feature extraction. In this stage, for two remote sensing images of a given scene acquired at different times, diff-images of multi-features (including intensity, orientation and texture) are generated. The second stage is saliency maps generation at which the saliency map is computed for each diff-image. The third stage is saliency maps combination at which saliency maps of multi-features are combined into a single topographical saliency map. In this way, for two remote sensing images of a given scene acquired at different times, a series of changed regions between them are detected by the final saliency map Feature Extraction In early vision system, we choose intensity, orientation and texture as features to describe a remote sensing image. For a remote sensing image, we stretch the gray value of each pixel of the input image as intensity feature map L. Then we use 2D Gabor wavelet Filter [11] to get the orientation feature maps. The exact formula of the Gabor function which is used in this paper is as follows: g( θ, x, y) = Kexp( πσ2( x2+ y2)) 2 2 (exp( j 2 πσ ( xcosθ + ysin θ)) exp( σ )) 2 Where K is a constant value which scales the magnitude of the Gaussian envelop. σ is another constant value which scales the two axis of the Gaussian envelop. θ is the rotation angle of the Gaussian envelop which describes the orientation of the Gabor wavelet filter. And the exact formula of the orientation map is: θ O ( x, y) = L( x, y) g( θ, x, y) (2) θ = 0, 45,90,135 (1) Where means convolving here. We use discrete moment transform (DMT) [12] to describe the texture feature of a remote sensing image. If the size of the kernel is 2k + 1, the exact formula of DMT is as follows: r= k s= k p, q p q T ( x, y) = L( x r, y s) r s (3) r= k s= k Actually, we only compute T 1,0 0,1 1,1, T and T to describe the texture feature. Now, we mark each feature map given above with a unified token F ( F 11 which denotes intensity feature map, F ( 1,2,3,4) 2 j j = which denote four orientation feature maps and F 3 j ( j = 1,2,3) which denote three texture feature maps). We assume the two input remote sensing images of a given scene acquired at different times as I 1 & I 2. So the diff-image of each feature map can be computed as follows: 1 2 DF ( x, y) = F ( x, y) F ( x, y) (4) 3.2. Saliency maps Generation To increase the difference between salient regions and non-salient regions, we propose the global saliency measurement to improve the saliency model of Itti and compute the saliency map of each diff-image iteratively. In our global saliency measurement, the saliency map of each diff-image can be computed as follows: DS 0 ( x, y) = DF ( x, y) (5) height width l+ 1 l l = u= 1 v= 1 DS ( x, y) DS ( x, y) DS ( u, v) / (6) ( width height)

4 Where width and height are the width and the height l of DS, l is the times of iteration Saliency maps Combination For each feature, we regularize those saliency maps of the diff-images by the normalization operator N (.) [7], [13], [14] and combine them into one saliency map. So all feature maps are combined into three saliency maps DSi (i = 1, 2, 3) which denote three features [19]. Then the three saliency maps are still normalized by the normalization operator N (.) and combined Figure 4 (a) Image I1&I2 into the final saliency map DS. 1 FNumi N ( DS )( x, y) (7) FNumi j =1 1 CNum DS ( x, y ) = wi N ( DSi )( x, y) (8) CNum i =1 DSi ( x, y ) = CNum w = CNum i ( wi 0, i = 1, 2,..., CNum) i =1 Where FNumi is the number of the saliency maps Figure 4 (b) Our result & Segmentation result of the feature Fi, CNum is the number of feature categories and wi is the weight of the feature Fi. 4. Experiments and Analysis Our experiments are performed on a PC with AMD Athlon XP (1.91GHz) processor and 1G memory. The operating system is Microsoft Windows XP Professional Service Pack 2 and the software development environment is Matlab In our experiments, we simulate the real noise, blur, different brightness level and contrast level environments in which two remote sensing images of a given scene may be acquired at different times. Then we compare our approach with a traditional approach which is based on segmentation of a single feature. And the segmentation approach which we choose here is the minimal-error threshold segmentation based on intensity histogram match [6]. From the results which Figure 4 & 5 show, it is clear that our approach is more robust to noise, brightness level and contrast level than the segmentation approach. Because of multi-features saliency maps fusion, our approach can hardly be affected by the change of one single feature. Figure 5 (a) Image I1&I2 Figure 5 (b) Our result & Segmentation result Two hundred different experiments of noise, blur, brightness level and contrast level have been performed. Most remote sensing images used in our experiments are Quickbird sensor images, the rest are SPOT-5 sensor images. And the results of all experiments indicate that our approach is very robust to noise, brightness level and contrast level. Table 1 is the statistical results of our approach and the segmentation approach in two hundred different experiments. The data of Table 1 indicates that our

5 approach is superior to the threshold segmentation approach. Comparing with the segmentation approach, both the false alarms ratio and the missed alarms ratio are reduced. And the correct ratio of our approach can be increased along with the increase of iteration times. Table 1 Statistical comparison of our approach and the segmentation approach Approach Threshold Segmentation Correct ratio False alarms ratio Missed alarms ratio 80% 20% 7% Our Approach 95% 5% 5% 5. Conclusions We propose a novel approach for change detection in remote sensing images in this paper. In our approach, multi-features are extracted, analyzed, and fused into a single saliency map. The results of our experiments indicate that our approach is very effective and robust for change detection in remote sensing images. And visual saliency model can be used to detect changes in remote sensing images. For future work, we plan to experiment with multi-scale feature maps fusion in order to improve the robustness of our approach. We also plan to recognize the real changed objects and find the relationship between the changed regions and their surroundings. 6. References [1] Ma Jian-wen, Tian Guo-liang, Review of development of remote sensing change detection technology, Advance in Earth Sciences, 2004, Vol.19 (2), [2] Y. Bazi, L. Bruzzone, F. Melgani, An unsupervised approach based on the generalized Gaussian model to automatic change detection in multi-temporal SAR images, IEEE Transaction on Geo-science and Remote Sensing, 2005, in press. [3] L. Bruzzone and S. B. Serpico, An iterative technique for the detection of land-cover transitions in multi-temporal remote-sensing images, IEEE Trans. Geosci. Remote Sensing, vol. 35, pp , July [4] K. R. Merril and L. Jiajun, A comparison of four algorithms for change detection in an urban environment, Remote Sens. Environ, vol. 63, pp , image segmentation, IEEE Trans Image Processing, vol. 6, pp , Oct [6] L. Bruzzone, D. F. Prieto, Automatic Analysis of the Difference Image for Unsupervised Change Detection, IEEE Trans. on Geoscience and Remote Sensing, 2000, 38(3): [7] L. Itti, C. Koch, E. Niebur, A model of saliency-based visual attention for rapid scene analysis, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1998, 20(11): [8] A.M. Treisman and G. Gelade, A Feature-Integration Theory of Attention, Cognitive Psychology, vol. 12, no. 1, pp , Jan, [9] L. Itti, Models of Bottom-Up and Top-Down Visual Attention, California Institute of Technology, PhD Thesis, [10] L. Itti, C. Koch, Computational modeling of visual attention, Nature Reviews Neuroscience, 2001, 2(3): [11] Tai Sing Lee, Image Representation Using 2D Gabor Wavelets, IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 18, No. 10, October [12] VD. Gesu, C. Valent, L. Strinati, Local operators to detect regions of interest, Pattern Recognition Letter, 1997, 18(11-13): [13] L. Itti, C. Koch, A comparison of feature combination strategies for saliency-based visual attention systems, Conference on Human Vision and Electronic Imaging IV. SPIE, Vol. 3644, 1999, pp [14] L. Itti, C. Koch, Feature combination strategies for saliency-based visual attention systems, Journal of Electronic Imaging, 2001, 10(1): [15] ChenYang, Chen Ying, Lin Yi, Object-oriented classification of remote sensing data for change detection Proc. of SPIE, Vol. 6419, 64191J, [16] Radke R.J., Andra S., Al-Kofani O. and Roysan B, Image change detection algorithms: A systematic survey, IEEE Trans. Image Processing, 14 (3): , [17] A. Singh, Digital change detection techniques using remotely-sensed data, Int. J. Remote Sensing, vol. 10, no. 6, pp , [5] Y. Delignon, A. Marzouki, and W. Pieczynski, Estimation of generalized mixture and its application in

6 [18] T. Fung, An assessment of TM imagery for land-cover change detection, IEEE Trans. Geosci. Remote Sensing, vol. 28, no. 12, pp [19] P. Zhang, RS. Wang, Detecting salient regions based on location shift and extent trace, Journal of Software, 2004, 15(6): [20] X. Dai and S. Khorram, The effects of image misregistration on the accuracy of remotely sensed change detection, IEEE Trans. Geosci Remote Sensing, vol. 36, pp , Sept

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