Multipolarimetric Sar Image Change Detection Based on Multiscale Feature-level Fusion

Many methodologies of change detection have been discussed in the literature, but most of them are tested on only optical images or traditional synthetic-aperture radar (SAR) images. Few studies have investigated multipolarimetric SAR image change detection. In this study, we presented a type of multipolarimetric SAR image change detection approach based on nonsubsampled contourlet transform and multiscale feature-level fusion techniques. In this approach, Instead of denoising an image in advance, the nonsubsampled contourlet transform multiscale decomposition was used to reduce the effect of speckle noise by processing only the low-frequency sub-band coefficients of the decomposed image, and the multiscale feature-level fusion technique was employed to integrate the rich information obtained from various polarization images. Because SAR image information is dependent on scale, a multiscale multipolarimetric feature-level fusion strategy is introduced into the change detection to improve change detection precision; this feature-level fusion can not only achieve complementation of information with different polarizations and on different scales, but also has better robustness against noise. Compared with PCA methods, the proposed method constructs better differential images, resulting in higher change detection precision. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-7/W4, 2015 2015 International Workshop on Image and Data Fusion, 21 – 23 July 2015, Kona, Hawaii, USA This contribution has been peer-reviewed. doi:10.5194/isprsarchives-XL-7-W4-155-2015 155 (n = 0, 1,..., N − 1) is the number of decomposition layers. When 0  n , then D MS X X  0 , i.e. the differential image before decomposition. n MS X on every scale consists of a lowfrequency image (also referred to as approximate signal) and several high-frequency coefficients, i.e.   n J nj n n n MS D D D C X , , , , , 1    , where n C represents the low-frequency coefficient of the decomposition scale image, n j D is the high-frequency subband coefficient of the decomposition scale image in which j (j = 0, 1,..., J − 1) is the number of high-frequency subbands. Because the low-frequency coefficient represents the approximate information of the original image, whereas the high-frequency coefficient contains a large amount of noise, then approximated images on different scales are acquired only by reconstructing the low-frequency image while neglecting high-frequency information. With an increase in the number of decomposition layers n, the image features become increasingly ambiguous, and the quantity of information decreases until it is not sufficient to affect the change detection result. Therefore, the most appropriate decomposition scale must be determined. We determined the optimal decomposition scale by using the minimum entropy difference method. After the optimal decomposition scale is determined, the approximated image set for differential images on various scales ' D X can be acquired.   ' ' ' 0 ' , , , , L D n D D D X X X X    , 1   N L . Step 3. Multiscale feature-level fusion and differential image reconstruction The approximated differential images on different scales contain different difference information, and therefore different change detection results can be extracted from differential images on different scales. Hence, the difference information acquired on different scales can be summarized using the feature-level fusion method so as to reduce differential image dependence on scale. Multiscale fusion is executed on reliable scales only. The multiscale differential images on different scales contain different information quantities: the lower the scale, the greater the quantity of information the corresponding image contains, but the greater the noise component it contains; conversely, the higher the scale, the less information the corresponding image contains, but the lower the noise component it contains. Thus, the differential images on different scales were fused by using the weighted average method after computing the following formula: