Abstract
The neighborhood-based method was proposed and widely used in the change detection of synthetic aperture radar (SAR) images because the neighborhood information of SAR images is effective to reduce the negative effect of speckle noise. Nevertheless, for the neighborhood-based method, it is unreasonable to use a fixed window size for the entire image because the optimal window size of different pixels in an image is different. Hence, if you let the neighborhood-based method use a large window to significantly suppress noise, it cannot preserve the detail information such as the edge of a changed area. To overcome this drawback, we propose a spatial-temporal adaptive neighborhood-based ratio (STANR) approach for change detection in SAR images. STANR employs heterogeneity to adaptively select the spatial homogeneity neighborhood and uses the temporal adaptive strategy to determine multi-temporal neighborhood windows. Experimental results on two data sets show that STANR can both suppress the negative influence of noise and preserve edge details, and can obtain a better difference image than other state-of-the-art methods.
Highlights
Change detection techniques can be applied to detect changes that occurred in the study area, which usually uses remote sensing images acquired in the same geographical area on two different dates [1]
For the difference images acquired by using mean ratio (MR), with the increase of the window size, the pixel intensity in the rectangle A became very smooth because MR exploits the neighborhood information of synthetic aperture radar (SAR) image; the intensity of the unchanged pixel in the rectangle B became larger, and it was hard to distinguish the changed and unchanged areas in rectangle B when the window was 9 × 9, which reflected the drawback of using the same window size for the entire image
In the difference image acquired with spatial-temporal adaptive neighborhood-based ratio (STANR), the pixel intensity in rectangle A was very smooth, and there were clear boundaries between the changed and unchanged areas in rectangle B, which was a benefit from the spatial-temporal adaptive window selection strategy adopted by the proposed method
Summary
Change detection techniques can be applied to detect changes that occurred in the study area, which usually uses remote sensing images acquired in the same geographical area on two different dates [1]. Generating the difference image is a key step for change detection in SAR images [5,13] In this context, subtraction and ratio (R) operators are two classic methods. The optimal window size for the entire image is generally artificially determined by comparing multiple experimental results at different window sizes. The optimal window is a large window for a homogeneous area because it is beneficial to significantly restrain the negative influence of speckle noise, thereby accurately describing the change occurred in multi-temporal SAR images. We employ the heterogeneity of a neighborhood to describe its homogeneity degree and select the optimal window size that satisfies the setting condition adaptively On this basis, the spatial-temporal adaptive neighborhood-based ratio (STANR) method is proposed for change detection in SAR images
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