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Remote sensing image segmentation based on Wilcoxon rank sum test and mean absolute deviation

In this paper, a novel threshold segmentation method for remote sensing images is proposed. The proposed method is based on Wilcoxon rank sum test and mean absolute deviation (MAD) model with color feature and can segment roads and residential areas from vegetation more accurately. Three steps are used to realize the new method. First, we use blue and green color components as paired sample on Wilcoxon rank sum test to partition the vegetation. Second, a road-residential area map is constructed by mean absolute deviation on an improved two dimensional histogram to get the optimal threshold for segmentation. Finally, we fuse vegetation and residential map to get the final segmentation result. Compared with several existing algorithms, the proposed method presents a more accurate segmentation.