Multiscale segmentation of high-resolution remote sensing images based on region merging

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Journal of Nanjing University(Natural Sciences) ›› 2015, Vol. 51 ›› Issue (5) : 1030-1038.

Zhang Xueliang1, Feng Xuezhi1,2,3*, Xiao Pengfeng1,2,3

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Abstract

Image segmentation is the critical step in object-based analysis of high-resolution remote sensing images. In this study, we examined the key steps of region merging method for remote sensing image segmentation. The following five aspects are involved. (1) We construct a graph model, including the region adjacency graph and the nearest neighbor graph, to improve segmentation efficiency. (2) The features of region homogeneity, shape, and edges are integrated in the merging criterion to improve segmentation accuracy. (3) We present and compare three different region merging strategies, including the global-oriented, local-oriented and hybrid region merging. (4) A step-wise scale parameter strategy is proposed to set scale parameters, aiming at producing nested multiscale segmentations by local-oriented region merging methods. (5) We present a segment tree model to represent multiscale segments, which can be used to produce segmentations at different scale extremely fast without repeating the region merging procedure. The proposed methods are applicable for object-based image analysis, geographic object recognition, and information extraction from high spatial resolution remote sensing images.

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Zhang Xueliang1, Feng Xuezhi1,2,3*, Xiao Pengfeng1,2,3
. Multiscale segmentation of high-resolution remote sensing images based on region merging[J]. Journal of Nanjing University(Natural Sciences), 2015, 51(5): 1030-1038

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