Novel Piecewise Distance Based on Adaptive Region Key-Points Extraction for LCCD With VHR Remote-Sensing Images

Abstract

Land cover change detection (LCCD) with very high-resolution remote-sensing images (VHR_RSIs) is important in observing surface change on Earth. However, pseudo changes usually reduces the accuracy of the detection map. In this paper, novel piecewise distance based on adaptive region key-points extraction called sparse key-point distance (SKPD) is developed to measure the change magnitude between the bitemporal VHR_RSIs for LCCD. The proposed approach consists of three steps. First, an adaptive region generation algorithm is promoted for exploring spatial-contextual information. Then, the adaptive region around each pixel is sparsely represented with the box-whisker plot theory and the adaptive region is converted into a sparse key point vector. Finally, a piecewise distance is defined to measure the change magnitude between the bi-temporal images. While the entire VHR_RSIs are scanned and the proposed SKPD method proceeds on a pixel by pixel basis, a change magnitude image (CMI) can be generated and a binary threshold method can be applied on the CMI to obtain a change detection map. Experimental results based on four pairs of real VHR_RSIs and four state-of-the-art methods effectively demonstrated the superiority of the proposed approach for achieving LCCD with VHR_RSIs, such as the improvements for the four datasets are 5.25%, 14.76%, 18.13%, and 22.24%, respectively in terms of overall accuracy.