Subpixel Change Detection Based on Radial Basis Function with Abundance Image Difference Measure for Remote Sensing Images

Abstract

Recently, land cover change detection has become a research focus of remote sensing. To obtain the change information from remote sensing images at fine spatial and temporal resolutions, subpixel change detection is widely studied and applied. In this paper, a new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed, in which the abundance image difference measure (AIDM) is designed and utilized to enhance the subpixel mapping (SPM) by borrowing the fine spatial distribution of the fine spatial resolution image to decrease the influence of the spectral unmixing error. First, the fine and coarse spatial resolution images are used to develop subpixel change detection. Second, linear spectral mixing modeling and the degradation procedure are conducted on the coarse and fine spatial resolution image to produce two temporal abundance images, respectively. Then, the designed AIDM is utilized to enhance the RBF-based SPM by comparing the two temporal abundance images. At last, the proposed RBF-AIDM method is applied for SPM and subpixel change detection. The synthetic images based on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and real case images based on two temporal Landsat-8 Operational Land Imager (OLI) images and one Moderate Resolution Imaging Spectroradiometer (MODIS) image are undertaken to validate the proposed method. The experimental results indicate that the proposed method can sufficiently decrease the influence of the spectral unmixing error and improve the subpixel change detection results.