A Sub-Pixel Multiple Change Detection Approach for Hyperspectral Imagery

One of the most important applications of remote sensing is change detection (CD). The accurate detection of changes is of great significance for the optimal management of available resources. This article presents an unsupervised ‘multiple-change detection’ method using multi-temporal hyperspectral imaging based on the integration of an unmixing technique, multi-resolution segmentation, similarity measure methods, and the Otsu algorithm. The proposed method is presented in the context of two main scenarios: the first scenario is hyperspectral change detection (HSCD) at the sub-pixel level with no ancillary data; and the second is the HSCD at the sub-pixel level based on ancillary data (high-resolution panchromatic (PAN) data). The main advantages of the proposed method are that it is unsupervised, easy to use, providing ‘multiple-change’ maps at the sub-pixel level with high accuracy. To evaluate the performance of the proposed method, real bi-temporal hyperspectral Hyperion images and high spatial resolution of PAN data belonging to the Advanced Land Imager (ALI) sensor with a variety of land cover classes were used. The results show the overall accuracy improved by >5%, and kappa coefficient improved by >0.13 with respect to the results obtained from their original resolution.