Spatial-Contextual Information Utilization Framework for Land Cover Change Detection With Hyperspectral Remote Sensed Images

Land cover change detection (LCCD) using bitemporal remote sensing images is a crucial task for identifying the change areas on the Earth's surface. However, the utilization of hyperspectral remote sensing images (HRSIs) introduces challenges as the detection performance is affected by the spectral noise and deducing change detection accuracies. In this work, we concentrated on utilizing spatial-contextual information to improve the change detection performance while using HRSIs. First, a band selection approach is used to minimize the spectral redundancy of HRSIs. Second, an iterative spatial-adaptive filter is proposed to smooth the noise of HRSIs. Thereafter, the change magnitude between bitemporal HRSIs is measured by coupling change vector analysis and the adaptive region around each pixel, resulting in a change magnitude image (CMI). Subsequently, the CMI is divided into a binary change detection map by using an Ostu threshold method. The experimental results on three pairs of real HRSIs efficiently demonstrated the feasibility and superiorities of the proposed approach compared with six state-of-art methods. For example, the improvement rates are approximately 0.43%-11.83% and 1.05%-15.41% for overall accuracy and average accuracy, respectively. The code of our proposed approach will be available at: https://github.com/ImgSciGroup/2023-HSICD.