Updating land cover map based on change detection of high-resolution remote sensing images

Abstract

Multi-temporal high-resolution land cover (LC) information is of great significance to landscape monitoring, environmental assessment, and local climate change. Given the LC map in the former phase, an automatic LC updating approach based on change detection of high-resolution remote sensing images is proposed. First, object-based change detection is implemented combining spectral bands, normalized difference vegetation index, and normalized difference water index. Second, the changed objects are classified using training samples generated from the unchanged area, and the LC labels of the training samples were transferred from the LC map in the former phase. Finally, as the updated objects with abnormal area (AREA) or perimeter-area ratio (PARA) are recognized as slivers or spurious stretches, and removed using specifically designed rules, an AREA-PARA-based updating method is proposed to update the LC map. Two pairs of GaoFen-1 panchromatic and multispectral sensor images acquired in 2013 and 2015 of two areas in Jiangsu, China, were used to validate the effectiveness of the proposed method. Results and the comparisons with two other updating methods manifested the superiority of the APU method in reducing abnormal LC fragmentation and shape complexity, and maintaining LC consistency between two phases.