Density-based anomaly detection for short-term land use-land cover change detection using repeat-pass Radarsat images
基于时空域密度异常的土地利用/土地覆盖短期变化检测

Although drastic land use change took place rapidly in very short interval in the Pearl River Delta of China, hardly any research has been done on this for lacking of available data. Located in the south of China, Pearl River Delta suffers from heavy cloud cover for more than half of the year. This makes real-time land use-land cover change (LUCC) monitoring almost impossible using optical remote sensing images. In this paper, the orbital highest resolution SAR (Synthetic Aperture Radar) data-Fine Mode Radarsat data is selected and three scenes of repeat-pass Radarsat images are used for short-term land use change detection. Short-term land use change caused by human activity is considered as spatial and temporal abnormal in time series images. And a Density-Based Anomaly Detection (DBAD) algorithm is designed to detect abnormally changed land parcels in time series Radarsat images. After that field survey data are used for validation. The result shows that DBAD gained better accuracy in comparison with object-based image regression method. Besides, DBAD exhibits greater capabilities in detecting under-constructed area and newly built up area (with error lower than 12%). While for built up area and some mixed used area, DBAD gained relatively lower accuracy (with error from 10% to 28.57%).