A robust anomaly based change detection method for time-series remote sensing images

35th International Symposium on Remote Sensing of Environment

地球科学;生态环境科学

Shoujing, Yin^1 ; Qiao, Wang^1 ; Chuanqing, Wu^1 ; Xiaoling, Chen^2 ; Wandong, Ma^1 ; Huiqin, Mao^1

Satellite Environment Center, Ministry of Environmental Protection, Beijing, China^1

State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan, China^2

关键词: Global environment;    Human activities;    Land-cover types;    Long time series;    Normalized difference vegetation index;    Remote sensing images;    Spatial and temporal changes;    Vegetation phenology;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/17/1/012059/pdf
DOI  :  10.1088/1755-1315/17/1/012059

学科分类：环境科学（综合）

来源: IOP

【 摘 要 】

Time-series remote sensing images record changes happening on the earth surface, which include not only abnormal changes like human activities and emergencies (e.g. fire, drought, insect pest etc.), but also changes caused by vegetation phenology and climate changes. Yet, challenges occur in analyzing global environment changes and even the internal forces. This paper proposes a robust Anomaly Based Change Detection method (ABCD) for time-series images analysis by detecting abnormal points in data sets, which do not need to follow a normal distribution. With ABCD we can detect when and where changes occur, which is the prerequisite condition of global change studies. ABCD was tested initially with 10-day SPOT VGT NDVI (Normalized Difference Vegetation Index) times series tracking land cover type changes, seasonality and noise, then validated to real data in a large area in Jiangxi, south of China. Initial results show that ABCD can precisely detect spatial and temporal changes from long time series images rapidly.