Land Cover Change Detection in Satellite Image Time Series Using an Active Learning Method

Some of the first Earth Observation (EO) missions date back to the 1970s. Over the time, large datasets of Satellite Image Time Series (SITS) have been used to identify and monitor land cover evolutions. The processing complexity increases proportionally to the time span of the Earth Observation (EO) series. Because of the SITS dataset complexity and variety of contained evolution patterns, most unsupervised classification methods fail to detect and isolate the user's evolution pattern of interest. This is usually caused by the discrepancy between automatically extracted low-level features and high-level meaning assigned by the user who searches for a specific evolution. In an effort to find a solution for this difficult task, this paper presents a SVM based active learning method for the extraction of specific evolution classes from SITS. Several experiments were conducted on a dataset composed of Landsat 4 TM (Thematic Mapper) and Landsat 5 TM acquisitions over Bucharest, Romania, in the time interval of 1984-1993.