Change Vector Analysis in Posterior Probability Space: A New Method for
Land Cover Change Detection
Jin Chen a* , Xuehong Chena, Xihong Cuia, Jun Chenb
a State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
b National Geoinformatic Centre of China, Beijing, 100255, China
Abstract:
Post Classification Comparison (PCC) and Change Vector Analysis (CVA) have been widely used for land use/ cover change detection using remotely sensed data. However, PCC suffers from error cumulation stemmed from individual image classification error, while strict requirement of radiometric consistency in remotely sensed data is a bottleneck of CVA. This paper proposed a new method named change vector analysis in posterior probability space (CVAPS), which analyze the posterior probability by using CVA. CVAPS approach was applied and validated by a case study of land cover change detection in the Shunyi district of Beijing, China based on multi-temporal Landsat Thematic Mapper data. Accuracies of “change/no-change” detection and “from-to” types of change were assessed. The results show that error cumulation in PCC was reduced in CVAPS. Furthermore, the main drawbacks in CVA were also alleviated effectively by using CVAPS. Therefore, CVAPS is potentially useful in land-use/ cover change detection.
Published in IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 8 (2): 317-321,2011.
(SCI, Impact index 1.4).
