An improved change detection approach using tri-temporal logic-verified change vector analysis

Abstract Change vector analysis (CVA) is an effective and widely used unsupervised change detection algorithm in remote sensing. It separates changed pixels from unchanged pixels by binarizing bi-temporal difference image. However, the results and performance are affected by the image acquisitions at different dates and the threshold decision rules for change magnitudes, resulting in serious false and missed detections. This paper proposed a novel tri-temporal logic-verified change vector analysis (TLCVA) approach which can identify the errors of CVA through logical reasoning and judgement with an additional temporal image assistance. This approach can not only achieve a reliable modification to the original change detection results, but also produce two additional improved change detection results in the logical circulation of land surface change automatically. The proposed method consists of three parts: traditional CVA change detection, automated sample selection, and refined modification based on SVM posterior probability comparison in temporal space. It was experimented by land cover change detection from Sentinel-2 and Planet Labs images in three study areas located in Ma’anshan, Nanjing and Taizhou City. The results show that accuracies have significant improvements by the TLCVA approach, and omission and commission errors reduce obviously. The generalization, sensitivity and efficiency of the proposed approach were also analyzed in the experiments. It is concluded that different threshold decision methods of preliminary CVA in the proposed approach can work effectively and efficiently, and a small size of training samples selected from the automated sample decision method is enough to achieve improved change detection performance.