Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images

Change vector analysis (CVA) is a simple yet attractive method to detect changes with remote sensing images. Since its first introduction in 1980, CVA has received increased attention by the remote sensing community, leading to the definition of several new methodologies based on the CVA's concept while extending its applicability. In this study, we provide an extensive review of CVA-based approaches in the context of land-cover change detection (LCCD). We first provide the mathematical background of the CVA and review the properties of several promising approaches. We then analyze and compare the performance of five selected methods. The analysis was carried out on seven real datasets acquired by different sensors and platforms (e.g. Landsat, Quick Bird, airborne), spatial resolutions (from 0.5 m/pixel to 30 m/pixel), with scenes from both urban and natural landscapes. The analysis shows several findings that the performance of CVA-based approaches is in general resolution dependency, and the detection accuracies of a specific method vary with different input datasets, for example, when applying the classical CVA to the datasets with resolution from 0.5 m/pixel to 30 m/pixel, the accuracy of FA ranges from 2.26\% to 23.22\%. Furthermore, the diagnoses also remind that the detection accuracies for a specific method varied with the size of the area being considered for a given dataset. Moreover, comparing the detection accuracies of different methods implies that the content of an image scene still plays an important role when disregarding the unique preferences of different methods