Abstract
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 from the remote sensing community, leading to the definition of several new methodologies based on the CVAs concept while extending its applicability. In this article, we provide an extensive review of CVA-based approaches in the context of land-cover change detection (LCCD). We first reviewed the development of the CVA-based LCCD method with remote sensing images, and some classical-related methods were discussed. Then, we 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, and airborne) and spatial resolutions (from 0.5 to 30 m/pixel), with scenes from both urban and natural landscapes. The analysis shows several 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.
Highlights
T HE continuous development and improvement of remote sensing technology for Earth observation has enabled new capabilities in characterizing and monitoring natural phenomena and human activities from local to global scale
These methods include the classical change vector analysis (CVA) [13], the CVA coupled with Markov random field (CVA_MRF) [14], the CVA integrated with the spectral angle mapper (CVA_SAM) [15], robust CVA (RCVA) [16], deep CVA (DCVA) [1], and tritemporal logic-verified CVA (TLCVA) [17], which are widely used for land-cover change detection (LCCD) with remote sensing images
We briefly reviewed the CVA-based LCCD methods with remote sensing images, and some of the widely used techniques, including the classical CVA [13], CVA_MRF [14], CVA_SAM [15], RCVA [16], DCVA [1], and TLCVA [17], were diagnosed with remote sensing images of different resolutions
Summary
T HE continuous development and improvement of remote sensing technology for Earth observation has enabled new capabilities in characterizing and monitoring natural phenomena and human activities from local to global scale. We give a comprehensive diagnosis on the performance of six selected methods with different datasets These methods include the classical CVA [13], the CVA coupled with Markov random field (CVA_MRF) [14], the CVA integrated with the spectral angle mapper (CVA_SAM) [15], robust CVA (RCVA) [16], deep CVA (DCVA) [1], and tritemporal logic-verified CVA (TLCVA) [17], which are widely used for LCCD with remote sensing images. More details about these methods will be presented
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