Journal
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
Volume 14, Issue -, Pages 10199-10212Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSTARS.2021.3115481
Keywords
Earth; Indexes; Hyperspectral imaging; Vegetation mapping; Task analysis; Sensors; Forestry; Change vector analysis (CVA); land-cover change detection (LCCD); remote sensing images
Categories
Funding
- National Natural Science Foundation of China [61701396, 42001407, 41971296]
- Guangdong Basic and Applied Basic Research Foundation [2019A1515110729]
- Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, MNR [KF-2019-04-042]
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The study provides an extensive review of CVA-based approaches in land-cover change detection, analyzing and comparing the performance of five selected methods. The results indicate that the content of an image scene remains important in detecting changes accurately.
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.
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