Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images
Diagnostic Analysis on Change Vector Analysis Methods for LCCD Using Remote Sensing Images
复制标题
DOI：
10.1109/jstars.2021.3115481
复制
发表时间：
2021
影响因子：
5.5
通讯作者：
Lv Zhiyong;Fengjun Wang;L. Xie;Weiwei Sun;N. Falco;J. Benediktsson;Z. You
中科院分区：
文献类型：
--
作者：
Lv Zhiyong;Fengjun Wang;L. Xie;Weiwei Sun;N. Falco;J. Benediktsson;Z. You
变化向量分析（CVA）是一种简单而有吸引力的方法来检测遥感图像的变化。自1980年首次引入以来，CVA越来越受到遥感界的关注，导致在CVA概念的基础上定义了几种新的方法，同时扩大了其适用性。在这篇文章中，我们提供了一个广泛的审查基于CVA-based方法的背景下，土地覆盖变化检测（LCCD）。本文首先回顾了基于CVA-LCCD的遥感图像处理方法的发展，并对一些经典的相关方法进行了讨论。然后，我们分析和比较五种方法的性能。该分析是在由不同传感器和平台（例如，Landsat、Quick Bird和机载）和空间分辨率（从0.5米/像素到30米/像素），包括城市和自然景观的场景。分析表明，几个此外，比较不同方法的检测精度意味着，图像场景的内容仍然发挥着重要作用时，无视不同方法的独特偏好。
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.