Abstract

Change vector analysis in posterior probability space (CVAPS) has been introduced recently as an effective method for change detection. CVAPS is based on the length of change and its direction in a posterior probability (PP) space. However, CVAPS is prone to similar direction cosine values. An approach to analyzing change by combining CVAPS and a new method called posterior probability space angle mapper (PSAM) is proposed in this study. PSAM establishes the similarity between two PP vectors of a pixel for two different dates by calculating the angle between them. This research presents a new change-detection algorithm based on combining CVAPS and PSAM (CVAPSAM), which is able to fully exploit change vectors in a PP space. While CVAPS uses a suitable threshold value to detect changes, CVAPSAM does not need to set a threshold. In addition, it reduces the similar direction cosine values source of error in identifying ‘from-to’ classes.

摘要

最近引入了后验概率空间中的变化向量分析（CVAPS）作为变化检测的有效方法。CVAPS 基于后验概率 (PP) 空间中的变化长度及其方向。然而，CVAPS 容易出现类似的方向余弦值。本研究提出了一种结合 CVAPS 和一种称为后验概率空间角度映射器 (PSAM) 的新方法来分析变化的方法。PSAM 通过计算两个不同日期的像素的两个 PP 矢量之间的角度来建立它们之间的相似性。本研究提出了一种基于组合 CVAPS 和 PSAM (CVAPSAM) 的新变化检测算法，该算法能够充分利用 PP 空间中的变化向量。虽然 CVAPS 使用合适的阈值来检测变化，但 CVAPSAM 不需要设置阈值。此外，