转化(遗传学)
计算机科学
保险丝(电气)
人工智能
变更检测
图像(数学)
模式识别(心理学)
过程(计算)
图形
计算机视觉
图像处理
领域(数学分析)
数据挖掘
数学
理论计算机科学
操作系统
电气工程
工程类
基因
数学分析
生物化学
化学
作者
Kuowei Xiao,Yuli Sun,Lin Lei
出处
期刊:Remote Sensing
[MDPI AG]
日期:2022-11-07
卷期号:14 (21): 5622-5622
被引量:1
摘要
Change detection (CD) with heterogeneous images is currently attracting extensive attention in remote sensing. In order to make heterogeneous images comparable, the image transformation methods transform one image into the domain of another image, which can simultaneously obtain a forward difference map (FDM) and backward difference map (BDM). However, previous methods only fuse the FDM and BDM in the post-processing stage, which cannot fundamentally improve the performance of CD. In this paper, a change alignment-based change detection (CACD) framework for unsupervised heterogeneous CD is proposed to deeply utilize the complementary information of the FDM and BDM in the image transformation process, which enhances the effect of domain transformation, thus improving CD performance. To reduce the dependence of the transformation network on labeled samples, we propose a graph structure-based strategy of generating prior masks to guide the network, which can reduce the influence of changing regions on the transformation network in an unsupervised way. More importantly, based on the fact that the FDM and BDM are representing the same change event, we perform change alignment during the image transformation, which can enhance the image transformation effect and enable FDM and BDM to effectively indicate the real change region. Comparative experiments are conducted with six state-of-the-art methods on five heterogeneous CD datasets, showing that the proposed CACD achieves the best performance with an average overall accuracy (OA) of 95.9% on different datasets and at least 6.8% improvement in the kappa coefficient.
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