A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection
A hypergraph-based context-sensitive representation technique for VHR remote-sensing image change detection
复制标题
基于超图的VHR遥感图像变化检测上下文敏感表示技术
DOI：
10.1080/2150704x.2016.1163744
复制
发表时间：
2016-01-01
影响因子：
3.4
通讯作者：
Zhang, Chenwei
中科院分区：
文献类型：
--
作者：
Jian, Ping;Chen, Keming;Zhang, Chenwei
本文提出了一种针对高分辨率（VHR）遥感图像的半监督变化检测方法。该方法旨在充分利用图像中像素之间的上下文敏感关系来提取变化信息。这是通过基于超图模型的上下文敏感图像表示技术来实现的。首先，将每个时间图像建模为一个超图，该超图利用一组超边缘来捕获图像中像素的上下文敏感属性。其次，通过两个超图之间的相似性和一致性来衡量双时态图像的差异。最后，利用基于超图的半监督分类器对差异图像进行区分。在不同VHR遥感数据集上的实验结果表明了该方法的有效性。
ABSTRACT This article presents a novel semi-supervised change detection approach for very-high-resolution (VHR) remote-sensing images. The proposed approach aims at extracting the change information by making full use of the context-sensitive relationships among pixels in the images. This is accomplished via a context-sensitive image representation technique based on hypergraph model. First, each temporal image is modelled as a hypergraph that utilizes a set of hyperedges to capture the context-sensitive properties of pixels in the image. Second, the difference in the bi-temporal images is measured by both the similarity and the consistency between the two hypergraphs. Finally, the changes are separated from the unchanged ones by a hypergraph-based semi-supervised classifier on the difference image. Experimental results obtained on different VHR remote-sensing data sets demonstrate the effectiveness of the proposed approach.