论文标题
用于异质变化检测的图形信号处理第二部分：光谱结构域分析
Graph Signal Processing for Heterogeneous Change Detection Part II: Spectral Domain Analysis
论文作者
论文摘要
这是本文的第二部分，为异质变化检测（HCD）问题提供了新的策略，即从图形信号处理（GSP）的角度解决HCD。我们构造一个图表以表示每个图像的结构，并将每个图像视为图表上定义的图形信号。这样，我们可以将HCD问题转换为图表上定义的系统的信号响应的比较。在第一部分中，通过比较顶点域的图之间的结构差来衡量变化。在本第二部分中，我们分析了来自光谱域的HCD的GSP。我们首先分析了同一图上不同图像的光谱特性，并表明它们的光谱表现出共同点和差异。特别是，正是变化导致了光谱的差异。然后，我们提出了HCD的回归模型，该模型将源信号分解为回归信号并更改信号，并且需要回归的信号具有与同一图上的目标信号相同的光谱属性。借助图光谱分析，提出的回归模型是灵活且可扩展的。在七个真实数据集上进行的实验显示了所提出的方法的有效性。
This is the second part of the paper that provides a new strategy for the heterogeneous change detection (HCD) problem, that is, solving HCD from the perspective of graph signal processing (GSP). We construct a graph to represent the structure of each image, and treat each image as a graph signal defined on the graph. In this way, we can convert the HCD problem into a comparison of responses of signals on systems defined on the graphs. In the part I, the changes are measured by comparing the structure difference between the graphs from the vertex domain. In this part II, we analyze the GSP for HCD from the spectral domain. We first analyze the spectral properties of the different images on the same graph, and show that their spectra exhibit commonalities and dissimilarities. Specially, it is the change that leads to the dissimilarities of their spectra. Then, we propose a regression model for the HCD, which decomposes the source signal into the regressed signal and changed signal, and requires the regressed signal have the same spectral property as the target signal on the same graph. With the help of graph spectral analysis, the proposed regression model is flexible and scalable. Experiments conducted on seven real data sets show the effectiveness of the proposed method.
