基于图的多波段图像序列变化点在线检测
Online Graph-Based Change Point Detection in Multiband Image Sequences
摘要：
自动检测不同时刻采集的多光谱和高光谱图像之间的变化或异常是一个活跃且具有挑战性的研究课题。为了有效地执行变化点检测在多时相图像中，重要的是要设计的技术，是计算效率高的处理大型数据集，并不需要知识的性质的变化。在本文中，我们介绍了一种新的在线框架检测多时相遥感图像的变化。该算法将相邻光谱作为图中的相邻顶点，着重于同时激活对应于紧凑、连通性好且光谱均匀的图像区域的顶点组的异常。它充分受益于图形信号处理的最新进展，以利用位于不规则支撑上的数据的特性。此外，使用超像素分解算法直接从图像估计图。学习算法是可扩展的，在这个意义上，它是有效的，空间分布。实验结果验证了该方法的检测和定位性能。
英文摘要：
The automatic detection of changes or anomalies between multispectral and hyperspectral images collected at different time instants is an active and challenging research topic. To effectively perform change-point detection in multitemporal images, it is important to devise techniques that are computationally efficient for processing large datasets, and that do not require knowledge about the nature of the changes. In this paper, we introduce a novel online framework for detecting changes in multitemporal remote sensing images. Acting on neighboring spectra as adjacent vertices in a graph, this algorithm focuses on anomalies concurrently activating groups of vertices corresponding to compact, well-connected and spectrally homogeneous image regions. It fully benefits from recent advances in graph signal processing to exploit the characteristics of the data that lie on irregular supports. Moreover, the graph is estimated directly from the images using superpixel decomposition algorithms. The learning algorithm is scalable in the sense that it is efficient and spatially distributed. Experiments illustrate the detection and localization performance of the method.
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