Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter
Fast Hyperspectral Anomaly Detection via High-Order 2-D Crossing Filter
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DOI：
10.1109/tgrs.2014.2326654
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发表时间：
2015-02-01
影响因子：
8.2
通讯作者：
Zhu, Guokang
中科院分区：
文献类型：
--
作者：
Yuan, Yuan;Wang, Qi;Zhu, Guokang
异常检测一直是高光谱图像分析中的一个重要课题。这种技术有时比监督目标检测更可取，因为它不需要感兴趣的材料的先验信息。人们在这方面做了很多努力，但是，它们通常遭受过多的时间成本和较高的假阳性率。有两个主要问题导致了这样的困境。首先，背景模型的构建和相似度估计通常过于复杂。第二，这些方法中的大多数必须对背景的光谱分布施加严格的假设，然而，这些假设并不适用于所有的实际情况。基于这种考虑，本文提出了一种新的方法，允许快速而准确的像素级高光谱异常检测。本文的主要贡献在于：1）构建了一种高阶二维交叉方法，用于快速发现光谱中快速变化的区域，无需任何先验假设; 2）设计了一种低复杂度的快速高光谱异常检测判别框架，该框架可以通过一系列线性时间开销的滤波算子实现。对包含多个像素级异常的三种不同高光谱图像的实验表明，与基准方法相比，该检测器具有优越性。
Anomaly detection has been an important topic in hyperspectral image analysis. This technique is sometimes more preferable than the supervised target detection because it requires no a priori information for the interested materials. Many efforts have been made in this topic; however, they usually suffer from excessive time cost and a high false-positive rate. There are two major problems that lead to such a predicament. First, the construction of the background model and affinity estimation are often overcomplicated. Second, most of these methods have to impose a stringent assumption on the spectrum distribution of background; however, these assumptions cannot hold for all practical situations. Based on this consideration, this paper proposes a novel method allowing for fast yet accurate pixel-level hyperspectral anomaly detection. We claim the following main contributions: 1) construct a high-order 2-D crossing approach to find the regions of rapid change in the spectrum, which runs without any a priori assumption; and 2) design a low-complexity discrimination framework for fast hyperspectral anomaly detection, which can be implemented by a series of filtering operators with linear time cost. Experiments on three different hyperspectral images containing several pixel-level anomalies demonstrate the superiority of the proposed detector compared with the benchmark methods.