Anomaly Detection in Hyperspectral Images Based on an Adaptive Support Vector Method
Anomaly Detection in Hyperspectral Images Based on an Adaptive Support Vector Method
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DOI：
10.1109/lgrs.2010.2098842
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发表时间：
2011-07-01
影响因子：
4.8
通讯作者：
Mojaradi, Barat
中科院分区：
文献类型：
--
作者：
Khazai, Safa;Homayouni, Saeid;Mojaradi, Barat
最近，异常检测（AD）引起了各种高光谱遥感应用的极大兴趣。这种无监督目标检测技术的目标是识别与邻近背景具有显着不同光谱特征的像素。内核方法，例如基于内核的支持向量数据描述 (SVDD) (K-SVDD)，已被认为是解决 AD 问题的成功方法。最常用的核是高斯核函数。使用基于高斯核的 AD 方法的主要问题是 sigma 的最优设置。为了解决这个问题，本文提出了一种直接且自适应的高斯 K-SVDD 测量方法（GK-SVDD）。所提出的措施基于 GK-SVDD 的几何解释。在目标检测盲测数据集的真实目标和合成植入目标上给出了实验结果。与之前的测量相比，结果显示出更好的性能，特别是对于子像素异常。
Recently, anomaly detection (AD) has attracted considerable interest in a wide variety of hyperspectral remote sensing applications. The goal of this unsupervised technique of target detection is to identify the pixels with significantly different spectral signatures from the neighboring background. Kernel methods, such as kernel-based support vector data description (SVDD) (K-SVDD), have been presented as the successful approach to AD problems. The most commonly used kernel is the Gaussian kernel function. The main problem using the Gaussian kernel-based AD methods is the optimal setting of sigma. In an attempt to address this problem, this paper proposes a direct and adaptive measure for Gaussian K-SVDD (GK-SVDD). The proposed measure is based on a geometric interpretation of the GK-SVDD. Experimental results are presented on real and synthetically implanted targets of the target detection blind-test data sets. Compared to previous measures, the results demonstrate better performance, particularly for subpixel anomalies.