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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing. >A Time-Efficient Method for Anomaly Detection in Hyperspectral Images
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A Time-Efficient Method for Anomaly Detection in Hyperspectral Images

机译：一种高效的高光谱图像异常检测方法

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摘要

We propose a computationally efficient method for determining anomalies in hyperspectral data. In the first stage of the algorithm, the background classes, which are the dominant classes in the image, are found. The method consists of robust clustering of a randomly chosen small percentage of the image pixels. The clusters are the representatives of the background classes. By using a subset of the pixels instead of the whole image, the computation is sped up, and the probability of including outliers in the background model is reduced. Anomalous pixels are the pixels with spectra that have large relative distances from the cluster centers. Several clustering techniques are investigated, and experimental results using realistic hyperspectral data are presented. A self-organizing map clustered using the local minima of the U-matrix (unified distance matrix) is identified as the most reliable method for background class extraction. The proposed algorithm for anomaly detection is evaluated using realistic hyperspectral data, is compared with a state-of-the-art anomaly detection algorithm, and is shown to perform significantly better.
机译：我们提出了一种用于确定高光谱数据异常的计算有效方法。在算法的第一阶段，找到背景类别，它们是图像中的主要类别。该方法包括对随机选择的小百分比图像像素进行鲁棒聚类。群集是背景类的代表。通过使用像素的子集而不是整个图像，可以加快计算速度，并减少在背景模型中包含异常值的可能性。异常像素是具有与聚类中心的相对距离较大的光谱的像素。研究了几种聚类技术，并给出了使用实际高光谱数据的实验结果。使用U矩阵（统一距离矩阵）的局部最小值进行聚类的自组织图被确定为背景类提取的最可靠方法。拟议的异常检测算法使用现实的高光谱数据进行评估，并与最新的异常检测算法进行比较，并显示出明显更好的性能。

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