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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A New Hyperspectral Anomaly Detection Method Based on Higher Order Statistics and Adaptive Cosine Estimator
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A New Hyperspectral Anomaly Detection Method Based on Higher Order Statistics and Adaptive Cosine Estimator

机译：基于高阶统计和自适应余弦估计的新高光谱异常检测方法

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摘要

Hyperspectral anomaly detection is a hot topic in remote sensing applications. Most of the conventional detectors are based on the Reed-Xiaoli (RX) method and assumedly targets and backgrounds follow a Gaussian distribution in which two problems exist: the outliers in the Gaussian distribution statistics limit the detection accuracy of RX method, and the larger proportions between the backgrounds and anomaly targets account for the higher false alarm rate. In this letter, a new hyperspectral anomaly detection method is proposed, which can solve the two problems mentioned above. The new method includes two improved ideas. First, third- and fourth-order moments are used as statistical features to improve the outlier peak values and highlight the targets. Second, the adaptive cosine estimation as the structural assumption for the RX method is used to suppress the backgrounds for anomalous targets. Experiments on real hyperspectral data sets suggest that our proposed method could not only effectively decrease the impact of background statistics but also improve the detection ability of such outlier values. Furthermore, comparative experimental results revealed that the proposed method achieves higher detection rates with lower false alarm rates.
机译：Hyperspectral异常检测是遥感应用中的热门话题。大多数传统探测器基于Reed-xiaoli（Rx）方法，并且假设目标和背景遵循高斯分布，其中存在两个问题：高斯分布统计中的异常值限制了Rx方法的检测精度，更大的比例在背景和异常目标之间占较高的误报率。在这封信中，提出了一种新的高光谱异常检测方法，可以解决上述两个问题。新方法包括两个改进的想法。首先，第三次和第四次时刻用作统计功能，以改善异常值峰值并突出显示目标。其次，使用作为RX方法的结构假设的自适应余弦估计来抑制异常目标的背景。真实高光谱数据集的实验表明，我们所提出的方法不仅可以有效地降低背景统计的影响，而且还可以提高这种异常值的检测能力。此外，比较实验结果表明，该方法具有较低误报率的检测率较高。

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