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Performance Analysis for RX Algorithm in Hyperspectral Remote Sensing Images

机译：高光谱遥感影像中RX算法的性能分析

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Anomaly detection for remote sensing has been intensely investigated in recent years. It is not an easy task since an anomaly has distinct unknown spectral features from its neighborhood, and it usually has small size with only a few pixels. Several methods are devoted to this problem, such as the well-known RX algorithm which takes advantage of the second-order statistics. The RX algorithm assumes Gaussian noise and uses sample covariance matrix for data whitening. However, when the anomalies pixel number exceeds certain percentage or the data is ill distributed, the sample covariance matrix can not represent the background distribution. In this case, the RX algorithm will not perform well. In this paper, we perform a computer simulation to analyze the performance of the RX algorithm under different circumstances, including the number of anomaly pixels, number of anomaly types, the distance of anomaly spectrum from background, the noise distribution, etc. Later we used AVIRIS data and utilized the characteristic of principle component analysis to estimate the covariance matrix and mean of the pixels of the background. We will analyze the performance of the RX algorithm by using the estimated covariance matrix with the original version.
机译：近年来，对遥感异常检测进行了深入研究。这不是一件容易的事，因为异常具有与其邻域不同的未知光谱特征，并且通常尺寸很小，只有几个像素。有几种方法专门用于解决此问题，例如利用二阶统计信息的著名RX算法。 RX算法假设高斯噪声，并使用样本协方差矩阵进行数据白化。但是，当异常像素数超过一定百分比或数据分布不均时，样本协方差矩阵将无法代表背景分布。在这种情况下，RX算法将无法正常运行。在本文中，我们进行了计算机仿真，以分析RX算法在不同情况下的性能，包括异常像素的数量，异常类型的数量，异常光谱与背景的距离，噪声分布等。 AVIRIS数据并利用主成分分析的特征来估计背景像素的协方差矩阵和均值。我们将使用估计的协方差矩阵和原始版本来分析RX算法的性能。

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