一种改进PCA与IHS融合的高光谱图像异常检测算法

An improved anomaly detection algorithm for hyperspectral images based on PCA and IHS fusion

高光谱图像空间分辨率不足容易导致异常检测虚警率过高,针对此提出了一种新的异常检测算法。算法首先利用主成分分析PCA对低分辨率高光谱图像进行主成分提取,然后对所提取的主成分和高分辨率图像分别进行IHS变换,分别得到各自的强度分量。运用IHS变换的可逆性,将高光谱数据新的强度分量与原色度分量H和饱和度分量S进行IHS逆变换,得到空间信息增强的高光谱图像数据,最后使用改进的KwRX算法对空间信息增强的高光谱图像数据进行异常检测。仿真实验表明,与KRX算法、PCA-KRX算法相比,本算法在检测目标像素数和虚警个数上都有较大的改善,说明了本算法的的有效性和可行性。

Lack of spatial resolution in hyperspectral images can cause high false alarm rate in anomaly detection.Aiming at this problem,we present a new anomaly detection algorithm.Firstly,the main components of the low resolution hyperspectral images are extracted by the principal component analysis（PCA）method.Then the extracted principal components and high-resolution images are converted using the IHS transform,and the intensity of each component is obtained.Taking its advantage of reversibility,the IHS inverse transform is performed on the new strength of hyperspectral data components,the colors of the components H and the saturation component S,thus the hyperspectral image data with spatial information enhancement is obtained.Finally the KwRX algorithm is used for detecting the anomaly of hyperspectral images.Simulation experiments show that the proposed algorithm outperforms the KRX algorithm and the PCA-KRX algorithm,in the number of target pixels and the number of false alarms thus demonstrating its effectiveness and feasibility.