Noise robust ICA feature extraction algorithm for hyperspectral image
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摘要: 特征提取是高光谱数据应用的一个重要环节,用于将高光谱数据中具有特殊性质的地物分离出来并去除冗余信息.提出了一种使用独立成分分析(ICA,Independent Component Analysis)进行高光谱遥感地物特征提取的方法.为了解决ICA对噪声过分敏感的问题,采用最大噪声分量(MNF,Maximum Noise Fraction)算法替代传统的主成分分析对数据作降噪处理,由MNF引出的不完全独立成分分析(UICA,Undercomplete ICA)在不牺牲特征提取能力的情况下能够获得很高的运算效率.给出了HYDICE和PHI的数据试验结果,分别测试了算法在时间效率和特征提取能力方面的性能,证明了该算法具有预期的性能.Abstract: Feature extraction is important to hyperspectral imagery processing in that it can distinguish special featured object from background clutter and remove redundant information. An ICA(independent component analysis) based on the feature extraction algorithm for hyperspectral remote sensing data is proposed. In order to handle the over-sensitivity of ICA to noise and data imperfection, the MNF(maximum noise fraction) is adopted as the replacement of conventional principal component analysis. The UICA(undercomplete ICA) led by the MNF not only raises the time efficiency, but also maintains the extracting ability of ICA. The performance of the algorithm is verified by the results of HYIDCE and PHI experiments.
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