高光谱数据非监督分类的改进独立成分分析方法

An Improved Independent Component Analysis Method for Unsupervised Classification of Hyperspectral Data

利用数据本身统计特性是实现高光谱数据非监督分类的有效方法之一。针对利用高光谱数据一阶、二阶统计量不能完全表征数据结构的问题,提出了一种基于数据高阶统计特性——峭度的改进独立成分分析方法(Improved Kurtosis-Based Independent Component Analysis,IKICA)的高光谱数据非监督分类方法,并针对利用峭度进行非高斯性度量时对噪声等敏感的问题进行了模型改进。利用同一航带的OMIS高光谱遥感数据对该算法的性能进行了评价,并分别与基于最大似然估计和基于负熵的独立成分分析(ICA)方法进行了性能比较。将该方法应用于PHI获取的方麓茶场航空高光谱数据的非监督分类,结果表明,本文提出的算法明显地提高了运算的收敛速度和鲁棒性,并具有较高的分类精度和较强的抗噪声能力。

To solve the problem that the first-order and second-order statistics may be inadequate for obtaining a complete representation of the data, a high-order statistics - based method, kurtosis-based independent component analysis （ KICA）, is introduced to implement unsupervised classification of hyperspectral data. Aimed at the purpose that kurtosis can be very sensitive to outliers such as noise, the improved KICA （IKICA） model is proposed in the work when kurtosis is used as optimization criterion for the ICA problem. To evaluate the performance of the proposed algorithm and its application capability in unsupervised classification, IKICA is compared with maximum likelihood-based ICA and negentropy-based ICA, and the synthesized and real hyperspectral data acquired by Object Modularization Imaging Spectrometer （OMIS） and Pushbroom Hyperspectral Imager （PHI） are used. The results show that convergence speed and robustness are enhanced obviously and anti-noise capability is improved in the authors＇ work. The application result has high precision of classification