联合核稀疏多元逻辑回归和TV-L1错误剔除的高光谱图像分类算法

Hyperspectral Image Classification Combining Kernel Sparse Multinomial Logistic Regression and TV-L1 Error Rejection

稀疏多元逻辑回归(SMLR)是高光谱监督分类中的重要方法,然而仅仅利用光谱信息的SMLR忽略了影像本身的空间特征,在少量监督样本下的分类精度和算法的鲁棒性仍明显不足;虽然通过引入核技巧,核稀疏多元逻辑回归(KSMLR)可以部分克服上述缺点,其分类错误仍然有待进一步降低.本文基于核稀疏多元逻辑回归分类误差的统计建模分析,提出一种联合核稀疏多元逻辑回归和正则化错误剔除的高光谱图像分类模型.提出的模型通过引入隐概率场,采取L1范数度量KSMLR分类误差的重尾特性建立数据保真项;利用全变差(Total Variation,TV)正则化度量隐概率场的局部空间光滑性.由Indian Pines和University of Pavia数据集等实测数据应用表明,该方法可以得到更鲁棒和更高的分类精度.

Sparse multinomial logistic regression（ SMLR） is an important supervised classification method for hyperspectral images（ HSI）. However,because the traditional SMLR based pixel-wise classifiers only use the spectral signatures,the good robustness and high classification accuracy are hardly achieved with a small number of samples without considering the spatial information of HSI. By using the kernel tricks, the kernel sparse multinomial logistic regression（ KSMLR） method can partly overcome this limitation,however the resulted misclassification errors are still expected to be further reduced. According to the statistical analysis of classification errors resulted in KSMLR,we propose a novel two stage framework which combines KSMLR and error rejection for HSI classification. The proposed model,named KSMRL-TVL1, adopts the L1 norm to measure the heavy-tailed property of the classification errors so as to build the data fidelity term, and uses the total variation（TV） regularization term to measure the local spatial smoothness of the hidden probability field. The experiments on Indian Pines dataset and University of Pavia dataset show that the proposed method can better improve the robustness and classification accuracy.