本文提出了一种高光谱图像降维的判别流形学习方法.针对获取的大量遥感对地观测数据存在大量冗余信息的特点, 引入改进的流形学习方法对高光谱遥感数据进行降维处理, 以提高遥感图像自动分类的总体准确度.该方法充分利用遥感图像自动分类中训练样本的判别信息, 将输入样本的类别信息加入到常规流形学习方法的框架中, 从本质上提高输出的特征在低维空间中的判别力.同时, 引入线性化模型以解决流形学习方法中常见的小样本问题.对高光谱遥感图像自动分类的实验表明, 基于判别流形学习的高光谱遥感图像自动分类方法能够显著地提高图像分类准确度.

A discriminant manifold learning approach for hyperspectral image dimension reduction was proposed. In order to overcome the high dimensional and high redundancy of remotely sensed earth observation images, a modified manifold learning algorithm was suggested for dataset linear dimensional reduction to improve the performance of image classification. The proposed method addressed the discriminative information of given training samples into the current manifold learning framework to learn an optimal subspace for subsequent classification, in particular, the linearization of discriminant manifold learning is introduced to deal with the out of sample problem. Experiments on hyperspectral image demonstrated that the proposed method could achieve higher classification rate than the conventional image classification technologies.