加权的判别性协同表示方法用于高光谱遥感图像分类

Weighted Discriminative Collaborative Representation for Hyperspectral Remote Sensing Image Classification

针对现有协同表示算法应用在高光谱遥感图像分类过程中出现的较差分类精度的问题,提出了一种基于加权判别性协同表示的分类算法。提出的算法对高光谱图像进行基于空间邻域的平滑化处理以消除图像中的噪声和异常光谱。通过考虑训练样本和测试样本的相关性提出基于相关性的加权正则项;通过考虑不同类别训练样本重构给定测试样本的误差进一步提出了基于最小重构误差的正则项。在Indian Pines和University of Pavia两种真实数据集上的实验结果表明,所提出的算法相比其他典型的基于协同表示的分类算法具有更高的分类精度,分别能获得98.2%和95.70%的总体精度。实验证明,所提出的分类器算法有效改善了现有基于协同表示的高光谱图像分类方法的低精度问题。

To handle the problem of insufficient classification accuracy in the application of existing collaborative representation algorithms in hyperspectral remote sensing image classification,a classification algorithm based on weighted discriminative collaborative representation is proposed.The hyperspectral image is performed smooth processing based on the spatial neighborhood by the proposed algorithm to eliminate the noise and abnormal spectrum in the image.Then a weighted regularization term is proposed by considering the correlations between the training samples and the test samples.Meanwhile,a regularization term is further proposed based on the minimum reconstruction error by considering the errors of different training samples to reconstruct a given test sample.The experimental results on two real datasets,Indian Pines and University of Pavia,show that the proposed algorithm has higher classification accuracy than other typical classification algorithms based on collaborative representation,and the overall accuracy of 98.2%and 95.70%can be obtained by the proposed algorithm,respectively.The experimental results show that the proposed classifier algorithm effectively improves the accuracy of the existing hyperspectral image classification methods based on collaborative representation.