邻域谱概率协同表示的高光谱图像分类方法

为了提高高光谱遥感图像的分类精度,通过结合像元邻域谱与概率协同表示方法,提出了一种基于空间信息与光谱信息的分类方法.首先采用插值方法生成像元的邻域谱,然后用概率协同表示方法将待测样本进行分类.用所提出的方法在AVIRIS Indian Pines和Salinas scene高光谱遥感数据库上进行分类实验,并和主成分分析、支持向量机、稀疏表示分类器和协同表示分类器方法进行了比较.结果表明,所提出的方法在AVIRIS Indian Pines数据库上识别精度比主成分分析法高约17%,其识别精度和kappa系数都优于另外4种方法.该方法是一种较好的高光谱遥感图像分类方法.

Hyperspectral image classification method based on neighborhood spectra and probability cooperative representation

In order to improve classification accuracy of hyperspectral remote sensing images, a classification method based on spatial information and spectral information was proposed by combining pixel neighborhood spectrum with probability co-representation method. Firstly, the neighborhood spectrum of pixels was generated by interpolation method. Then, the probability cooperative representation method was used to classify the samples to be tested. By using the proposed method, classification experiments were carried out on AVIRIS Indian Pines and Salinas scene hyperspectral remote sensing databases, compared with principal component analysis, support vector machine, sparse representation classifier and cooperative representation classifier. The results show that, the recognition accuracy of the proposed method on AVIRIS Indian Pines database is about 17% higher than that of the principal component analysis method. Its recognition accuracy and kappa coefficient are better than those of the other four methods. This method is a good classification method for hyperspectral remote sensing images.