Hyperspectral image classification based on multi-layer feature extraction

Abstract

The Hyperspectral image classification is an important issue, which has been pursued in recent year. The field of application involves many aspects of life. Hyperspectral images (HSIs) exhibit a limited number of labeled high-dimensional training samples, which limits the performance of some classification methods on feature extraction or feature reduction. In the paper, we propose a supervised method based on the PCA network (PCANet) and linear SVM for HSIs classification. We used PCANet (principal component analysis network) to learn the character features. We verified the influence of these parameters on the performance of PCANet by modifying the key parameters of the experiment. We carry out extensive experiments on India pines dataset. The results demonstrate that our method significantly outperforms PCA+KNN methods . And the results not only are optimistic but also the recognition rate can reach 94.29%. At last, we compared the experimental results of the same algorithm on different data sets and so on.