MATEC Web Conf.
Volume 246, 20182018 International Symposium on Water System Operations (ISWSO 2018)
|Number of page(s)||5|
|Section||Parallel Session II: Water System Technology|
|Published online||07 December 2018|
Hyperspectral image classification based on multi-layer feature extraction
College of Computer Science & Engineering, Northwest Normal University, 730070 Lanzhou, China
a Corresponding author: firstname.lastname@example.org
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.
© The Authors, published by EDP Sciences, 2018
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.