点击次数：

论文类型：期刊论文

发表时间：2016-10-01

发表刊物：ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING

收录刊物：SCIE、EI、Scopus

卷号：120

页面范围：99-107

ISSN号：0924-2716

关键字：Hyperspectral image; Semisupervised classification; Deep learning

摘要：Semisupervised learning is widely used in hyperspectral image classification to deal with the limited training samples, however, some more information of hyperspectral image should be further explored. In this paper, a novel semisupervised classification based on multi-decision labeling and deep feature learning is presented to exploit and utilize as much information as possible to realize the classification task. First, the proposed method takes two decisions to pre-label each unlabeled sample: local decision based on weighted neighborhood information is made by the surrounding samples, and global decision based on deep learning is performed by the most similar training samples. Then, some unlabeled ones with high confidence are selected to extent the training set. Finally, self decision, which depends on the self features exploited by deep learning, is employed on the updated training set to extract spectral-spatial features and produce classification map. Experimental results with real data indicate that it is an effective and promising semisupervised classification method for hyperspectral image. (C) 2016 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.