Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network

Haokui Zhang, Ying Li, Yuzhu Zhang, Qiang Shen

Research output: Contribution to journalArticlepeer-review

281 Scopus citations

Abstract

In this article, a novel dual-channel convolutional neural network (DC-CNN) framework is proposed for accurate spectral-spatial classification of hyperspectral image (HSI). In this framework, one-dimensional CNN is utilized to automatically extract the hierarchical spectral features and two-dimensional CNN is applied to extract the hierarchical space-related features, and then a softmax regression classifier is used to combine the spectral and spatial features together and predict classification results eventually. To overcome the problem of the limited available training samples in HSIs, we propose a simple data augmentation method which is efficient and effective for improving HSI classification accuracy. For comparison and validation, we test the proposed method along with three other deep-learning-based HSI classification methods on two real-world HSI data sets. Experimental results demonstrate that our DC-CNN-based method outperforms the state-of-the-art methods by a considerable margin.

Original languageEnglish
Pages (from-to)438-447
Number of pages10
JournalRemote Sensing Letters
Volume8
Issue number5
DOIs
StatePublished - 4 May 2017

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