Hyperspectral classification based on spectral–spatial convolutional neural networks. (February 2018)

• Record Type: Journal Article
• Authors: Chen, Congcong; Jiang, Feng; Yang, Chifu; Rho, Seungmin; Shen, Weizheng; Liu, Shaohui; Liu, Zhiguo
• Is Part Of: Engineering applications of artificial intelligence. Volume 68(2017:Aug.)
• Page Start: 165
• Page End: 171
• Publication Date: 2018-02
• Subjects: Hyperspectral classification -- Convolutional neural network -- Support vector machine -- Spectral–spatial convolutional neural network -- Adaptive window; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2017.10.015
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Hyperspectral image classification is an important task in remote sensing image analysis. Traditional machine learning techniques are difficult to deal with hyperspectral images directly, because hyperspectral images have too many redundant spectral channels. In this paper we propose a novel method for hyperspectral image classification, by which spectral and spatial features are jointly exploited from hyperspectral images. Firstly, considering the local similarity in spatial domain, we employ a large spatial window to get image blocks from hyperspectral image Secondly, each spectral channel of the image block is filtered to extract their spatial and spectral features, after that the features are merged by convolutional layers. Finally, the fully-connected layers are used to get the classification result. Comparing with other state-of-the-art techniques, the proposed method pays more attention to the correlation of spatial neighborhood by using a large spatial window in the network. In addition, we combine the proposed network with the traditional support vector machine (SVM) classifier to improve the performance of hyperspectral image classification. Moreover, an adaptive method of the spatial window sizes selection is proposed in this paper. Experimental results conducted on the AVIRIS and ROSIS datasets demonstrate that the proposed method outperforms the state-of-the-art techniques.