FMRSS Net: Fast Matrix Representation-Based Spectral-Spatial Feature Learning Convolutional Neural Network for Hyperspectral Image Classification. (21st June 2018)

• Record Type: Journal Article
• Authors: Hou, Feifei; Lei, Wentai; Li, Hong; Xi, Jingchun
• Other Names: Addesso Paolo Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2018(2018)
• Publication Date: 2018-06-21
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2018/9218092
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Convolutional Neural Network- (CNN-) based land cover classification algorithms have recently been applied in hyperspectral images (HSI) field. However, the large-scale training parameters bring huge computation burden to CNN and the spatial variability of spectral signatures leads to relative low classification accuracy. In this paper, we propose a CNN-based classification framework that extracts square matrix representation-based spectral-spatial features and performs land cover classification. Numerical results on popular datasets show that our framework outperforms sparsity-based approaches like basic thresholding classifier-weighted least squares (BTC-WLS) and other deep learning-based methods in terms of both classification accuracy and computational cost.