Affiliations: [a]
School of Computer, National University of Defense Technology, Changsha, China
| [b]
Electronic Engineering College, Naval University of Engineering, Wuhan, China
Correspondence:
[*]
Corresponding author. Qi Lv, School of Computer, National University of Defense Technology, Changsha, China.
Tel.: +86 0731-84574617; E-mail: [email protected].
Abstract: This paper proposes a classification approach for hyperspectral image using the local receptive fields based random weights networks (RWN). Considering the local correlations of spectral features, it is promising to improve the performance of hyperspectral image (HSI) classification by introducing the local receptive fields (LRF). It is the first time to apply such LRF-based RWN structure to HSI classification. The proposed classification framework consists of four layers, i.e., input layer, convolution layer, pooling layer, and output layer. The convolution and pooling layer are used for feature extracting and the last layer is used as the classifier. Experimental results on two real hyperspectral image datasets have confirmed the effectiveness of the proposed HSI classification method.
Keywords: Hyperspectral image classification, random weights networks, local receptive field
