FMRSS Net: Fast Matrix Representation-Based Spectral-Spatial Feature Learning Convolutional Neural Network for Hyperspectral Image Classification

Release time:2021-06-28

Impact Factor:1.009

DOI number:10.1155/2018/9218092

Journal:MATHEMATICAL PROBLEMS IN ENGINEERING

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.

First Author:侯斐斐(博士生)

Indexed by:Journal paper

Correspondence Author:雷文太

Discipline:Engineering

First-Level Discipline:Information and Communication Engineering

Document Type:J

Translation or Not:no

Date of Publication:2018-06-21

Included Journals:SCI、EI