FMRSS Net: Fast Matrix Representation-Based Spectral-Spatial Feature Learning Convolutional Neural Network for Hyperspectral Image Classification
发布时间：2021-06-28
点击次数：
影响因子：1.009
DOI码：10.1155/2018/9218092
发表刊物：MATHEMATICAL PROBLEMS IN ENGINEERING
摘要：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.
第一作者：侯斐斐(博士生)
论文类型：期刊论文
通讯作者：雷文太
学科门类：工学
一级学科：信息与通信工程
文献类型：J
是否译文：否
发表时间：2018-06-21
收录刊物：SCI、EI