Paper Info
Title
A Dual-Branch Extraction and Classification Method Under Limited Samples of Hyperspectral Images Based on Deep Learning.
Abstract
The convolutional neural network (CNN) has been gradually applied to the hyperspectral images (HSIs) classification, but the lack of training samples caused by the difficulty of HSIs sample marking and ignoring of correlation between spatial and spectral information seriously restrict the HSIs classification accuracy. In an attempt to solve these problems, this paper proposes a dual-branch extraction and classification method under limited samples of hyperspectral images based on deep learning (DBECM). At first, a sample augmentation method based on local and global constraints in this model is designed to augment the limited training samples and balance the number of different class samples. Then spatial-spectral features are simultaneously extracted by the dual-branch spatial-spectral feature extraction method, which improves the utilization of HSIs data information. Finally, the extracted spatial-spectral feature fusion and classification are integrated into a unified network. The experimental results of two typical datasets show that the DBECM proposed in this paper has certain competitive advantages in classification accuracy compared with other public HSIs classification methods, especially in the Indian pines dataset. The parameters of the overall accuracy (OA), average accuracy (AA), and Kappa of the method proposed in this paper are at least 4.7%, 5.7%, and 5% higher than the existing methods.
Year
DOI
Venue
2020
10.3390/rs12030536
REMOTE SENSING
Keywords
Field
DocType
dual-branch structure,hyperspectral images (HSIs),local and global constraints,spatial-spectral feature extraction
Computer vision,Remote sensing,Hyperspectral imaging,Artificial intelligence,Deep learning,Geology
Journal
Volume
Issue
Citations 
12
3
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Bingqing Niu100.34
Jinhui Lan2216.55
Yang Shao300.34
Hui Zhang400.34