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Fully Dense Multiscale Fusion Network for Hyperspectral Image Classification

Authors :
Zhe Meng
Lingling Li
Licheng Jiao
Zhixi Feng
Xu Tang
Miaomiao Liang
Source :
Remote Sensing, Vol 11, Iss 22, p 2718 (2019)
Publication Year :
2019
Publisher :
MDPI AG, 2019.

Abstract

The convolutional neural network (CNN) can automatically extract hierarchical feature representations from raw data and has recently achieved great success in the classification of hyperspectral images (HSIs). However, most CNN based methods used in HSI classification neglect adequately utilizing the strong complementary yet correlated information from each convolutional layer and only employ the last convolutional layer features for classification. In this paper, we propose a novel fully dense multiscale fusion network (FDMFN) that takes full advantage of the hierarchical features from all the convolutional layers for HSI classification. In the proposed network, shortcut connections are introduced between any two layers in a feed-forward manner, enabling features learned by each layer to be accessed by all subsequent layers. This fully dense connectivity pattern achieves comprehensive feature reuse and enforces discriminative feature learning. In addition, various spectral-spatial features with multiple scales from all convolutional layers are fused to extract more discriminative features for HSI classification. Experimental results on three widely used hyperspectral scenes demonstrate that the proposed FDMFN can achieve better classification performance in comparison with several state-of-the-art approaches.

Details

Language :
English
ISSN :
20724292
Volume :
11
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Remote Sensing
Publication Type :
Academic Journal
Accession number :
edsdoj.3f451b2f6f34cdb954b118d9cc23b5a
Document Type :
article
Full Text :
https://doi.org/10.3390/rs11222718