基于3D多尺度特征融合残差网络的高光谱图像分类

doi:10.16451/j.cnki.issn1003-6059.201910002

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Abstract Hyperspectral image(HSI) data used for training in deep learning are insufficient, and therefore deeper network is unfavorable for spectral-spatial feature extraction. To solve this problem, a 3D multi-scale feature fusion residual network is proposed. Spectral-spatial features are learned by deep learning and multi-scale feature fusion. Firstly, the dimension of 3D-HSI data is adaptively reduced, and the images after dimensionality reduction are used as the input of the network. Secondly, spectral-spatial features are extracted successively through multi-scale feature fusion residual blocks and features of different scales are fused. The information flow is enhanced through sharing features and richer features are obtained. Finally, the network is trained end-to-end and tested on corresponding datasets. Experimental results show the satisfactory classification performance of the proposed network.

Key words： Deep Learning Multi-scale Feature Fusion Feature Extraction Hyperspectral Image Classification

Received: 20 May 2019

ZTFLH:	TN 911.71
	TP 183

Fund:Supported by National Natural Science Foundation of China(No.61672477)

Corresponding Authors: CAO Feilong, Ph.D., professor. His research interests include deep learning and image processing.

About author:: UO Wenhui, master student. Her research interests include deep learning and image processing.

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GUO Wenhui,CAO Feilong. Hyperspectral Image Classification Based on 3D Multi-scale Feature Fusion Residual Network[J]. , 2019, 32(10): 882-891.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201910002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2019/V32/I10/882

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