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Type: Journal article
Title: Hyperspectral classification based on lightweight 3-D-CNN with transfer learning
Author: Zhang, H.
Li, Y.
Jiang, Y.
Wang, P.
Shen, Q.
Shen, C.
Citation: IEEE Transactions on Geoscience and Remote Sensing, 2019; 57(8):5813-5828
Publisher: IEEE
Issue Date: 2019
ISSN: 0196-2892
Statement of
Haokui Zhang, Ying Li, Yenan Jiang, Peng Wang, Qiang Shen, and Chunhua Shen
Abstract: Recently, hyperspectral image (HSI) classification approaches based on deep learning (DL) models have been proposed and shown promising performance. However, because of very limited available training samples and massive model parameters, DL methods may suffer from overfitting. In this paper, we propose an end-to-end 3-D lightweight convolutional neural network (CNN) (abbreviated as 3-D-LWNet) for limited samples-based HSI classification. Compared with conventional 3-D-CNN models, the proposed 3-D-LWNet has a deeper network structure, less parameters, and lower computation cost, resulting in better classification performance. To further alleviate the small sample problem, we also propose two transfer learning strategies: 1) cross-sensor strategy, in which we pretrain a 3-D model in the source HSI data sets containing a greater number of labeled samples and then transfer it to the target HSI data sets and 2) cross-modal strategy, in which we pretrain a 3-D model in the 2-D RGB image data sets containing a large number of samples and then transfer it to the target HSI data sets. In contrast to previous approaches, we do not impose restrictions over the source data sets, in which they do not have to be collected by the same sensors as the target data sets. Experiments on three public HSI data sets captured by different sensors demonstrate that our model achieves competitive performance for HSI classification compared to several state-of-the-art methods.
Keywords: 3-D lightweight convolutional network (3-D-LWNet); deep learning (DL); hyperspectral classification; transfer learning
Rights: © 2019 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See for more information.
DOI: 10.1109/TGRS.2019.2902568
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