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Open AccessArticle

A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification

1
School of Computer Science, Shenyang Aerospace University, Shenyang 110136, China
2
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
3
College of Mathematics and Computer Science, Yan’an University, Yan’an 716000, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(7), 1054; https://doi.org/10.3390/rs12071054
Received: 24 February 2020 / Revised: 16 March 2020 / Accepted: 24 March 2020 / Published: 25 March 2020
(This article belongs to the Special Issue Deep Learning and Feature Mining for Hyperspectral Imagery)
Deep learning has attracted extensive attention in the field of hyperspectral images (HSIs) classification. However, supervised deep learning methods heavily rely on a large amount of label information. To address this problem, in this paper, we propose a two-stage deep domain adaptation method for hyperspectral image classification, which can minimize the data shift between two domains and learn a more discriminative deep embedding space with very few labeled target samples. A deep embedding space is first learned by minimizing the distance between the source domain and the target domain based on Maximum Mean Discrepancy (MMD) criterion. The Spatial–Spectral Siamese Network is then exploited to reduce the data shift and learn a more discriminative deep embedding space by minimizing the distance between samples from different domains but the same class label and maximizes the distance between samples from different domains and class labels based on pairwise loss. For the classification task, the softmax layer is replaced with a linear support vector machine, in which learning minimizes a margin-based loss instead of the cross-entropy loss. The experimental results on two sets of hyperspectral remote sensing images show that the proposed method can outperform several state-of-the-art methods. View Full-Text
Keywords: hyperspectral image classification; deep domain adaptation; Spatial–Spectral Siamese Network; MMD; convolutional neural network hyperspectral image classification; deep domain adaptation; Spatial–Spectral Siamese Network; MMD; convolutional neural network
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MDPI and ACS Style

Li, Z.; Tang, X.; Li, W.; Wang, C.; Liu, C.; He, J. A Two-stage Deep Domain Adaptation Method for Hyperspectral Image Classification. Remote Sens. 2020, 12, 1054.

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