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Article

Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection

1
School of Computer Science, Northwestern Polytechnical University, Xi’an 710129, China
2
Department of Electronics and Informatics, Vrije Universiteit Brussel, Brussel 1050, Belgium
*
Author to whom correspondence should be addressed.
Received: 24 January 2018 / Revised: 21 March 2018 / Accepted: 3 April 2018 / Published: 8 April 2018
(This article belongs to the Section Remote Sensing Image Processing)
This paper studies the classification problem of hyperspectral image (HSI). Inspired by the great success of deep neural networks in Artificial Intelligence (AI), researchers have proposed different deep learning based algorithms to improve the performance of hyperspectral classification. However, deep learning based algorithms always require a large-scale annotated dataset to provide sufficient training. To address this problem, we propose a semi-supervised deep learning framework based on the residual networks (ResNets), which use very limited labeled data supplemented by abundant unlabeled data. The core of our framework is a novel dual-strategy sample selection co-training algorithm, which can successfully guide ResNets to learn from the unlabeled data by making full use of the complementary cues of the spectral and spatial features in HSI classification. Experiments on the benchmark HSI dataset and real HSI dataset demonstrate that, with a small number of training data, our approach achieves competitive performance with maximum improvement of 41% (compare with traditional convolutional neural network (CNN) with 5 initial training samples per class on Indian Pines dataset) for HSI classification as compared with the results from those state-of-the-art supervised and semi-supervised methods. View Full-Text
Keywords: hyperspectral image classification; deep learning; residual networks; co-training; sample selection hyperspectral image classification; deep learning; residual networks; co-training; sample selection
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MDPI and ACS Style

Fang, B.; Li, Y.; Zhang, H.; Chan, J.C.-W. Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection. Remote Sens. 2018, 10, 574. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10040574

AMA Style

Fang B, Li Y, Zhang H, Chan JC-W. Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection. Remote Sensing. 2018; 10(4):574. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10040574

Chicago/Turabian Style

Fang, Bei, Ying Li, Haokui Zhang, and Jonathan C.-W. Chan 2018. "Semi-Supervised Deep Learning Classification for Hyperspectral Image Based on Dual-Strategy Sample Selection" Remote Sensing 10, no. 4: 574. https://0-doi-org.brum.beds.ac.uk/10.3390/rs10040574

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