Elsevier

Neurocomputing

Volume 448, 11 August 2021, Pages 179-204
Neurocomputing

A survey: Deep learning for hyperspectral image classification with few labeled samples

https://doi.org/10.1016/j.neucom.2021.03.035Get rights and content
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Abstract

With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI classification with few labeled samples. In this article, we concentrate on this topic and provide a systematic review of the relevant literature. Specifically, the contributions of this paper are twofold. First, the research progress of related methods is categorized according to the learning paradigm, including transfer learning, active learning and few-shot learning. Second, a number of experiments with various state-of-the-art approaches has been carried out, and the results are summarized to reveal the potential research directions. More importantly, it is notable that although there is a vast gap between deep learning models (that usually need sufficient labeled samples) and the HSI scenario with few labeled samples, the issues of small-sample sets can be well characterized by fusion of deep learning methods and related techniques, such as transfer learning and a lightweight model. For reproducibility, the source codes of the methods assessed in the paper can be found at https://github.com/ShuGuoJ/HSI-Classification.git.

Keywords

Hyperspectral image classification
Deep learning
Transfer learning
Few-shot learning

Cited by (0)

Sen Jia received his B.E. and Ph.D degrees from College of Computer Science, Zhejiang University in 2002 and 2007, respectively. He is currently an Associate Professor with the College of Computer Science and Software Engineering, Shenzhen University, China. His research interests include hyperspectral image processing, signal and image processing, pattern recognition and machine learning.

Shuguo Jiang received the B.E. degree in software engineering from Xiamen University of Technology, Xiamen, China, in 2020. He is currently pursuing the master?s degree in software engineering with the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. His research interests include hyperspectral image classification, machine learning, and pattern recognition.

Zhijie Lin received the B.E. degree from the Guangzhou Medical University of Information Management and Information System, Guangzhou, China, in 2017. He is currently pursuing the master?s degree in computer technology with the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. His research interests include hyperspectral image classification, machine learning, and pattern recognition.

Nanying Li received the B.S. degree in automation from the Hunan Institute of Science and Technology, Yueyang, China, in 2017, and is currently working toward the M.Sc. degree in information and communication engineering with the same Institute. Her research interests include hyperspectral image classification and anomaly detection.

Meng Xu received the B.S. and M.E. degrees in electrical engineering from the Ocean University of China, Qingdao, China, in 2011 and 2013, respectively, and the Ph.D. degree from the University of New South Wales, Canberra, ACT, Australia, in 2017. She is currently an Associate Research Fellow with the College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China. Her research interests include cloud removal and remote sensing image processing.

Shiqi Yu is currently an associate professor in the Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China. He received his B.E. degree in computer science and engineering from the Chu Kochen Honors College, Zhejiang University in 2002, and Ph.D. degree in pattern recognition and intelligent systems from the Institute of Automation, Chinese Academy of Sciences in 2007. He worked as an assistant professor and an associate professor at Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences from 2007 to 2010, and as an associate professor at Shenzhen University from 2010 to 2019. His research interests include computer vision, pattern recognition and artificial intelligence.