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A survey: Deep learning for hyperspectral image classification with few labeled samples
- Source :
- Neurocomputing. 448:179-204
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
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.
- Subjects :
- FOS: Computer and information sciences
0209 industrial biotechnology
Source code
Computer Science - Artificial Intelligence
Computer science
Active learning (machine learning)
Computer Vision and Pattern Recognition (cs.CV)
Cognitive Neuroscience
media_common.quotation_subject
Computer Science - Computer Vision and Pattern Recognition
02 engineering and technology
Machine learning
computer.software_genre
Field (computer science)
020901 industrial engineering & automation
Artificial Intelligence
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
Hyperspectral image classification
media_common
business.industry
Deep learning
Image and Video Processing (eess.IV)
Hyperspectral imaging
Electrical Engineering and Systems Science - Image and Video Processing
Computer Science Applications
Artificial Intelligence (cs.AI)
020201 artificial intelligence & image processing
Artificial intelligence
Transfer of learning
Focus (optics)
business
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 448
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi.dedup.....cb687649484a6f0629212dd20e6e0e7f