A Hyperspectral Image Change Detection Framework With Self-Supervised Contrastive Learning Pretrained Model Article Swipe
Xianfeng Ou
,
Liangzhen Liu
,
Shulun Tan
,
Guoyun Zhang
,
Wujing Li
,
Bing Tu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2022.3204541
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1109/jstars.2022.3204541
Hyperspectral images (HSIs) have high spatial resolution and spectral resolution, and using HSI as a change detection (CD) data source is crucial for detecting surface changes. However, there is a large amount of real noise in HSIs, and most deep-learning-based CD methods require a large number of ground-truth labels for training, which is difficult and expensive to label manually. To reduce the dependence of CD on ground-truth labels and weaken the interference of noise on CD in HSIs, in this article, we propose an HSI change detection framework with a self-supervised contrastive learning pretrained model (CDSCL). CDSCL consists of two parts: self-supervised contrastive learning pretrained model and CD classification network. The main contributions of this article are as follows: a data augmentation strategy based on Gaussian noise is proposed to improve the ability of the model to extract variation information from HSIs with different random Gaussian noises; based on the information bottleneck theory, a progressive feature extraction module is developed to remove redundant or irrelevant details in changing information spectrum; and a contrastive loss function based on the Pearson correlation coefficient and negative cosine correlation is designed to make the features extracted by the two branches of the siamese network close to each other. Experimental results on four real hyperspectral datasets demonstrate that the CD performance of CDSCL outperforms the most representative CD methods.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/jstars.2022.3204541
- https://ieeexplore.ieee.org/ielx7/4609443/4609444/09878202.pdf
- OA Status
- gold
- Cited By
- 39
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4294811351
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4294811351Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/jstars.2022.3204541Digital Object Identifier
- Title
-
A Hyperspectral Image Change Detection Framework With Self-Supervised Contrastive Learning Pretrained ModelWork title
- Type
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articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-01-01Full publication date if available
- Authors
-
Xianfeng Ou, Liangzhen Liu, Shulun Tan, Guoyun Zhang, Wujing Li, Bing TuList of authors in order
- Landing page
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https://doi.org/10.1109/jstars.2022.3204541Publisher landing page
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/09878202.pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/4609443/4609444/09878202.pdfDirect OA link when available
- Concepts
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Hyperspectral imaging, Artificial intelligence, Computer science, Pattern recognition (psychology), Noise (video), Ground truth, Feature extraction, Image (mathematics)Top concepts (fields/topics) attached by OpenAlex
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39Total citation count in OpenAlex
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2025: 8, 2024: 23, 2023: 8Per-year citation counts (last 5 years)
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59Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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