Binary Change Guided Hyperspectral Multiclass Change Detection Article Swipe
Meiqi Hu
,
Chen Wu
,
Bo Du
,
Liangpei Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.04493
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2112.04493
Characterized by tremendous spectral information, hyperspectral image is able to detect subtle changes and discriminate various change classes for change detection. The recent research works dominated by hyperspectral binary change detection, however, cannot provide fine change classes information. And most methods incorporating spectral unmixing for hyperspectral multiclass change detection (HMCD), yet suffer from the neglection of temporal correlation and error accumulation. In this study, we proposed an unsupervised Binary Change Guided hyperspectral multiclass change detection Network (BCG-Net) for HMCD, which aims at boosting the multiclass change detection result and unmixing result with the mature binary change detection approaches. In BCG-Net, a novel partial-siamese united-unmixing module is designed for multi-temporal spectral unmixing, and a groundbreaking temporal correlation constraint directed by the pseudo-labels of binary change detection result is developed to guide the unmixing process from the perspective of change detection, encouraging the abundance of the unchanged pixels more coherent and that of the changed pixels more accurate. Moreover, an innovative binary change detection rule is put forward to deal with the problem that traditional rule is susceptible to numerical values. The iterative optimization of the spectral unmixing process and the change detection process is proposed to eliminate the accumulated errors and bias from unmixing result to change detection result. The experimental results demonstrate that our proposed BCG-Net could achieve comparative or even outstanding performance of multiclass change detection among the state-of-the-art approaches and gain better spectral unmixing results at the same time.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2112.04493
- https://arxiv.org/pdf/2112.04493
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4200628907
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4200628907Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2112.04493Digital Object Identifier
- Title
-
Binary Change Guided Hyperspectral Multiclass Change DetectionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-12-08Full publication date if available
- Authors
-
Meiqi Hu, Chen Wu, Bo Du, Liangpei ZhangList of authors in order
- Landing page
-
https://arxiv.org/abs/2112.04493Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2112.04493Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2112.04493Direct OA link when available
- Concepts
-
Change detection, Hyperspectral imaging, Computer science, Artificial intelligence, Binary number, Pattern recognition (psychology), Pixel, Boosting (machine learning), Mathematics, ArithmeticTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
-
2023: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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