A Novel Filter-Based Anomaly Detection Framework for Hyperspectral Imagery
Open accessAbstract
Hyperspectral images stand out from other remote sensing images in anomaly target detection because they contain unique distinguishing spectral information and attract great interest in applications of search and rescue. However, most of the popular techniques for hyperspectral anomaly detection tasks focus on improving accuracy with complicated algorithms and face difficulty in efficiently balancing performance and complexity. In this paper, we propose a novel anomaly detection approach using a selected band image extracted from the band selection model combined with an image filter. Singular value decomposition (SVD) is adopted for spectral dimensionality reduction. A dual-window guided filter is constructed to highlight the potential anomaly targets. To quickly calculate the abnormity degree, we design an efficient diagonal matrix operation to achieve the energy of each pixel, and a spatial regulation model is designed to enhance the subpixel target detection performance. Extensive experiments conducted on two real-world hyperspectral datasets demonstrate that, compared with the existing relevant state-of-the-art approaches, the proposed method requires less detection time and achieves higher detection accuracy.
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Self-supervised learning with only positive pairs has resulted in competitive performance in comparison to contrastive learning which requires both negative pairs and positive pairs.
“Self-supervised learning with only positive pairs [14], [16] has resulted in competitive performance in comparison to contrastive learning which requires both negative pairs and positive pairs [13], [17].”
From Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation (2021) · cites this paper as [16]
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Cited by
Sorted by citations
Self-Supervised Learning for Anomaly Detection With Dynamic Local Augmentation
Yoa, S., Lee, S., Kim, C., & Kim, H. J. (2021) · IEEE Access · doi:10.1109/access.2021.3124525 · Cited by 38 · see citing works
- Authors
- Wang, W., Song, W., Li, Z., Zhao, B., & Zhao, B.
- Affiliations
- Peking University (CN)
- Beijing Institute of Graphic Communication (CN)
- Chinese Academy of Sciences (CN)
- Beijing Institute of Technology (CN)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 124033-124043
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3196454609
- Cited by
- 7
- References
- 31
- Citations by year
- 2025: 1 · 2024: 1 · 2023: 4 · 2021: 1
- FWCI
- 0.67
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Science Foundation for Young Scientists of China (62001455)
- Hundred Leading Talent Project of Beijing Science and Technology (Z141101001514005)
- Topics
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Physical Sciences › Engineering › Aerospace Engineering › Infrared Target Detection Methodologies
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Keywords
- hyperspectral anomaly detection, band selection, singular value decomposition, spectral dimensionality reduction, guided filter, subpixel target detection, search and rescue
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