Nonlocal and Local Feature-Coupled Self-Supervised Network for Hyperspectral Anomaly Detection
Chinese Academy of Sciences · Aerospace Information Research Institute · +4 more institutions
Abstract
Hyperspectral anomaly detection (HAD) aims to locate targets deviating from the background distribution in hyperspectral images (HSIs) without requiring prior knowledge. Most current deep learning-based HAD methods struggle to effectively distinguish anomalies due to limited utilization of supervision information and intrinsic nonlocal self-similarity in HSIs. To this end, this article proposes a novel nonlocal and local feature-coupled self-supervised network (NL2Net) tailored for HAD. NL2Net employs a dual-branch architecture that integrates both local and nonlocal feature extraction. The local feature extraction branch (LFEB) leverages centrally masked and dilated convolutions to extract local…
Citation impact
- FWCI
- 39.42
- Percentile
- 100%
- References
- 52
Authors
5- DWDegang WangCorresponding
Chinese Academy of Sciences, Aerospace Information Research Institute
- LRLongfei Ren
Chinese Academy of Sciences, Aerospace Information Research Institute
- XSXu Sun
Chinese Academy of Sciences, Aerospace Information Research Institute
- LGLianru Gao
Chinese Academy of Sciences, Aerospace Information Research Institute
- JCJocelyn Chanussot
Institut polytechnique de Grenoble, Centre National de la Recherche Scientifique, GIPSA-Lab, Université Grenoble Alpes
Topics & keywords
- Hyperspectral imaging
- Anomaly detection
- Pattern recognition (psychology)
- Computer science
- Artificial intelligence
- Feature (linguistics)
- Anomaly (physics)
- Feature extraction