We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms.
- Authors
Wang, Hanyu; Yang, Mingyu; Zhang, Tao; Tian, Dapeng; Wang, Hao; Yao, Dong; Meng, Lingtong; Shen, Honghai
- Abstract
Anomaly detection is hampered by band redundancy and the restricted reconstruction ability of spectral–spatial information in hyperspectral remote sensing. A novel hyperspectral anomaly detection method integrating differential attribute profiles and genetic algorithms (DAPGA) is proposed to sufficiently extract the spectral–spatial features and automatically optimize the selection of the optimal features. First, a band selection method with cross-subspace combination is employed to decrease the spectral dimension and choose representative bands with rich information and weak correlation. Then, the differentials of attribute profiles are calculated by four attribute types and various filter parameters for multi-scale and multi-type spectral–spatial feature decomposition. Finally, the ideal discriminative characteristics are reserved and incorporated with genetic algorithms to cluster each differential attribute profile by dissimilarity assessment. Experiments run on a variety of genuine hyperspectral datasets including airport, beach, urban, and park scenes show that the effectiveness of the proposed algorithm has great improvement with existing state-of-the-art algorithms.
- Subjects
ANOMALY detection (Computer security); GENETIC algorithms; INTRUSION detection systems (Computer security); REMOTE sensing; FEATURE selection; REDUNDANCY in engineering; ALGORITHMS
- Publication
Remote Sensing, 2023, Vol 15, Issue 4, p1050
- ISSN
2072-4292
- Publication type
Article
- DOI
10.3390/rs15041050