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Volume 15
Issue 4
10.3390/rs15041050
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Article

Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms

by
Hanyu Wang
1,2,3,
Mingyu Yang
1,3,
Tao Zhang
1,2,3,*,
Dapeng Tian
1,2,3,
Hao Wang
1,2,3,
Dong Yao
1,2,3,
Lingtong Meng
1,2,3 and
Honghai Shen
1,2,3
1
Key Laboratory of Airborne Optical Imaging and Measurement, Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
3
Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2023, 15(4), 1050; https://doi.org/10.3390/rs15041050
Submission received: 5 January 2023 / Revised: 3 February 2023 / Accepted: 11 February 2023 / Published: 15 February 2023
(This article belongs to the Special Issue Advances in Hyperspectral Remote Sensing Image Processing)
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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.
Keywords: anomaly detection; attribute profile; genetic algorithms (GAs); feature selection; hyperspectral imagery (HSI) anomaly detection; attribute profile; genetic algorithms (GAs); feature selection; hyperspectral imagery (HSI)

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MDPI and ACS Style

Wang, H.; Yang, M.; Zhang, T.; Tian, D.; Wang, H.; Yao, D.; Meng, L.; Shen, H. Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms. Remote Sens. 2023, 15, 1050. https://doi.org/10.3390/rs15041050

AMA Style

Wang H, Yang M, Zhang T, Tian D, Wang H, Yao D, Meng L, Shen H. Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms. Remote Sensing. 2023; 15(4):1050. https://doi.org/10.3390/rs15041050

Chicago/Turabian Style

Wang, Hanyu, Mingyu Yang, Tao Zhang, Dapeng Tian, Hao Wang, Dong Yao, Lingtong Meng, and Honghai Shen. 2023. "Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms" Remote Sensing 15, no. 4: 1050. https://doi.org/10.3390/rs15041050

APA Style

Wang, H., Yang, M., Zhang, T., Tian, D., Wang, H., Yao, D., Meng, L., & Shen, H. (2023). Hyperspectral Anomaly Detection with Differential Attribute Profiles and Genetic Algorithms. Remote Sensing, 15(4), 1050. https://doi.org/10.3390/rs15041050

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