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Spectra-Based Selective Searching for Hyperspectral Anomaly Detection
C. S. Yin; C. S. Han; X. C. Xue and L. Huang
2021
发表期刊Applied Sciences-Basel
卷号11期号:1页码:20
摘要The research on hyperspectral anomaly detection algorithms has become a hotspot, driven by a lot of practical applications, such as mineral exploration, environmental monitoring and the national defense force. However, most existing hyperspectral anomaly detectors are designed with a single pixel as unit, which may not make full use of the spatial and spectral information in the hyperspectral image to detect anomalies. In this paper, to fully combine and utilize the spatial and spectral information of hyperspectral images, we propose a novel spectral-based selective searching method for hyperspectral anomaly detection, which firstly combines adjacent pixels with the same spectral characteristics into regions with adaptive shape and size and then treats those regions as one processing unit. Then, by fusing adjacent regions with similar spectral characteristics, the anomaly can be successfully distinguished from background. Two standard hyperspectral datasets are introduced to verify the feasibility and effectiveness of the proposed method. The detection performance is depicted by intuitive detection images, receiver operating characteristic curves and area under curve values. Comparing the results of the proposed method with five popular and state-of-the-art methods proves that the spectral-based selective searching method is an accurate and effective method to detect anomalies.
DOI10.3390/app11010175
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文献类型期刊论文
条目标识符http://ir.ciomp.ac.cn/handle/181722/65646
专题中国科学院长春光学精密机械与物理研究所
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C. S. Yin,C. S. Han,X. C. Xue and L. Huang. Spectra-Based Selective Searching for Hyperspectral Anomaly Detection[J]. Applied Sciences-Basel,2021,11(1):20.
APA C. S. Yin,C. S. Han,&X. C. Xue and L. Huang.(2021).Spectra-Based Selective Searching for Hyperspectral Anomaly Detection.Applied Sciences-Basel,11(1),20.
MLA C. S. Yin,et al."Spectra-Based Selective Searching for Hyperspectral Anomaly Detection".Applied Sciences-Basel 11.1(2021):20.
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