Article
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Preserved in Portico This version is not peer-reviewed
Spectral-Spatial Feature Fusion for Hyperspectral Anomaly Detection
Version 1
: Received: 30 December 2023 / Approved: 31 December 2023 / Online: 3 January 2024 (08:11:55 CET)
A peer-reviewed article of this Preprint also exists.
Liu, S.; Li, Z.; Wang, G.; Qiu, X.; Liu, T.; Cao, J.; Zhang, D. Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection. Sensors 2024, 24, 1652. Liu, S.; Li, Z.; Wang, G.; Qiu, X.; Liu, T.; Cao, J.; Zhang, D. Spectral–Spatial Feature Fusion for Hyperspectral Anomaly Detection. Sensors 2024, 24, 1652.
Abstract
Hyperspectral anomaly detection is to recognize anomalies from complex scene in an unsupervised way. Currently, many spectral-spatial detection methods have been proposed with a cascaded manner. However, they often neglect complementary characteristics between spectral and spatial dimensions, which easily leads to yield high false alarm rate. To alleviate this issue, a spectral-spatial information fusion (SSIF) method is designed for hyperspectral anomaly detection. First, an isolation forest is exploited to obtain spectral anomaly map, in which the object-level feature is constructed with entropy rate segmentation algorithm. Then, a local spatial saliency detection scheme is proposed to produce spatial anomaly result. Finally, the spectral and spatial anomaly scores are integrated together followed by a domain transform recursive filtering to generate the final detection result. Experiments on five hyperspectral datasets prove that the proposed SSIF produces superior detection result over other state-of-the-art detection techniques.
Keywords
Hyperspectral image; isolation forest; local saliency detection; anomaly detection; spectralspatial fusion
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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