期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 58, 期 9, 页码 5998-6007出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2019.2961703
关键词
Feature extraction; Detectors; Hyperspectral imaging; Anomaly detection; Optimization; Object detection; Anomaly detection; guided filtering; hyperspectral images (HSIs); image fusion; subpixel
类别
资金
- National Natural Science Foundation of China for International Cooperation and Exchanges [61520106001]
- Fund of Hunan Province for Science and Technology Plan Project [2017RS3024]
Most of the existing hyperspectral anomaly detectors are designed based on a single pixel-level feature. These detectors may not adequately utilize spectra spatial information in hyperspectral images (HSIs) for detecting anomalies. To overcome this problem, this article introduces a novel subpixel-pixel-superpixel guided fusion (SPSGF) method for hyperspectral anomaly detection. This approach comprises three main steps. First, subpixel-, pixel-, and superpixel-level features are extracted from an HSI by employing the spectral unmixing, morphological operation, and superpixel segmentation techniques, respectively. Then, based on the spatial consistency of three features, a guided filtering-based weight optimization technique is developed to construct weight maps for fusion. Finally, a simple yet effective decision fusion method is adopted to utilize the complemental information of three features, and then generates a fused detection result. The performance of the proposed approach is evaluated on three real-scene HSIs and one synthetic HSI. Experimental results validate the advantages of the SPSGF method.
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