Subpixel-Pixel-Superpixel Guided Fusion for Hyperspectral Anomaly Detection

Abstract

Most of the existing hyperspectral anomaly detectors are designed based on a single pixel-level feature. These detectors may not adequately utilize spectral–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.