Efficient Hyperspectral Target Detection and Identification With Large Spectral Libraries

Abstract

Numerous hyperspectral algorithms have been developed to detect both full and subpixel solid target materials. Target signatures are obtained from spectral libraries that contain both target and nontarget materials. When the library is large and contains many potential targets, it is inefficient to run an individual detector for each material of interest. Additionally, such an approach produces numerous false alarms (i.e., multiple detections per pixel) due to spectral similarity among targets. In this article, we present an efficient approach for detecting multiple targets within large spectral libraries while mitigating false alarms. We first group spectrally similar materials within the library into a hierarchy of clusters. From each cluster containing a target material, a single detector is obtained. Each detector represents multiple library spectra, so an identification step is needed for detected pixels. Detected pixels are modeled as a mixture between their local in-scene background and candidate library spectra. Candidates are chosen from adjacent library clusters. The candidate model providing the best fit is chosen to report. Use of local background spectra provides a physically meaningful mixing model that adapts to detected pixels. Clustering the library reduces the computational complexity of modeling detected pixels. We demonstrate detection and false alarm mitigation performance of our proposed algorithm with a real hyperspectral dataset.