Intelligent multimodal and hyperspectral sensing for real-time moving target tracking

Real time moving target tracking and identification with hyperspectral imagery is still very challenging with conventional sensors and algorithms. The increased information content of hyperspectral imaging has enabled improved classification and quantification of targets of interest. However, recording hyperspectral data for target classification is very time consuming. We design a sensor platform with multi-modalities, consisting of a dual-panoramic peripheral vision system and a narrow field-of-view hyperspectral fovea. Thus, we only need to capture hyperspectal images in regions of interest. This design is inspired by the human vision system where the periphery vision of the retina is used to detect motion and the fovea of the retina is used to recognize objects. The proposed intelligent sensors design is also supported by real-time algorithms for target detection, tracking and identification. Only hyperspectral data for areas of interest are captured for target classification and recognition. Important issue such as multimodal sensing component cooperation, region of interest extraction, target tracking, hyperspectral image analyzing and target signature identification are discussed.