Advanced Algorithms for Exploitation of Space-Based Imagery to Detect Targets against Structured Earth Backgrounds

Signature-based hyperspectral target-detection algorithms have demonstrated encouraging performance over a range of imagery. These algorithms require as input a spectral signature for the target material of interest. Signature-based detection algorithms are often demonstrated using a carefully measured laboratory spectrum for an ideal sample of a target material. In many situations, however, a laboratory spectrum will not be available for a target material. In some cases, we may have a remotely sensed radiance spectrum obtained by an image analyst for a target or multiple field measurements from different sources. In the most challenging cases, the remotely sensed radiance spectrum may be for a subpixel or concealed target. We have shown that the performance of signature-based detection algorithms has a significant dependence on the input target spectral model. Thus, the generation of optimized target models from different input sources is a critical issue for the success of target detection systems in operational environments. In this project, we will develop an innovative software product called MODGEN that produces optimized target models using input laboratory, field, and remote spectral measurements. This product can be used in combination with signature-based detection algorithms to provide a system that can be used operationally for model selection and target detection. MODGEN will consider both the visible through short-wave infrared (VNIR/SWIR) spectral range and the long-wave infrared (LWIR) spectral range. We will assess MODGEN by considering several VNIR/SWIR and LWIR hyperspectral datasets.