Hyperspectral Identification for Collaborative Tracking

The primary goal of this effort is to develop advanced Hyperspectral Image (HSI) data and algorithms for early detection of plant degradation due to Chemical/Biological agents. During Phase I, proof-of-concept demonstrated that HSI algorithms are capable of detecting de-greening in arabidopsis plants infused with covert de-greening circuits. In Phase II, the major objectives are: (1) Extend genetic engineering towards more operational viability, i.e., subject larger and mature plants to a wider range of chemical and biological agents, (2) Develop advanced Detection/Classification algorithms: multiple-hypothesis detect/ID for multiple plant specimens affected by different chem-bio agents, signature-based temporal change/anomaly detection, kernelization of linear algorithms to account for nonlinearities, ICA-based unmixing of HSI data, genetic algorithm for automated feature extraction, (3) HSI aided Triage resource management for distinguishing live and deceased dismounts in urban calamity region using HSI thermal-IR bands, and (4) Generation of and experimentation with synthetic remote sensing data and analytical prediction models: Incorporate healthy/de-greened plant and human skin spectral reflectance/emissivity signatures within FASSP/DIRSIG modeling environment, add atmospheric/illumination/sensor effects to generate synthetic electro-optical imagery that an airborne sensor might observe from a distance, apply appropriate detect/ID algorithms on the synthetic images, and perform model based sensitivity analysis to explore detection bounds.