DoD HBCU/MI - Advanced Unmixing of Hyperspectral Remote Sensing Data for Space Situational Awareness
Abstract
The United States has become more dependent economically and militarily in space assets. Orbiting satellites provide a multitude of critical services, which are critical for AmericaÕs military dominance and economic wealth. Space situational awareness (SSA) is needed to have a clear picture of the environment surrounding US space assets to detect any changes or potential threats. Radar ground assets are primarily used for observing targets in Low Earth Orbit (up to ~2000 km) while optical ground assets are used to assess the environment at higher altitudes; both of which do not routinely use imaging sensor technology. Current ground-based space telescope technology cannot spatially resolve objects in space that are distant (orbits beyond 1,000 km altitude, e.g. GEO) or that are small (e.g. nanosatellites). These are denoted as non-imaging objects (NOI). Current NOI technology only allows to qualitatively assessing the risk from objects in Earth orbit. An approach to potentially extract quantitative information about NOI is hyperspectral remote sensing. The high spectral resolution of hyperspectral sensors allows extraction information about the material composition of the NOI from their contribution to the measured spectra. Thus, hyperspectral remote sensing can provide a quantitative approach to assessing/extracting orbiting objects material information. Even though the object cannot be spatially resolved, it can be spectrally resolved. A significant aspect of hyperspectral analysis and exploitation for SSA hinges on the successful identification of all unique signatures present within a measured mixed spectral signature for the determination of the space-object composition. Static spectral libraries of representative materials of space objects may not fully cover existing materials and may not properly account for natural spectral variability due to various effects: integrated material response, space weathering effects, aspect angle dependencies, and other effects that are not well understood. These library uncertainties lead to incorrect NOI characterization. An approach that integrates spectral libraries with algorithms to mine the remote sensing spectral measurements are a promising approach to improve information extraction for SSA using hyperspectral remote sensing. This project proposes the development of data-driven adaptive algorithms applied to hyperspectral remote sensing measurements that integrate reflectance properties of known spacecraft material to disentangle mixed spectral signatures for applications towards material type determination. The data-driven approach will enable the discovery of material signatures not present in the library, and to capture the natural spectral variability of material signatures. The work includes development of models and of algorithms, and testing and validation using remote sensing data and laboratory spectra collected using imagers and spectrometers available via UTEP facilities/staffed laboratories. Remote sensing and laboratory data should result in methodologies to better characterize non-imaged space objects from their composition extracted from spectral analysis. This project will provide the opportunity to enhance UTEP resources in SSA by integrating expertise in hyperspectral remote sensing data exploitation, sensor and signal characterization, orbital debris characterization using laboratory and telescope signature analysis, and high-resolution spectroscopy. Two doctoral students will be directly supported in the effort. The proposed work addresses the Topic ÒRemote Sensing and Imaging PhysicsÓ under Physical Sciences (RTB1) Program of announcement number BAA-AFRL-AFOSR-2016-0007 from AFOSR.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 14, 2019
- Source ID
- W911NF1910011
Entities
People
- Miguel Velez-Reyes
Organizations
- Army Contracting Command
- Office of the Secretary of Defense
- University of Texas at El Paso