Ground-based remote sensing is an important technology to gain situational awareness of the environment surrounding space assets. Ground-based optical telescopes cannot spatially resolve objects in space that are distant (orbits beyond 1,000 km altitude, e.g. GEO) or that are small (e.g. CubeSats). These objects are denoted as unresolved resident space objects (URSO). Hyperspectral remote sensing has been proposed as a technology to extract quantitative information about URSOs. The high spectral resolution of hyperspectral sensors contains information about URSO material composition. Even though the object cannot be spatially resolved, it may be spectrally resolved. Simulation models provide an alternative to the limited access to real data for algorithm testing and validation. They also provide a platform to perform “controlled” experiments to understand algorithm performance before processing real observations. Here we will present our work in combining tools such as MATLAB, STK and DIRSIG to develop simulation models of different levels of complexity to generate data sets to support remote sensing algorithm testing and validation.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.