Information Theoretic Characterizations of Coded Imaging-based Space Object Identification
Abstract
Obtaining information about the surface material composition of man-made objects such as earth orbiting satellites is an important goal of space object identification (SOI). Often initial information about the possible material composition of such targets is known a priori, at least in a statistical sense, in the form of a material database which includes the measured reflectance curves of materials most often used in construction of satellites. In fact, such a database serves as an appropriate sparse basis for modeling the target using a coded spectral imaging system. A coded imaging system exploits the fact that images of man-made objects posses correlations in their physical structure. These correlations, which can occur spatially as well as spectrally, can suggest a more natural sparse basis for compressing and representing the scene when compared to standard pixels or voxels. A coded imaging system attempts to acquire and encode the scene in this sparse-basis, while preserving all relevant information in the scene. Due to strong a priori statistical information in the form of the material database and knowledge of their standard morphology, statistical information provides a natural theoretical framework for assessing the content, acquisition, and processing of information by a coded imaging system about man-made space objects. Here, we will demonstrate the use of a statistical model of a coded imaging system for which we compute statistical information in the coded measurements using a highly efficient Monte-Carlo based algorithm. Specifically, we will study the problem of encoding spatial information about the scene at different wavelengths into the measurements.
Document Details
- Document Type
- Technical Report
- Publication Date
- Sep 01, 2010
- Accession Number
- ADA531685
Entities
People
- D. Hope
- Sudhakar Prasad
Organizations
- University of New Mexico