Information Theoretic Characterizations of Coded Imaging-based Space Object Identification

Details Information Theoretic Characterizations of Coded Imaging-based Space Object Identification Information Theoretic Characterizations of Coded Imaging-based Space Object Identification

Sep 2010

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2010amos.confe..56h&link_type=abstract

Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, held in Wailea, Maui, Hawaii, September

Physics

Scientific paper

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 [1]. Specifically, we will study the problem of encoding spatial information about the scene at different wavelengths into the measurements. An important parameter that will concern us is the minimum number of spectral measurements required to unambiguously identify each material in the scene, given some level of noise in the data. Due to our information-based approach, we have the ability to study the effects of gradually introducing more prior information via the material database on the identification of the materials from the measurements. We expect interdependence between the choice of spatial measurement vectors and the number of spectral channels. We anticipate that our information-theoretic results will yield insight into how to optimally choose the measurement vectors. Such knowledge will be crucial when designing a system to identify materials with similar reflectance curves.

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