Hyperspectral information efficiency and pattern classification

This paper explores the relationship between information efficiency and pattern classification in hyperspectral imaging systems. Hyperspectral imaging is a powerful tool for many applications, including pattern classification for scene analysis. However, hyperspectral imaging can generate data at rates that challenge communication, processing, and storage capacities. System designs with fewer spectral bands have lower data overhead, but also may have reduced performance, including diminished capability to classify spectral patterns. This paper presents an analytic approach for assessing the capacity of a hyperspectral system for gathering information related to classification and the system's efficiency in that capacity. Our earlier work developed approaches for analyzing information capacity and efficiency in hyperspectral systems with either uniform or non-uniform spectral-band widths. This paper presents a model-based approach for relating information capacity and efficiency to pattern classification in hyperspectral imaging. The analysis uses a model of the scene signal for different classes and a model of the hyperspectral imaging process. Based on these models, the analysis quantifies information capacity and information efficiency for designs with various spectral-band widths. Example results of this analysis illustrate the relationship between information capacity, information efficiency, and classification.