On the Relationships Between Physical Phenomena, Distance Metrics, and Best Bands Selection in Hyperspectral Processing

摘要

The objective of hyperspectral processing algorithms is to efficiently capitalize on the wealth of information in the scene being imaged. Radiation collected in hundreds of contiguous electromagnetic channels and stored as data in a vector provides insight about the reflective and emissive properties of each pixel in the scene. However, it is not intuitively clear that for common applications such as estimation, classification, and detection that the best performance results from utilizing every measurement in the vector. In fact, it is quite easy to show that for some tasks, more data can degrade performance. In this paper, we explore the role of metrics and best bands algorithms in the context of maximizing the performance of hyperspectral algorithms. Specifically, we first focus on creating an intuitive framework for physical information measured by a sensor. Then, we examine how it is translated into numerical quantities by a distance metric. We discuss how two common distance metrics for hyperspectral signals, the Spectral Angle Mapper (SAM), and the Euclidean Minimum Distance (EMD), quantify the distance between two spectra. Focusing on the SAM metric, we demonstrate, in the context of target detection, how the separability of two spectra can be increased by retaining only those bands that maximize the metric. Finally, this intuition about best bands analysis for SAM is extended to the Generalized Likelihood Ratio Test (GLRT) for a practical target/background detection scenario. Results are shown for a scene imaged by the HYDICE sensor demonstrating that the separability of targets and background can be increased by carefully choosing the best bands for the test.