How to effectively utilize information to design hyperspectral target detection and classification algorithms

Abstract

Hyperspectral imagery offers a means of uncovering enormous spectral information that can be used for various applications in data exploitation. How effectively such information is used affects the way image analysis algorithms are designed. In this paper, we take up this issue and focus on algorithms designed and developed for target detection and classification in hyperspectral imagery. In order to effectively characterize the information available before and after the data are processed, the a priori information and a posteriori information are used in accordance with how the information is obtained. A piece of information is referred to as a priori information if it is provided by known knowledge before data are processed. On the other hand, a piece of information is referred to as a posteriori information if it is unknown a priori, but can be obtained directly from the data in an unsupervised fashion during the course of data processing. Since a priori information is known beforehand, it can be further decomposed into two types of information, desired and undesired a priori information. The desired a priori information is the knowledge that will assist, improve and enhance data analysis, whereas the undesired a priori information is the knowledge that hinders, interferes or destructs analysis during data processing. This paper investigates how these three types of information play their roles in design and development of several hyperspectral target detection and classification algorithms. Experiments are also conducted to validate their utility.