Hyperspectral and Spectro-Polarimetric Pixel-Level Classification Using Genetic Programming

Abstract

The objective force will be relying heavily on their sensors to be a combat multiplier to help improve the force's effectiveness and survivability, particularly for reconnaissance, surveillance, and target acquisition missions. Currently, fielded passive sensor systems are generally ineffective against camouflage, concealment, and deception. Their performance is also sensitive to environmental conditions. To meet future needs, several new sensor systems are being developed and evaluated. Two of these new sensors are passive systems that collect additional, measurable characteristics of light: hyperspectral (HS) systems and spectro-polarimetric (SP) systems.To fully take advantage of the information that these systems collect requires new algorithms and techniques. This report discusses why new techniques are necessary and details the development of a computer-assisted design system for the discovery of classification algorithms via a small number of sample target and background signatures. The technique is called genetic programming (GP). GP is an adaptive learning technique that automatically generates a computer program (in this work, a mathematical equation) to solve the problem it is given. This report documents work conducted primarily between September 1999 and August 2000, while the author was on a rotation at the University of Michigan under the Federated Laboratories Consortium program. The report demonstrates that GP could be a useful technique for processing HS and SP data. The experiments reported here show that by using even the simplest of operators (addition, subtraction, multiplication and division) the GP process can develop interesting and potentially useful solution equations. The results shown here are encouraging. However, many questions remain to be answered.