Salem M. Salem,Carl E. Halford The Univ. of Memphis (United States) Steve K. Moyer U.S. Army Night Vision & Electronic Sensors Directorate (United States) Matthew Gundy EOIR Technologies, Inc. (United States)
A new approach to linear discriminant analysis (LDA), called orthogonal rotational LDA (ORLDA) is presented. Using ORLDA and properly accounting for target size allowed development of a new clutter metric that is based on the Laplacian pyramid (LP) decomposition of clutter images. The new metric achieves correlation exceeding 98% with expert human labeling of clutter levels in a set of 244 infrared images. Our clutter metric is based on the set of weights for the LP levels that best classify images into clutter levels as manually classified by an expert human observer. LDA is applied as a preprocessing step to classification. LDA suffers from a few limitations in this application. Therefore, we propose a new approach to LDA, called ORLDA, using orthonormal geometric rotations. Each rotation brings the LP feature space closer to the LDA solution while retaining orthogonality in the feature space. To understand the effects of target size on clutter, we applied ORLDA at different target sizes. The outputs are easily related because they are functions of orthogonal rotation angles. Finally, we used Bayesian decision theory to learn class boundaries for clutter levels at different target sizes.