Wavelet-based fractal signature analysis for automatic target recognition

Toshiro Kubota University of South Carolina Department of Computer Science Intelligent Systems Laboratory Columbia, South Carolina 29208 Abstract. Texture measures offer a means of detecting targets in background clutter that has similar spectral characteristics. Our previous studies demonstrated that the ‘‘fractal signature’’ (a feature set based on the fractal surface area function) is very accurate and robust for grayscale texture classification. This paper introduces a new multichannel texture model that characterizes patterns as 2-D functions in a Besov space. The wavelet-based fractal signature generates an n-dimensional surface, which is used for classification. Results of some experimental studies are presented demonstrating the usefulness of this texture measure. © 1998 Society of Photo-Optical Instrumentation Engineers. [S0091-3286(98)01001-0]