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4 May 2006 Haralick texture features expanded into the spectral domain
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Abstract
Robert M. Haralick, et. al., described a technique for computing texture features based on gray-level spatial dependencies using a Gray Level Co-occurrence Matrix (GLCM). The traditional GLCM process quantizes a gray-scale image into a small number of discrete gray-level bins. The number and arrangement of spatially co-occurring gray-levels in an image is then statistically analyzed. The output of the traditional GLCM process is a gray-scale image with values corresponding to the intensity of the statistical measure. A method to calculate Spectral Texture is modeled on Haralick's texture features. This Spectral Texture Method uses spectral-similarity spatial dependencies (rather than gray-level spatial dependencies). In the Spectral Texture Method, a spectral image is quantized based on discrete spectral angle ranges. Each pixel in the image is compared to an exemplar spectrum, and a quantized image is created in which pixel values correspond to a spectral similarity value. Statistics are calculated on spatially co-occurring spectral-similarity values. Comparisons between Haralick Texture Features and the Spectral Texture Method results are made, and possible uses of Spectral Texture features are discussed.
© (2006) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Angela M. Puetz and R. C. Olsen "Haralick texture features expanded into the spectral domain", Proc. SPIE 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 623311 (4 May 2006); https://doi.org/10.1117/12.665699
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