Please use this identifier to cite or link to this item: http://hdl.handle.net/1880/51900
Title: GLCM Texture: A Tutorial v. 3.0 March 2017
Authors: Hall-Beyer, Mryka
Keywords: remote sensing;spatial descriptors;spatial statistics;texture;GLCM;educational resource
Issue Date: Mar-2017
Publisher: Previously available at http://www.ucalgary.ca/UofC/nasdev/mhallbey/research.htm
Citation: Hall-Beyer, M., 2007. GLCM Texture: A Tutorial v. 1.0 through 2.7.
Abstract: This tutorial describes both the theory and practice of the use of Grey Level Co-occurrence Matrix (GLCM) textures as originally described by Haralick and others in 1973. It leads users through the practical construction and use of a small sample image, with the aim of deep understanding of the purpose, capabilities and limitations of this set of descriptive statistics. Explanations and examples are concentrated on use in a landscape scale and perspective for enhancing classification accuracy, particularly in the cases where spatial arrangement of tonal (spectral) variability provides independent data relevant to the class identification. Background information is provided to answer the questions arising from 15 years of use of the tutorial, and increased practical experience of the author in teaching and research. Some information is provided to make the material accessible to specialists in fields other than remote sensing, for example medical imaging and industrial quality control. However the author is not an expert in these fields and texture's use there is not covered in detail. A basic bibliography is provided for research that has promoted the field of remote sensing GLCM texture; research projects that simply make use of it are not systematically covered.
Description: Of use generally for students of intermediate or advanced undergraduate remote sensing classes, and graduate classes in remote sensing, landscape ecology, GIS and other fields using rasters as the basis for analysis. Also useful for researchers undertaking the use of texture in classification and other image analysis fields. May be of use for algorithm and app developers serving these communities.
URI: http://hdl.handle.net/1880/51900
Appears in Collections:Hall-Beyer, Mryka

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