Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales

• Published in: International journal of remote sensing Vol. 38; no. 5; pp. 1312 - 1338
• Main Author: Hall-Beyer, Mryka
• Format: Journal Article
• Language: English
• Published: London Taylor & Francis 04.03.2017; Taylor & Francis Ltd
• Subjects: Agricultural land; Balances (scales); Classification; Correlation; data collection; Dunes; Entropy; Glaciers; Guidelines; ice; Image analysis; Land cover; Landsat; Landsat satellites; landscapes; latitude; Natural vegetation; Pixels; principal component analysis; Principal components analysis; Remote sensing; Robustness; Rock glaciers; Rocks; Satellite imagery; Sea ice; spectral analysis; Texture; Variance; Variance analysis; Vegetation
• ISSN: 0143-1161; 1366-5901; 1366-5901
• DOI: 10.1080/01431161.2016.1278314

Texture measurements quantitatively describe relationships of DN values of neighbouring pixels. The output is a continuous measure of spatial information that may be used for further processing. Spatial relationships are not necessarily correlated with spectral data for a given class, and including a measure of them improves classification accuracy. This research develops a guideline for choosing among the Haralick (Grey Level Co-occurrence Matrix [GLCM]) set of texture measures. These guidelines are derived using a variety of land covers and spatial scales (window sizes). Principal component analysis (PCA) of eight GLCM measures was performed for three Landsat TM and ETM+ images: a mid-latitude agricultural and natural vegetation scene, a glacier-rock-sea ice scene, and a desert scene with dunes and structurally complex rocks. PCA was performed separately for neighbourhoods consisting of squares with 25, 13, and 5 pixels on a side to demonstrate robustness to different spatial scales. PCA loadings show that contrast (Con), dissimilarity, entropy (Ent), and GLCM variance are most commonly associated with visual edges of land-cover patches; homogeneity, GLCM mean, GLCM correlation (GLCM Cor), and angular second moment are associated with patch interiors. Edge-highlighting textures account for most dataset variance but fail to differentiate among classes. Eigenchannels highlighting patch interior characteristics rely on GLCM mean and to some extent GLCM Cor. These two textures do contribute to distinguishing individual class signatures for classification purposes. Ent does not appear consistently in edge or interior groupings. Ent is interpreted as important to the textures of particular classes, but which classes is not generalized from one scene to another. Con is effective for outlining patch edges and may serve for object formation in geographic object-based image analysis (GEOBIA). For classification purposes, the proposed guideline is a choose Mean and, where a class patch is likely to contain edge-like features within it, Con. Cor is an alternative for Mean in these situations, Dis may similarly be used in place of Con. For more detailed texture study, add Ent. This guideline does not constitute a complete texture analysis but may allow confident use of GLCM texture to enhance the efficiency of Landsat-based classification.