gray-level co-occurrence matrix (glcm) algorithm Search Results


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Harris Geospatial Solutions band4
Description of the input predictor variables used in this study (B1–B4 are Quickbird multispectral bands, while B5–B37 are identifications assigned to the derived spatial and spectral matrices).
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Description of the input predictor variables used in this study (B1–B4 are Quickbird multispectral bands, while B5–B37 are identifications assigned to the derived spatial and spectral matrices).

Journal: Remote sensing

Article Title: Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes

doi: 10.3390/rs10010046

Figure Lengend Snippet: Description of the input predictor variables used in this study (B1–B4 are Quickbird multispectral bands, while B5–B37 are identifications assigned to the derived spatial and spectral matrices).

Article Snippet: Texture Metrics , Texture variables (contrast (B20), correlation (B21), dissimilarity (B22), entropy (B23), homogeneity (B24), mean (B25), 2nd moment (B26), and variance (B27)) computed as a measure of Gray Level Co-occurrence Matrix (GLCM) using Band4 (Harris Geospatial Solutions, Herndon, VA, USA, version 5.3). , Source: Harris Geospatial, Texture Metrics Background. Available online: www.harrisgeospatial.com/docs/backgroundtexturemetrics.html (accessed on 26 December 2017)..

Techniques: Derivative Assay, Transformation Assay