Haralick Texture

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{{Content Tree|HEADER=QGIS Tutorial|NAME=QGIS tutorial}}
 
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Image texture is a quantification of the spatial variation of grey tone values. Haralick
 
Image texture is a quantification of the spatial variation of grey tone values. Haralick
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et al. (1973) presented texture measures that may be derived by comparing the values
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et al. (1973) suggested the use  of grey level co-occurrence matrices (GLCM). This method is based on the joint probability distributions of pairs of pixels. GLCM show how often  each  gray  level  occurs  at  a pixel  located  at  a fixed  geometric  position  relative  to  each  other  pixel, as  a function of the gray level (Srinivasan and Shobha 2008). An essential component is the definition of eight nearest-neighbor resolution cells (Fig.) that define different matrices for different angles (0°,45°,90°,135°) and distances between the horizontal neighboring pixels.
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of the digital numbers within a window. An essential component of the concept of
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[[File:Texture.png|center|500px|thumb|3x3 window definition and spatial  relationship for calculating Haralick texture measures. Pixel 1 and 5  are 0° (horizontal) nearest neighbors to the center pixel * ; pixel 2  and 6 are 135° nearest neighbors; pixels 3 and 7 are 90° nearest  neighbors, pixel 4 and 8 are 45° nearest neighbors to the center pixel *  (Haralick et al. 1973)]]
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the Haralick texture measures is the definition of eight nearest-neighbor resolution cells
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* In the search engine of the Processing Toolbox, type '''texture''' and select '''HaralickTextureExtraction''' under Feature Extraction of OTB.
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(Fig.). Now we may define different matrices for different angles (0o,45o,90o,135o) and
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* Under the Parameters tab, select a single band or a multiband file as input layer.
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distances between the horizontal neighboring pixels.
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* Select a band number in case of a multiband file under ''Selected Channel''
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Many studies in land cover and forest type classification utilize textural features to
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* Select the size of the neighborhood in x and y direction.
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improve the classifcation accuracies.
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* Specify the range of the grey levels for the specific input band in the '''Image minimum''' and '''Image maximum''' field. Check [Raster metadata] for correct values!
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We use the ASTER satellite band number 1 (green) and an inter-pixel sampling distance
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* Select as number of bins for the histogram: '''32'''.
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of one. At first we need to linearly transform the 16-bit ASTER data to 8-bit radimetric
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* Select the Texture Set: '''Simple'''
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resolution.
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[[File:otb_haralick.png|400px]]
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{{Exercise|message=Exercise 35|text=}}
 
  
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==Related articles==
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* The result file contains the following texture measures:
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* [[Automated cloud detection]]
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# Energy
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* [[Image subtraction]]
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# Entropy
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* [[Normalized Difference Vegetation Index (NDVI)]]
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# Correlation
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* [[Principal components analysis (PCA)]]
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# Inverse Difference Moment
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* [[Spectral ratioing]]
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# Inertia
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* [[Tasseled cap]]
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# Cluster Shade
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# Cluster Prominence
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# Haralick Correlation
  
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[[category:Image enhancement]]
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{| class="wikitable"
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|style="border: 0pt" | [[file:Qgis_goe_synthesis.png|thumb|left|500px|'''Figure A:''' Input image: Sentinel-2 band B2, (University Göttingen Campus North)]]
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|style="border: 0pt" | [[file:Qgis_goe_synthesis_haralick.png|thumb|center|500px|'''Figure B:''' Output image: Haralick textures (RGB= Haralick Correlation, Energy, Entropy)]]
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|}
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[[category:Spatial Filtering]]

Latest revision as of 22:20, 29 November 2020

Image texture is a quantification of the spatial variation of grey tone values. Haralick et al. (1973) suggested the use of grey level co-occurrence matrices (GLCM). This method is based on the joint probability distributions of pairs of pixels. GLCM show how often each gray level occurs at a pixel located at a fixed geometric position relative to each other pixel, as a function of the gray level (Srinivasan and Shobha 2008). An essential component is the definition of eight nearest-neighbor resolution cells (Fig.) that define different matrices for different angles (0°,45°,90°,135°) and distances between the horizontal neighboring pixels.

3x3 window definition and spatial relationship for calculating Haralick texture measures. Pixel 1 and 5 are 0° (horizontal) nearest neighbors to the center pixel * ; pixel 2 and 6 are 135° nearest neighbors; pixels 3 and 7 are 90° nearest neighbors, pixel 4 and 8 are 45° nearest neighbors to the center pixel * (Haralick et al. 1973)
  • In the search engine of the Processing Toolbox, type texture and select HaralickTextureExtraction under Feature Extraction of OTB.
  • Under the Parameters tab, select a single band or a multiband file as input layer.
  • Select a band number in case of a multiband file under Selected Channel
  • Select the size of the neighborhood in x and y direction.
  • Specify the range of the grey levels for the specific input band in the Image minimum and Image maximum field. Check [Raster metadata] for correct values!
  • Select as number of bins for the histogram: 32.
  • Select the Texture Set: Simple

Otb haralick.png



  • The result file contains the following texture measures:
  1. Energy
  2. Entropy
  3. Correlation
  4. Inverse Difference Moment
  5. Inertia
  6. Cluster Shade
  7. Cluster Prominence
  8. Haralick Correlation
Figure A: Input image: Sentinel-2 band B2, (University Göttingen Campus North)
Figure B: Output image: Haralick textures (RGB= Haralick Correlation, Energy, Entropy)
Retrieved from "http://wiki.awf.forst.uni-goettingen.de/wiki/index.php?title=Haralick_Texture&oldid=16134"
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