Transcript Landsat classification - u

Landsat classification
©
Team
©
Team
Delia Mitrea – Technical University of Cluj-Napoca, Romania
Sándor Szolyka – Budapest Tech, Hungary
Imre Hajagos – University of Szeged, Hungary
Szabolcs Berecz - Budapest Tech, Hungary
Gergely Grósz – University of Veszprém Georgikon Faculty
of Agricultural, Hungary
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The Problem
• Input: Landsat images of terrain, plus sample images
of fields, sea, forests or etc.
• Aim: Segmentation of scene based on texture and
colour.
• Output: Label scene.
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The Solution
Solution 1. - Histogram matching I.
Step 1. Decompose the image into small cells.
Step 2. Compute the histogram in the RGB
levels (All grid has three (red, green, blue)
histograms.).
Step 3. Classification based on the correlation
of histograms.
Step 4. Segment the image.
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The Solution
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The Solution
Solution 2. - Histogram matching II.
Convert the histograms to a greyscale.
(Y=0,299 R+0,587 G+0,114 B)
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The Solution
Solution 3. – Markov Random Fields
•Statistics based classifier algorithm.
•Uses spatial information.
•Driven by energy minimization.
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The Solution
Solution 4. - Texture-based recognition
Features used:
•Average edge frequency (density)
•Average edge contrast
•GLCM (Gray Level Cooccurrence Matrix) homogeneity
•GLCM (Gray Level Cooccurrence Matrix) entropy
•GLCM (Gray Level Cooccurrence Matrix) variance
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•GLCM (Gray Level Cooccurrence Matrix) energy
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The Solution
Solution 4. - Texture-based recognition
Step 1. Learning
• Select a known region int the image (forest
mountains or water)
• Compute GLCM features and edge-based
features
• Store the feature vector in the training set for
the corresponding class
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The Solution
Solution 4. - Texture-based recognition
Step 2. Recognition
•Select an unknown area in the image in order to
classify it: forest mountains or water
•Compute the GLCM features and the edge-based
features
•Compare the feature vectors with the data int he
training set: euclidean distance
•Use the k-nn method and decide the class
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The Solution
Solution 4. - Texture-based recognition
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References
• M. Berthod, Z. Kato, S. Yu, J. Zerubia: Bayesian
imageclassification using Markov random fields.
Image and Vision Computing,14(1996): 285-295,
1996.
• Z. Kato: Multi-scale Markovian Modelisation in
Computer Vision withApplications to SPOT Image
Segmentation. PhD thesis, INRIA
SophiaAntipolis, France, 1994.
• Z. Kato, J. Zerubia and M. Berthod: Satellite
image classification using amodified Metropolis
dynamics Proc. IEEE International Conf. on
Acoust., Speechand Sig. Proc., vol. 3, pp. 573576, San Francisco, CA, March 23-26,1992.
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The End
Thank you for your
attention!