1. Landsat classification © Team

2. SSIP 20052 © 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

3. SSIP 20053 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.

4. SSIP 20054 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.

5. SSIP 20055 The Solution

6. SSIP 20056 The Solution Solution 2. - Histogram matching II. Convert the histograms to a greyscale. (Y=0,299 R+0,587 G+0,114 B)

7. SSIP 20057 The Solution Solution 3. – Markov Random Fields Statistics based classifier algorithm. Uses spatial information. Driven by energy minimization.

8. SSIP 20058 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 GLCM (Gray Level Cooccurrence Matrix) energy

9. SSIP 20059 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

10. SSIP 200510 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

11. SSIP 200511 The Solution Solution 4. - Texture-based recognition

12. SSIP 200512 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. 573- 576, San Francisco, CA, March 23-26,1992.

13. The End Thank you for your attention!