A geographical approach to self-organizing maps algorithm applied to image segmentation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6915 LNCS, Issue , 2011, Pages 162-170

  Korting, Thales Sehn ^a   Fonseca, Leila Maria Garcia ^a   Câmara, Gilberto ^a  

^a INSTITUTO NACIONAL DE PESQUISAS ESPACIAIS   (Brazil)

Author keywords

[No Author keywords available]

Indexed keywords

AGRICULTURAL CROPS; CLASSIFICATION ALGORITHM; IMAGE SEGMENTS; INFORMATION EXTRACTION; SATELLITE IMAGES; SPATIAL CORRELATIONS;

ALGORITHMS; CONFORMAL MAPPING; CROPS; PIXELS; REMOTE SENSING; SELF ORGANIZING MAPS; COMPUTER VISION; DATA MINING; IMAGE PROCESSING; LAND USE;

IMAGE SEGMENTATION;

EID: 80052159067     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-23687-7_15     Document Type: Conference Paper

References (21)

1. Baatz, M., Schape, A.: Multiresolution Segmentation: an optimization approach for high quality multi-scale image segmentation. In: Wichmann-Verlag (ed.) XII Angewandte Geographische Informationsverarbeitung, Herbert Wichmann Verlag, Heidelberg (2000)
2. Baça&tild;o, F., Lobo, V., Painho, M.: Applications of different self-organising map variants to geographical information science problems. Self-organising Maps: Applications in Geographic Information Science (2008)
3. Bhandarkar, S., Koh, J., Suk, M.: Multiscale image segmentation using a hierarchical self-organizing map. Neurocomputing 14(3), 241-272 (1997)
4. Bins, L., Fonseca, L., Erthal, G., Li, F.: Satellite imagery segmentation: a region growing approach. In: Brazilian Remote Sensing Symposium, vol. 8 (1996)
5. Chesnel, A.L., Binet, R., Wald, L.: Object oriented assessment of damage due to natural disaster using very high resolution images. In: IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2007, pp. 3736-3739 (2007)
6. Gamanya, R., Demaeyer, P., Dedapper, M.: An automated satellite image classification design using object-oriented segmentation algorithms: A move towards standardization. Expert Systems with Applications 32(2), 616-624 (2007)
7. Haralick, R., Shapiro, L.: Image segmentation techniques. Applications of Artificial Intelligence II 548, 2-9 (1985)
8. He, Y., Wang, H., Zhang, B.: Color based road detection in urban traffic scenes. In: Proceedings of IEEE Intelligent Transportation Systems, vol. 1 (2003)
9. Kim, D., Jeong, S., Park, C.: Comparison of Three Land Cover Classification Algorithms-ISODATA, SMA, and SOM-for the Monitoring of North Korea with MODIS Multi-temporal Data. Korean Journal of Remote Sensing 23(3), 181-188 (2007)
10. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin (2001)
11. Korting, T.S., Fonseca, L.M.G., Bação, F.: Expectation-Maximization x Self-Organizing Maps for Image classification. In: IEEE International Conference on Signal Image Technology and Internet Based Systems, SITIS 2008, pp. 359-365 (2008)
12. Laaksonen, J., Viitaniemi, V., Koskela, M.: Application of Self-Organizing Maps and automatic image segmentation to 101 object categories database. In: Proc. Fourth International Workshop on Content-Based Multimedia Indexing (CBMI 2005), Riga, Latvia,Citeseer (2005)
13. Lee, S., Lathrop, R.: Subpixel analysis of Landsat ETM+ Using Self-Organizing Map (SOM) Neural Networks for Urban Land Cover Characterization. IEEE Transactions on Geoscience and Remote Sensing 44(6), 1642-1654 (2006)
14. Li, Y., Chi, Z.: MR Brain image segmentation based on self-organizing map network. International Journal of Information Technology 11(8), 45-53 (2005)
15. Moreira, J., Costa, L.F.: Neural-based color image segmentation and classification using self-organizing maps. Proceedings of the IX SIBGRAPI 12(6), 47-54 (1996)
16. Perez, A., Benlloch, J., Lopez, F., Christensen, S.: Colour and shape analysis techniques for weed detection in cereal fields. Computers and Electronics in Agriculture 25, 197-212 (2000)
17. Silva, M., Câmara, G., Souza, R., Valeriano, D., Escada, M.: Mining patterns of change in remote sensing image databases. In: The Fifth IEEE International Conference on Data Mining, New Orleans, Louisiana, USA. Citeseer (2005)
18. Tobler, W.: A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46, 234-240 (1970)
19. Vergés-Llahí, J.: Color Constancy and Image Segmentation Techniques for Applications to Mobile Robotics. Ph.D. thesis, UPC (2005)
20. Villmann, T., Merényi, E.: Extensions and modifications of the Kohonen-SOM and applications in remote sensing image analysis. Studies in Fuzziness and Soft Computing 78, 121-144 (2002)
21. Wehrens, R.: Self-organising Maps for Image Segmentation. In: Advances in Data Analysis, Data Handling and Business Intelligence: Proceedings of the 32nd Annual Conference of the Gesellschaft Für Klassifikation EV, Joint Conference with the British Classification Society (BCS) and the Dutch/Flemish Classification, p. 373. Springer, Heidelberg (2009)