Remote sensing image classification development in the past decade

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7494, Issue , 2009, Pages

  Li, Yan ^a   Yan, Li ^a   Liu, Jin ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Artificial neural networks; Evolutionary algorithm; Fuzzy clustering algorithm; Image object; Multiscale analysis; Remote sensing image classification; Support vector machine

Indexed keywords

ARTIFICIAL NEURAL NETWORK; ARTIFICIAL NEURAL NETWORKS; CLASSIFICATION ALGORITHM; ENVIRONMENT MONITORING; FOREST MANAGEMENT; MILITARY TARGETS; MULTI SCALE ANALYSIS; MULTI-SPECTRAL; MULTISCALES; OBJECT BASED; PATTERN RECOGNITION METHOD; REMOTE SENSING APPLICATIONS; REMOTE SENSING CLASSIFICATION; REMOTE SENSING IMAGE CLASSIFICATION; REMOTE SENSING IMAGERY; RESEARCH AND APPLICATION; THEMATIC MAPPING;

BACKPROPAGATION; EVOLUTIONARY ALGORITHMS; FUZZY CLUSTERING; FUZZY NEURAL NETWORKS; FUZZY SYSTEMS; IMAGE ACQUISITION; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RECONSTRUCTION; IMAGE RETRIEVAL; MAPPING; MILITARY APPLICATIONS; MILITARY PHOTOGRAPHY; OBJECT RECOGNITION; PLANNING; REMOTE SENSING; SUPPORT VECTOR MACHINES;

CLUSTERING ALGORITHMS;

EID: 71449091236     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.832872     Document Type: Conference Paper

References (35)

1. Thapa, R. B. and Murayama, Y., "Urban mapping, accuracy, & image classification: A comparison of multiple approaches in Tsukuba City, Japan", Applied Geography, 29(1), 135-144 (2009).
2. Chmiel, J. and Fijalłkowska A., http://www.isprs.org/commission4/ geobia2008/Sessions/Session6/6748-chmiel-Proc-pos.pdf
3. Huang, X., Zhang, L. P. and Li, P. X., "An adaptive multiscale information fusion approach for feature extraction and classification of IKONOS multispectral imagery over urban areas", Geoscience and Remote Sensing Letters, 4(4), 654-658 (2007).
4. Lu, D. and Weng, Q., "A survey of image classification methods and techniques for improving classification performance", International Journal of Remote Sensing, 28(5), 823-870 (2007).
5. Hay, G. J., Castilla, G., Wulder, M. A. and Ruiz, J. R., "An automated object-based approach for the multiscale image segmentation of forest scenes", International Journal of Applied Earth Observation and Geoinformation, (7), 339-359 (2005).
6. Witkin, A. P., "Scale-space filtering" Proc. Int'l Joint Conf. Artificial Intelligence, Palo Alto, Calif.: Kaufmann, 1019-1022 (1983).
7. Wu, J. G., Jelinski, D. E., Luck, M. and Tueller, P. T., "Multiscale analysis of landscape heterogeneity: scale variance and pattern metrics", Geographic Information Sciences, 6(1), 6-19 (2000).
8. Hay, G. J., Blaschke, T., Marceau, D. J. and Bouchard, A., "A comparison of three image-objet methods for the multiscale analysis of landscape structure", ISPRS Journal of Photogrammetry & Remote Sensing, 57 (5-6), 327-345 (2003).
9. Lindeberg, T., [Scale-Space Theory in Computer Vision], Kluwer Academic Publishers, Dordrecht, The Netherlands, 165-185 (1994).
10. Baatz, M. and Schäpe, A., "Multiresolution segmentation: an optimization approach for high quality multiscale image segmentation". In: Strobl, J., Blaschke, T. (Eds.), Angewandte Geogr. Informationsverarbeitung, vol. XII. Wichmann, Heidelberg, 12-23 (2000).
11. Benz, U.C., Hofmann, P., Willhauck, G., Lingenfelder, I. and Heynen, M., "Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information", International Journal of Photogrammetry and Remote Sensing, 58 (3-4), 239-258 (2004).
12. Gilmore, M. S., Wilson, E. H., Barrett, N., Civco, D. L., Prisloe, S., Hurd, J. D. and Chadwick, C., "Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh", Remote Sensing of Environment, 112(11), 4048-4060 (2008).
13. Triepke, F. J., Brewer, C. K., Leavell, D. M. and Novak, S. J., "Mapping forest alliances and associations using fuzzy systems and nearest neighbor classifiers", Remote Sensing of Environment, 112(3), 1037-1050 (2008).
14. Melgani, F. and Bruzzone, L., "Classification of hyperspectral remote sensing images with support vector machines", IEEE Transactions on Geoscience and Remote Sensing, 42(8), 1778 - 1790 (2004).
15. Huang, C., Davis, L. S. and Townshend, J. R. G., "An assessment of support vector machines for land cover classification", International Journal of Remote Sensing, 23, 725-749 (2002).
16. Yang, H. C., Zhang, S. B., Deng, K. Z. and Du, P. J., "Research into a feature selection method for hyperspectral imagery using PSO and SVM", Journal of China University of Mining and Technology, 17(4), 473-478 (2007).
17. Tseng, M. H., Chen, S. J., Hwang, G. H. and Shen, M. Y., "A genetic algorithm rule-based approach for land-cover classification", ISPRS Journal of Photogrammetry and Remote Sensing, 63(2), 202-212 (2008).
18. Liu, Z. j., Liu, A. X., Wang, C. Y. and Niu, Z., "Evolving neural network using real coded genetic algorithm (GA) for multispectral image classification", Future Generation Computer Systems, 20(7), 1119-1129 (2004).
19. Li, L. Y. and Li D. R., "Fuzzy entropy image segmentation based on particle swarm optimization", Progress in Natural Science, 18(9), 1167-1171 (2008).
20. Elbeltagi, E., Hegazy, T. and Grierson, D., "Comparison among five evolutionary-based optimization algorithms", Advanced Engineering Informatics 19 (1), 43-53 (2005).
21. Eberhart, R. C., Simpson, P. K., and Dobbins, R. C., [Computational intelligence PC tools], Orlando, FL: Academic Press (1996).
22. Wang, W. N. and Zhang, Y. J., "On fuzzy cluster validity indices, Fuzzy Sets and Systems", 158(19), 2095-2117 (2007).
23. Wang, X., Wang, Y. and Wang L., "Improving fuzzy c-means clustering based on feature weight learning", Pattern Recognit. Lett., 25, 1123-1132 (2004).
24. Fan, J. C., Han, M. and Wang, J., "Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation", Pattern Recognition, 42(11), 2527-2540 (2009).
25. Maulik, U. and Saha, I., "Modified differential evolution based fuzzy clustering for pixel classification in remote sensing imagery", Pattern Recognition, 42(9), 2135-2149 (2009).
26. Pacifici, F., Chini, M. and Emery, W. J., "A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification", Remote Sensing of Environment, 113(6), 1276-1292 (2009).
27. Zhai, Y. S., Thomasson, J. A., Boggess, J. E. and Sui, R. X., "Soil texture classification with artificial neural networks operating on remote sensing data", Computers and Electronics in Agriculture, 54(2), 53-68 (2006).
28. Villmann, T., Merényi, E. and Hammer, B., "Neural maps in remote sensing image analysis", Neural Networks, 16(3-4), 389-403 (2003).
29. Filippi, A. M. and J. R. Jensen, "Fuzzy learning vector quantization for hyperspectral coastal vegetation classification", Remote Sensing of Environment, 100(4), 512-530 (2006).
30. Oza, N. C. and Tume, K., "Classifier ensembles: select real-world applications", Information Fusion, 9(1), 4-20 (2008).
31. Mitrakis, N. E., Topaloglou, C. A., Alexandridis, T. K., Theocharis, J.B. and Zalidis, G. C., "Decision fusion of GA self-organizing neuro-fuzzy multilayered classifiers for land cover classification using textural and spectral features", Geoscience and Remote Sensing, 46(7), 2137-2152 (2008).
32. Fauvel, M., Chanussot, J. and Benediktsson, J. A., "Decision fusion for the classification of urban remote sensing images", IEEE Transactions on Geoscience and Remote Sensing, 44(10), 2828-2838(2006).
33. Chan, J. C.W. and Paelinckx, D., "Evaluation of random forest and adaboost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery", Remote Sensing of Environment, 112(6), 2999-3011 (2008).
34. Gislason, P. O., Benediktsson, J. A. and Sveinsson, J. R., "Random Forests for land cover classification", Pattern Recognition Letters, 27(4), 294-300 (2006).
35. Haralick, R. M., Shanmugan, K. and Dinstein, I., "Texture features for image classification", IEEE Trans. Syst., Man Cybern., SMC-3(6), 610-621 (1973).