An effective hybrid approach to remote-sensing image classification

References

• Bezdek, J. C., R. Ehrlich, and W. Full. 1984. “FCM: The Fuzzy C-Means Clustering Algorithm.” Computers & Geosciences 10 (2): 191–203. doi:10.1016/0098-3004(84)90020-7.
   Web of Science ®Google Scholar
• Binaghi, E., P. A. Brivio, P. Ghezzi, and A. Rampini. 1999. “A Fuzzy Set-Based Accuracy Assessment of Soft Classification.” Pattern Recognition Letters 20 (9): 935–948. doi:10.1016/S0167-8655(99)00061-6.
   Web of Science ®Google Scholar
• Chawla, S. 2010. Possibilistic C-Means-Spatial Contextual Information Based Sub-Pixel Classification Approach for Multi-Spectral Data. Enschede: University of Twente, Faculty of Geo-Information and Earth Observation (ITC).
   Google Scholar
• Congalton, R. G. 1991. “A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data.” Remote Sensing of Environment 37 (1): 35–46. doi:10.1016/0034-4257(91)90048-B.
   Web of Science ®Google Scholar
• Dave’, R. N. 1991. “Characterization and Detection of Noise in Clustering.” Pattern Recognition Letters 12: 657–664. doi:10.1016/0167-8655(91)90002-4.
   Web of Science ®Google Scholar
• Dave’, R. N. 1993, “Robust Fuzzy Clustering Algorithms.” In Second IEEE International Conference on Fuzzy Systems, San Francisco, CA, March 28, 1993– April 01, 1993, vol 2, 1281–1286.
   Google Scholar
• Dave’, R. N., and R. Krishnapuram. 1997. “Robust Clustering Methods: A Unified View.” IEEE Transactions on Fuzzy Systems 5 (2): 270–293. doi:10.1109/91.580801.
   Web of Science ®Google Scholar
• Dehghan, H., and H. Ghassemian. 2006. “Measurement of Uncertainty by the Entropy: Application to the Classification of MSS Data.” International Journal of Remote Sensing 27 (18): 4005–4014. doi:10.1080/01431160600647225.
   Web of Science ®Google Scholar
• Derin, H., and H. Elliott. 1987. “Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields.” IEEE Transactions on Pattern Analysis and Machine Intelligence 9 (1): 39–55. doi:10.1109/TPAMI.1987.4767871.
   PubMed Web of Science ®Google Scholar
• Dubes, R. C., and A. K. Jain. 1989. “Random Field Models in Image Analysis.” Journal of Applied Statistics 16 (2): 131–164. doi:10.1080/02664768900000014.
   Google Scholar
• Foody, G. M. 1997. “Fully Fuzzy Supervised Classification of Land Cover from Remotely Sensed Imagery with an Artificial Neural Network.” Neural Computing & Applications 5 (4): 238–247. doi:10.1007/BF01424229.
   Web of Science ®Google Scholar
• Foody, G. M. 2000. “Estimation of Sub-Pixel Land Cover Composition in the Presence of Untrained Classes.” Computers & Geosciences 26 (4): 469–478. doi:10.1016/S0098-3004(99)00125-9.
   Web of Science ®Google Scholar
• Geman, S., and D. Geman. 1984. “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images.” IEEE Transactions on Pattern Analysis and Machine Intelligence 6 (6): 721–741. doi:10.1109/TPAMI.1984.4767596.
   PubMed Web of Science ®Google Scholar
• Green, K., and R. G. Congalton. 2004. An Error Matrix Approach to Fuzzy Accuracy Assessment: The NIMA Geocover Project. Boca Raton, FL: CRC Press. eBook ISBN : 978-0-203-49758-6.
   Google Scholar
• Krishnapuram, R., and J. M. Keller. 1993. “A Possibilistic Approach to Clustering.” IEEE Transactions on Fuzzy Systems 1 (2): 98–110. doi:10.1109/91.227387.
   Google Scholar
• Krishnapuram, R., and J. M. Keller. 1996. “The Possibilistic C-Means Algorithm: Insights and Recommendations.” IEEE Transactions on Fuzzy Systems 4 (3): 385–393. doi:10.1109/91.531779.
   Web of Science ®Google Scholar
• Li, S. Z. 1995. “On Discontinuity-Adaptive Smoothness Priors in Computer Vision.” IEEE Transactions on Pattern Analysis and Machine Intelligence 17: 576–586. doi:10.1109/34.387504.
   Web of Science ®Google Scholar
• Li, S. Z. 2009. Markov Random Field Modeling in Image Analysis. London: Springer. ISBN: 978-1-84800-279-1.
   Google Scholar
• Magnussen, S., P. Boudewyn, and M. Wulder. 2004. “Contextual Classification of Landsat TM Images to Forest Inventory Cover Types.” International Journal of Remote Sensing 25 (12): 2421–2440. doi:10.1080/01431160310001642296.
   Web of Science ®Google Scholar
• Mather, P., and B. Tso. 2010. Classification Methods for Remotely Sensed Data. London: CRC Press. ISBN: 9781420090727.
   Google Scholar
• Pontius, Jr. R. G., and M. L. Cheuk. 2006. “A Generalized Cross-Tabulation Matrix to Compare Soft-Classified Maps at Multiple Resolutions.” International Journal of Geographical Information Science 20 (1): 1–30. doi:10.1080/13658810500391024.
   Web of Science ®Google Scholar
• Silván-Cárdenas, J., and L. Wang. 2008. “Sub-Pixel Confusion Uncertainty Matrix for Assessing Soft Classifications.” Remote Sensing of Environment 112 (3): 1081–1095. doi:10.1016/j.rse.2007.07.017.
   Web of Science ®Google Scholar
• Singha, M., A. Kumar, A. Stein, P. N. L. Raju, and Y. K. Murthy. 2015. “Importance of DA-MRF Models in Fuzzy Based Classifier.” Journal of the Indian Society of Remote Sensing 43 (1): 1–9.
   Web of Science ®Google Scholar
• Tolpekin, V. A., and A. Stein. 2009. “Quantification of the Effects of Land-Cover-Class Spectral Separability on the Accuracy of Markov-Random-Field-Based Superresolution Mapping.” IEEE Transactions on Geoscience and Remote Sensing 47 (9): 3283–3297. doi:10.1109/TGRS.2009.2019126.
   Web of Science ®Google Scholar
• Townsend, P. A. 2000. “A Quantitative Fuzzy Approach to Assess Mapped Vegetation Classifications for Ecological Applications.” Remote Sensing of Environment 72 (3): 253–267. doi:10.1016/S0034-4257(99)00096-6.
   Web of Science ®Google Scholar
• Tso, B., and R. C. Olsen. 2005. “Combining Spectral and Spatial Information into Hidden Markov Models for Unsupervised Image Classification.” International Journal of Remote Sensing 26 (10): 2113–2133. doi:10.1080/01431160512331337844.
   Web of Science ®Google Scholar
• Zadeh, L. A. 1965. “Fuzzy Sets.” Information and Control 8 (3): 338–353. doi:10.1016/S0019-9958(65)90241-X.
   Google Scholar
• Zhang, J., and G. Foody. 1998. “A Fuzzy Classification of Sub-Urban Land Cover from Remotely Sensed Imagery.” International Journal of Remote Sensing 19 (14): 2721–2738. doi:10.1080/014311698214479.
   Web of Science ®Google Scholar