This study is to classify satellite data based on supervised fuzzy classification technique. Attempts to classify remote sensed data with traditional statistical classification technique faced number of challenges as the traditional per-pixel classifier examine only the spectral variance ignoring the spatial distribution of the pixels, corresponding to the land cover classes and correlation between bands causes problems in classifying the data and its result. Hence in this work, we use fuzzy classification. this makes no assumption about stastical distribution of the data & it provides more complete information for a thorough image analysis. The results show that fuzzy supervised technique algorithm showed an improvement of more than 5% of accuracy at 12 classes on comparison with MLC.

1. V. Aurelie, A. Vladimir, and Gabriele Moser, "Supervised Classification of Multi-sensor and Multi-resolution Remote Sensing Images with a Hierarchical Copula-based Approach. " IEEE Transactions On Geoscience And Remote Sensing, 20 (3), 1-18 (2013).
2. N. Bedawi, and M. Kamel, "Segmentation of very high resolution remote sensing imagery of urban areas using particle swarm optimization algorithm. " In: Image Analysis and Recognition: Lecture Notes in Computer Science, New York: Springer-verlag (2010).
3. M. Brown, H. Lewis, and S. Gunn, "Linear spectral mixture models and support vector machines for remote sensing. " IEEE Transactions on Geosciences and Remote Sensing, 38 (5): 2346–2360 (2000).
4. C. Chen. "Fuzzy training data for fuzzy supervised classification of remotely sensed images. " Paper presented at 20th Asian Conference on Remote Sensing, November 460–465 (1999).
5. Farid Melani, Bakir. A. R. Al. Hashemy, and Saleem M. R. Taha, An Explicit Fuzzy Supervised Classification Methods for Multispectral Remotely Sensed Images, Transactions on Geoscience and Remote Sensing, VOL. 38, January 2000, pp 287-295.
6. Giles M. Foody, Status of Land Cover Classification Accuracy Assessment, Remote Sensing of Environment, 2002, 185-201.
7. Thomas M Lillesand, Ralf W Kiefer and Jonathan W Chipman, Remote Sensing and Image Interpretation, 5th Edition, John Wiley & Sons, 2004.
8. Akif Mohammed Al Fugra, Biswajeet Pradhan, Thamer Ahmed Mohammed, Improvement of land-use classification using object oriented and fuzzy logic approach, Appl Geomat, November 2009, pp 111-120.
9. A. Baraldi, and F. Parmiggiani. 1995, "A neural network for unsupervised categorization of multivalued input pattern: an application to satellite image clustering. " IEEE Transactions on Geoscience and Remote Sensing, 33 (2): 305–316 (1995).
10. D. LU and Q. Weng, A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, Vol. 28, No. 5, 10 March 2007, 823-870.

Fuzzy Supervised Classification Mlc Remote Sensing