Abstract
Segmentation is a process of partitioning an image space into some nonoverlapping meaningful homogeneous regions. The success of an image analysis system depends on the quality of segmentation. Two broad approaches to segmentation of remotely sensed images are gray level thresholding and pixel classification [1]. Multispectral nature of most remote sensing images make pixel classification the natural choice for segmentation.
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S. K. Pal, A. Ghosh, and B. Uma Shankar, “Segmentation of remotely sensed images with fuzzy thresholding, and quantitative evaluation,” International Journal of Remote Sensing, vol. 21(11), pp. 2269–2300, 2000.
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© 2003 Springer-Verlag Berlin Heidelberg
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Pal, S.K., Mitra, P. (2003). Rough Sets, EM Algorithm, MST and Multispectral Image Segmentation. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. RSFDGrC 2003. Lecture Notes in Computer Science(), vol 2639. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39205-X_13
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DOI: https://doi.org/10.1007/3-540-39205-X_13
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