Title :
Remote sensing image classification and recognition based on KFCM
Author :
Shi Yun-song ; Shi Yu-feng
Author_Institution :
Coll. of Civil Eng., Nanjing Forestry Univ., Nanjing, China
Abstract :
Based on fuzzy C-means method and the characteristics of kernel-based method, the algorithm of kernel-based fuzzy clustering is presented, in which the objective function of fuzzy C-means is substituted by Gaussian kernel objective function. The approach of kernel-based fuzzy C-means clustering is used in the classification and recognition of remote sensing images, and the result shows that it can effectively improve the classification accuracy of remote sensing images compared with the traditional fuzzy C-means clustering.
Keywords :
Gaussian processes; fuzzy set theory; geophysical image processing; image classification; pattern clustering; remote sensing; Gaussian kernel objective function; KFCM; classification accuracy; fuzzy c-means method; image recognition; kernel-based fuzzy c-means clustering; kernel-based method; remote sensing image classification; Accuracy; Algorithm design and analysis; Buildings; Classification algorithms; Clustering algorithms; Kernel; Remote sensing; classification and recognition; clustering; fuzzy C-means; kernel fuzzy C-means; remote sensing image;
Conference_Titel :
Computer Science and Education (ICCSE), 2010 5th International Conference on
Conference_Location :
Hefei
Print_ISBN :
978-1-4244-6002-1
DOI :
10.1109/ICCSE.2010.5593412
