Design of improved hyperspectral image classification scheme based on weighted fuzzy C means algorithm

Huan MA; Zhiyong JING; Ming CHEN; Jianwei ZHANG

doi:10.3969/j.issn.1007-5461. 2015.05.005

[6] Yang J M, Yu P T, Kuo B C. A nonparametric feature extraction and its application to nearest neighbor classification for hyperspectral image data［J］. IEEE Trans. Geosci. Remote Sens., 2010, 48(3): 1279-1293.

[7] Mojaradi B, Abrishami-Moghaddam H, Zoej M, et al. Dimensionality reduction of hyperspectral data via spectral feature extraction［J］. IEEE Trans. Geosci. Remote Sens., 2009, 47(7): 2091-2105.

[8] Lin K P, Chen M S. On the design of the privacy preserving SVM classifier［J］. IEEE Transactions on Knowledge and Data Engineering, 2011, 23(11): 1704-1717.

[9] Pal N R, Bezdek J C. On cluster validity for the fuzzy c-means model［J］. IEEE Trans. Fuzzy Systems, 1995, 3(4): 370-379.

[11] Bezdek J C. A convergence theorem for the fuzzy ISODATA clustering algorithm［J］. IEEE Trans. Pattern Analysis Machine Intelligence, 1980, 2(1): 1-8.

[12] Hathaway R J, Bezdek J C. Local convergence of the fuzzy c-means algorithms［J］. Pattern Recognition, 1986, 19(6): 477-480.

[13] Landgrebe D A. Signal Theory Methods in Multispectral Remote Sensing［M］. New York: Wiley, 2003.