These problems of nonlinearity, fuzziness and few labeled data were rarely considered in traditional remote sensing image classification. A semi-supervised kernel fuzzy C-means （SSKFCM） algorithm is proposed to overcome these disadvantages of remote sensing image classification in this paper. The SSKFCM algorithm is achieved by introducing a kernel method and semi-supervised learning technique into the standard fuzzy C-means （FCM） algorithm. A set of Beijing-1 micro-satellite＇s multispectral images are adopted to be classified by several algorithms, such as FCM, kernel FCM （KFCM）, semi-supervised FCM （SSFCM） and SSKFCM. The classification results are estimated by corresponding indexes. The results indicate that the SSKFCM algorithm significantly improves the classification accuracy of remote sensing images compared with the others.
An improved Pan-sharpening algorithm appropriate to vegetation applications is proposed to fuse a set of IKONOS panchromatic （PAN） and multispectral image （MSI） data. The normalized difference vegetation index （NDVI） is introduced to evaluate the quality of fusion products. Compared with other methods such as principal component analysis （PCA）, wavelet transform （WT）, and curvelet transform （CT）, this algorithm has a better trade-off between keeping the spatial and spectral information. The NDVI performances indicate that the fusion product of this method is more suitable for vegetation applications than the other methods.
