In this paper, FastICA and KERNELICA algorithms and their application as preprocessing for remote sensing imagery (RSI) classification are discussed, as well as a comparison between the two algorithms. Both of the algorithms are applied to a TM RSI, obtained from Shunyi, Beijing, 1999. Then a Maximum Likelihood Classification (MLC) method uniting ISODATA is used to perform classification for the raw and preprocessed data. The results show that use of the data preprocessed gives more confident results than those obtained from the raw data. And for the two ICA algorithms, on one side, both are quite steady and can get rid of correlations existing in RSI. On the other side, KERNELICA appears to perform better than FastICA on texture information left in the independent component image (ICI) and classification accuracy. The convergence of the two
['Hui He', 'Xianchuan Yu', 'Wanglu Peng']
A comparison between FastICA and KernelICA in remote sensing imagery classification