Remote Sensing Image Classification Based on a Loose Modified FastICA Algorithm

The multi-band remote sensing images reflect the spectral features of diverse surface features, and the classification is the basis of remote sensing applications. The independent component analysis (ICA) algorithm uses the high-order statistical information of multi-band remote sensing images. It not only removes the correlation of images, but also obtains the new band images that are mutual independent. But the computational complexity of FastICA is too big, influencing the application of ICA in remote sensing field. M-FastICA algorithm could improve the performance of FastICA algorithm by reducing the computational quantum. But like FastICA, its convergence is dependent on initial weight. By importing loose gene in the M-FastICA algorithm, the new algorithm (LM-FastICA) could implement convergence in large-scale. BP neural network is used in classification of the remote-sensing images which are pre-processed by ICA. The exactness rate of pre-processed images is higher than that of source images, and the performance of classification of three kinds of ICA algorithms is near.