Researches On Independent Component Analysis Method And Its Applications Of Remote Sensing For Dynamic Monitoring

Independent Component Analysis (ICA) was a new development of signal processing. As an effective method to the separation of blind signals, ICA had attracted broad attention. Calculated higher-order statistics information, ICA could estimate the source signals, which was statistics independent and mixed by unknown factor, from the observed signals. Because ICA reflected higher-order statistics characteristic of image data, it had successful application in many fields of image processing. This paper discussed ICA's fast algorithm and its application in remote image processing:ICA algorithm and its fast algorithm (FastICA) were introduced. M-FastICA was advanced based analyzing kernel iterate course of the FastICA algorithm. M-FastICA improved convergence performance and reduced iterations. Aimed at the convergent speed of M-FastICA was dependent on initial weights, LM-FastICA was advanced by imported looseness agent, and reduced the dependence on initial weights.Analyzing the imaging mechanism of satellite multi-spectral remote sensing images, we considered the bands images of remote sensing were mixed by the spectral features of diverse surface feature randomly. The independent components separated from the remote sensing images by ICA could concentrate the surface features information, and its separability was better and could obtain better classify result than PCA.LM-FastICA algorithm was applied to the remote system of Changzhou city. Firstly, the remote images were pre-processed by using the ICA alorithm's advantage. Secondly, Neural Network algorithm or Min-Distance algorithm were used to classify, and the cropper, forest, water's remote information were recovered.Finally, a practical processing system of remote system of Changzhou city was introduced. It could automatically recover the cropper remote information, such as rice, rape and wheat; could obtain the distribution map of thermal field; could recover the distribution map of forest and water, respectively. In addition, it could query and deliver the information which was obtained by the System.