独立分量分析及其在遥感动态监测中的应用研究
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摘要
独立分量分析是信号处理技术的新发展,它作为盲信号分离的一种有效方法而受到广泛的关注。独立分量分析算法通过计算数据的高阶统计信息,可以从观测信号中估计出相互统计独立的、原始的、被未知因素混合的信号。由于算法能够反映图像数据的高阶统计特征,在图像处理中得到成功的应用。本文对独立分量分析快速算法及其在遥感图像处理的应用进行了深入研究,主要有以下几方面的工作:
研究了独立分量分析算法,特别是独立分量分析算法中的快速算法FastICA。在分析FastICA算法的核心迭代过程的基础上,提出了改进算法M-FastICA,改善了算法的收敛性能,减少算法的迭代次数。针对M-FastICA算法的收敛依赖于初始权值的问题,在算法过程中加入松弛因子,提出LM-FastICA算法,改善了算法对初始权值的依赖性。
对卫星多光谱遥感图像的成像机理进行分析,认为多谱段遥感图像是地物的谱段信息的随机混合结果。应用独立分量分析算法对其进行分离,使每个独立分量尽量集中某些地物的信息,比主成分分析算法具有更好的可分离性,并得到了更好的分类结果。
将LM-FastICA算法应用到常州地区农业用地和环境动态监测卫星遥感系统中,利用ICA算法的优点对原始遥感图像进行预处理,然后用神经网络算法或者自适应最小距离分类算法,进行农作物分类、林地提取、水面提取。
基于独立分量分析算法构建了一个针对常州地区的可运行遥感系统,可以实现自动提取水稻、油菜和小麦等农作物信息,得到温场分布图,提取林地和水面的分布图。另外,它还可以查询和发布系统获取的信息。
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.
研究了独立分量分析算法,特别是独立分量分析算法中的快速算法FastICA。在分析FastICA算法的核心迭代过程的基础上,提出了改进算法M-FastICA,改善了算法的收敛性能,减少算法的迭代次数。针对M-FastICA算法的收敛依赖于初始权值的问题,在算法过程中加入松弛因子,提出LM-FastICA算法,改善了算法对初始权值的依赖性。
对卫星多光谱遥感图像的成像机理进行分析,认为多谱段遥感图像是地物的谱段信息的随机混合结果。应用独立分量分析算法对其进行分离,使每个独立分量尽量集中某些地物的信息,比主成分分析算法具有更好的可分离性,并得到了更好的分类结果。
将LM-FastICA算法应用到常州地区农业用地和环境动态监测卫星遥感系统中,利用ICA算法的优点对原始遥感图像进行预处理,然后用神经网络算法或者自适应最小距离分类算法,进行农作物分类、林地提取、水面提取。
基于独立分量分析算法构建了一个针对常州地区的可运行遥感系统,可以实现自动提取水稻、油菜和小麦等农作物信息,得到温场分布图,提取林地和水面的分布图。另外,它还可以查询和发布系统获取的信息。
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.
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