摘 要
遥感图像在民用方面、精确定位等方面都有非常重要的作用，遥感图像的自动识别最关键的就是遥感图像的特征提取，为此开展遥感图像的特征提取研究具有非常显著的应用前景和实际意义。这次我主要研究和讨论了遥感图像的光谱特征特征提取方法。
本文介绍了遥感和遥感技术发展的现状、图像的研究现状和特征提取，然后叙述了遥感图像特征提取方法的算法和基本理论，介绍了目前常用的光谱特征提取方法。在以上的基础上，针对传统的KPCA和PCA方法总结遥感数据的特点以及遥感图像光谱特征的缺陷，本文讨论一种将模糊c-均值聚类与KPCA方法相结合的多光谱遥感图像特征提取的方法，并着重研究了此方法在多光谱遥感图像特征提取中的算法、理论及其实现。通过对本文方法与KPCA和PCA方法的试验结果进行比较，证实了这种方法特征提取的性能较前两种方法有着显著的提高，可有效地提取出多光谱图像中的非线性信息。
关键词 遥感图像；遥感数据；光谱特征；特征提取；KPCA；FCM；
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Abstract
Remote sensing image has great importance for military reconnaissance, precision attack and civil activities. Feature extraction is critical for the automatic recognition technology of remote sensing image, so it has good application prospect to study feature extraction methods of remote sensing image. This thesis focuses the research work mainly on the feature extraction methods of spectrum.
First, the thesis introduces the concept and development of remote sensing image, the basic concept and research of image feature extraction. Then the thesis introduces the basic theory and algorithms of remote sensing image feature extraction methods. The commonly used remote sensing image feature extraction methods for spectrum are generalized separately.
Considering the character of the remote sensing image data and the limitation of traditional PCA and KPCA methods when they are used to extract the spectrum feature of remote sensing image, a combination of the FCM and KPCA methods is used for extracting the spectrum feature. Both the theory and algorithm are studied, as well as the implementation. A comparison between the results of PCA, KPCA and FCM+KPCA methods is given, which shows that the FCM+KPCA method can give a much better result than other methods, especially in extracting the nonlinear information of multi spectral remote sensing images.
Keywords Remote sensing image Spectrum feature Feature extraction KPCA
FCM
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