【作者】 古瑞东；

【导师】 宋梅萍；

【作者基本信息】 大连海事大学 ， 计算机科学与技术， 2012， 硕士

【摘要】 高光谱遥感技术作为一种新的遥感技术,受到越来越多的关注。其中主要的原因是：高光谱遥感技术能够探测到在宽波段中不可测的物质,而且高光谱遥感技术还将反映地物属性的光谱与直观的几何图像联系起来,使人们可以直观地“感受”到光谱的特征。于是,对地物的分析也转换为对光谱曲线的相似性和差异性的分析。而光谱曲线的特征提取是光谱曲线分析前提。因此,高效、准确地提取出光谱曲线的特征显得尤为重要。本文正是基于以上因素对光谱曲线的特征提取算法进行深入地研究。将模式识别中的曲线树算法引入到光谱曲线的特征提取中,并在深入理解曲线树算法的基础上对其进行改进,使其更适用于光谱曲线的特征提取。先通过实验室光谱仪采集了6种地物的光谱曲线。然后应用二值编码、曲线树及其改进算法对其进行特征提取和分类,验证曲线树改进算法的有效性。同时通过对特定曲线做平移、拉伸及旋转等操作,形成一组新的曲线。再应用三种算法提取这组曲线的特征,之后比较他们之间的欧氏距离。证明曲线树算法对于曲线的平移、旋转、拉伸具有良好的不变性。最后将曲线树及其改进算法应用到实际的高光谱图像中去。先对原始的高光谱图像用对数残差和内部平均法进行反射率反演；然后应用本文中的各种曲线特征提取算法对其进行曲线的特征提取,最后对图像进行分类。通过对实验结果的分析得出改进的曲线树算法对较为“纯”物质的分类的效果较好,而对油水混合物的分类的效果有待提高的结论。更多还原

【Abstract】 Hyperspectral remote sensing technology draws the more attention of the public as a new remote sensing technology. The main reason is:Hyperspectral remote sensing technology has a higher spectral resolution and broader spectral imaging range. That makes it possible to detect surface features characteristic that can’t be detected in the ordinary remote sensing conditions. And it also makes spectral curve contact with geometry, so that people can intuitively feel the characteristics of the spectrum. Therefore, the surface features analysis also converted to the analysis of the similarities and differences in the spectral curve. The feature extraction of the spectral curve is the premise of spectroscopy. Therefore, it’s important to extract the characteristics of spectral curve efficiently and accurately.So this article in-depth studies on the spectral curve feature extraction algorithm. And the algorithm of curve tree which is in the pattern recognition is introduced to the feature extraction of the spectral curve. Then improve the curve tree algorithm by in-depth studying it, for making it more suitable for the characteristics of the spectral curve extraction.Firstly, spectral curves of six groups of surface features were collected by laboratory spectrometer. Binary encoding algorithm, curve tree algorithm and its improved algorithm were used to classify. And some new the curve were created by processing the certain curve. So a new set of curves were formed. Then Euclidean distances between them were calculated, after the characteristics of the curves were extracted. That proves that curve tree algorithm has invariance for the translation, rotation, stretching of curve.Finally, the curve tree algorithm and its improved algorithm were applied to the hyperspectral oil spill image. And the original hyperspectral images were inversed by the algorithm of log-residuals and internal average. Then the characteristics of the curve were extracted by the algorithms in this paper. Finally, the oil spills in the image were classified. It can be seen from result that the classification results of improved curve tree algorithm are better for the "pure" substances, and the results are bad for the situation of oil-water mixture.更多还原