摘要
高光谱遥感图象目标探测与分类技术是高光谱遥感理论与应用研究的重要环节。本文从光谱的表达方式、无监督聚类方式、样本分布概率、高光谱图象在特征空间的几何结构以及在图象空间的连续性入手在特征提取、无监督分类、端元提取、线性解混、目标探测、异常探测等方面得到了如下结论:
1.提出了一种基于光谱重排的特征提取方法。理论及实践证明,任何两种不同地物的光谱通过光谱重排之后,总有显著的相对特征出现。这对具有相似波形不同地物的特征提取乃至进一步的分析与处理无疑大有裨益。
2.提出了空间连续性的概念及其定量描述并将其成功的应用于图象分类、光谱优化、去冗余以及端元提取的实时处理。
3.提出了一种基于万有引力的非监督自组织聚类算法。把需要分类的特征空间中的各个样本比做宇宙中的星球,在其间的万有引力作用下产生运动,最后所形成的各个大的星系即对应着我们所需要的分类结果。
4.提出了两种自动提取图象端元的算法。根据高光谱图象在其特征空间中的单形体结构,引入了高维解析几何及施密特正交化,提出了最大距离自动提取端元的算法;引入了一种新的与数据维数无关的求取高维单形体体积的公式,将其应用于端元的自动提取,克服了N-FINDR算法受数据维数限制的固有缺陷;并提出了端元结构函数的概念,使得端元的重要性取决于它对单形体结构的影响而不是其在图象中信息量的多少。这对小目标提取显然有着重大的意义。
5.提出了基于端元投影向量的目标提取算法。利用施密特正交化过程,由图象中的每个端元均可以向别的所有端元构成的超平面做一垂线,在此垂线上的投影即可得到在不考虑信息量的基础上此端元为前景、别的端元为背景的最大区分效果。而以此垂线指向为方向的单位向量我们称之为端元投影向量。
6.提出了一种基于单形体结构本质属性的几何定理并将其应用于线性解混。把图象中任何一个混合象元内部各个端元所占的比例归结为一个简单的体积比,并且给出了证明。无论从理论上,还是在应用上,此结论都有着重要的意义。
7.提出了加权相关矩阵(协方差矩阵)的思想。小目标探测与异常目标探测都是根据图象的统计特性从信息量分布的角度对图象中低概率目标进行探测与分
Target detection and classification is one of primary tasks of hyperspectral imaging. In terms of the method of spectral expression, the style of unsupervised cluster, probability in the data, the geometrical construction of hyperspectral imaging in the band space and the it's continuity in the image space, the dissertation draws some conclusion on feature extraction, unsupervised classification, endmember selection, linear unmixing, target detection and anomaly detection as follows:1. A method for spectral feature extraction was developed based on spectral recomposition. By arranging the spectra by the sort of their reflectance or DN, the spectral curves that are originally difficult to be extracted features from will usually produce some obvious features. It is helpful to feature extraction and father analysis and process.2. The concept of spatial continuity was proposed and successfully applied to image classification, spectral optimization, redundancy reduction and real-time endmember determination.3. A unsupervised classification method was proposed based on universal gravitation. Each pixel that was taken as a star in the universe would move with the gravitation of all the other pixels. The last formed galaxy is corresponding to the result of classification.4. Two approaches of autonomous spectral endmember determination were developed. Based on the convex nature of hyperspectral data in its characteristic space, Gram-Schmidt Orthonormalization process, high dimensional analytic geometry and distance between pixels were introduce to find a unique set of purest pixels in an image; A new volume formula of simplex which was independent of dimension of the data was introduced to find all the endmembers which are larger than any other volume formed from any other combination of pixels. The concept of endmember constrution function was first proposed, so the weightiness of each endmember is depended on it's influence to the simplex contraction, but not it's information magnitude. It's significant to the small target extraction.5. A method of target extraction based on endmember projection vector was developed. Based on the convex nature of hyperspectral data in its band space, a series of vectors named endmember projection vector are produced for use of object extraction. The technique is based on the fact that in band space, any endmember is the farthest point from the hyperplane consisted of all the other endmembers.6. A new theorem about the nature of simplex was proposed and applied to spectral linear unmixing. Once all the endmembers were found, the image cube can be "unmixed" into fractional abundances of each material in each pixel by a simple ratio of volume.
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