基于线性模型的高光谱图像解混及应用

发布时间：2018-04-26 09:11

本文选题：高光谱图像 + 混合像元分解　；参考：《成都理工大学》2014年硕士论文

【摘要】：上世纪80年代高光谱遥感技术开始发展，随着成像技术不断成熟，，高光谱遥感在越来越多领域得到广泛应用。高光谱图像与传统的多光谱遥感图像相比，能够获得每个像元的连续波谱信息，这解决了“成像无光谱”，“光谱不成像”的技术难题。然而由于成像光谱仪分辨率不高等原因，这使得图像中的每个像元内往往包含着多种不同的地物类型，从而形成混合像元，在信息提取过程中不能随便将这些像元划归到任何一类地物中去。若把只包含一种纯净地物的像元称为端元，把端元从高光谱图像中准确的提取出来成了研究的重点。获取端元后，才能很好地进行后续的解混、匹配、分类识别等研究。近年来国内外学者研究和发展了不少混合像元分解模型，其中线性混合模型以其结构简单，物理意义明确等优点成为研究的热点。本文介绍了高光谱图像的数据特点，高光谱图像的降维，端元提取的经典算法，像元的线性混合模型等基础理论。其中在数据降维方面主要采用了主成分分析法和最小噪声分离法，并采用了纯净像元指数和内部体积最大法进行了端元提取。然后研究了线性模型下的线性波谱分和基于MTMF的混合像元分解，其中线性波谱分离主要采用全约束下最小二乘法来求丰度。最后重点介绍了MTMF方法的理论基础和处理流程，即结合MNF变换，使用匹配滤波MF进行丰度估计，再用MT检查并排除假阳性值。模拟数据实验结果表明MTMF方法误差较小，同时，使用MTMF法对ENVI自带的高光谱图像数据实验表明该方法有良好效果。最后，将MTMF法应用到机载CASI/SASI高光谱遥感测量仪器在吉木萨尔地区获取的飞行测量高光谱SASI数据，对所识别出的端元进行基于MTMF法混合像元分解，并进行蚀变填图，结果显示与Google Earth所查找到的识别填图区域所在的地质风貌基本吻合。
[Abstract]:Hyperspectral remote sensing technology began to develop in 1980s. With the development of imaging technology, hyperspectral remote sensing has been widely used in more and more fields. Compared with traditional multispectral remote sensing images, hyperspectral images can obtain the continuous spectral information of each pixel, which solves the technical problem of "imaging without spectrum" and "spectrum without image". However, because of the low resolution of the imaging spectrometer, each pixel in the image often contains many different types of ground objects, thus forming a mixed pixel. In the process of information extraction, these pixels can not be randomly classified into any kind of feature. If the pixel containing only one kind of pure ground object is called endelement, it is the focus of the research to extract the endmember from hyperspectral image accurately. After obtaining the endelements, we can do the following research, such as demultiplexing, matching, classification recognition and so on. In recent years, many mixed pixel decomposition models have been studied and developed by scholars at home and abroad. Among them, the linear mixed model has become a hotspot for its simple structure and clear physical meaning. This paper introduces the data characteristics of hyperspectral image, the dimension reduction of hyperspectral image, the classical algorithm of End-element extraction, the linear mixed model of pixel and so on. The principal component analysis (PCA) and the minimum noise separation (MNSS) are used to reduce the dimension of the data, and the pure pixel index and the maximum internal volume method are used to extract the endcomponents. Then the linear spectral fraction and the mixed pixel decomposition based on MTMF are studied in the linear model, in which the least square method with full constraints is used to calculate the abundance of the linear spectral separation. In the end, the theoretical foundation and processing flow of MTMF method are introduced emphatically, that is, combining with MNF transform, using matched filter MF to estimate abundance, then using MT to check and exclude false positive value. The simulation results show that the error of MTMF method is small, and the MTMF method has good effect on the hyperspectral image data of ENVI. Finally, the MTMF method is applied to the flight measurement hyperspectral SASI data obtained by airborne CASI/SASI hyperspectral remote sensing instrument in Jimusar area. The identified endelements are decomposed by mixed pixel method based on MTMF method, and altered mapping is carried out. The results show that the geological features of the identified mapping areas found by Google Earth are in good agreement with each other.
【学位授予单位】：成都理工大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TP751

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