CASIA OpenIR  > 毕业生  > 硕士学位论文
Alternative TitleHyperspectral Unmixing Using Blind Source Separation
Thesis Advisor彭思龙
Degree Grantor中国科学院研究生院
Place of Conferral中国科学院自动化研究所
Degree Discipline计算机应用技术
Keyword高光谱图像 线性光谱混合模型 稀疏性 物质分子光谱 非负矩阵分解 Gabor变换 Hyperspectral Image Linear Mixing Model Sparseness Molecular Spectroscopy Nonnegative Matrix Factorization Gabor Transform
Abstract在过去三十年间,随着成像光谱技术的不断发展,在飞机或卫星平台上搭载的成像光谱仪采集得到的遥感图像包含了越来越丰富的空间、辐射和光谱信息,从而为地表物质的信息提取和目标检测提供了一个强有力的手段。高光谱遥感数据最主要的特点是将图像维与光谱维信息融合为一体,在获取地表空间图像的同时,得到每个地物的连续光谱信息,从而实现依据地物光谱特征的地物成份信息反演或地物识别。高光谱技术在作物估产、资源调查等领域中显示出巨大的优越性和重要性,但面临的一个问题就是混合像元。对于一套光学遥感器系统而言,图像空间分辨率和光谱分辨率是一对矛盾,在给定信噪比的条件下,较高光谱分辨率(窄光谱波段)往往意味着不能同时具有高空间分辨率。混合像元分解就是进入像元内部,将混合像元分解为不同的“基本组分单元”,或称“端元”,并求得这些基本组分所占比例。使遥感应用由像元级达到亚像元级。 近几年提出的非监督光谱解混算法中,利用非负矩阵分解模型的一类算法表现出了较好的性能,并且也产生了不少改进方法,然而目前已有的算法大多是考虑到光谱数据的宏观上的空间和波段维上的特性,都没有去挖掘光谱内在特性和物质光谱的物理产生机理。本论文从物质分子光谱的产生机制和光谱内在特性出发,引入了红外光谱中常见的一个吸光度曲线的介绍及其曲线特点,根据高光谱数据光谱特征的反射率曲线与吸光度曲线之间比较明显的转换关系,引入了相应的光谱曲线特性,并将此特性作为惩罚项融入了非负矩阵分解的目标函数,提出了相应改进算法,接着用模拟数据和真实高光谱数据所做的一系列实验也验证了算法的可行性和先进性。
Other AbstractIn the past three decades, with the imaging spectroscopy technology unceasing development, the remote sensing images obtained by imaging spectrometer aboard the plane or satellite platforms contain more and more rich information about space, radiation and spectrum, and thus provide a powerful means for surface material information extraction and target detection. The most significant characteristic of hyperspectral remote sensing data is its fusion of the spatial information and spectral information, it enables us to acquire the ground surface spatial image and get the corresponding continuous spectral information at the same time, thus realize the information inversion and geophysics recognition based on information of object spectrum. Hyperspectral technology has shown superiority and importance in the fields such as crop yield estimation, resource survey and so on, but one problem it faces with is the mixed pixels. The spatial resolution and spectral resolution is always a pair of contradictory for a set of optical remote sensor. Hyperspectral unmixing is to decompose the mixed pixel into “basic components”, which can also be called “endmembers”. In this way, the remote sensing application could reach the sub-pixel level. In recent years the unsupervised algorithm has been applied to hyperspectral unmixing and been paid more and more attention. Among this kind of method, the one based on the model of nonnegative matrix factorization has shown great performance, and some constraints have been introduced into this algorithm to render better estimates. Most constraints in this respect only paid attention to the hyperspectral data cube’s macroscopic spatial and spectral properties and did not deeply excavate the inner characteristic of the spectrum itself and the physical mechanism of the spectrum's formation. This thesis takes the theoretical basis of material molecular spectroscopy and inner property of the spectrum as the starting point, and then introduces absorbance curve which is common in infrared spectroscopy and also presents the intrinsic properties of this curve. Based on the obvious transformation relation between spectral reflectance and its relevant absorbance curve, a characteristic of spectrum is introduced, that is the sparseness of the spectrum’s Gabor transform coefficients. We corporate this sparisity-constraint into the cost function and propose a novel algorithm. Results obtained with synthetic and real data are used to...
Other Identifier200828009029019
Document Type学位论文
Recommended Citation
GB/T 7714
汪梦欣. 利用盲信号分离的高光谱图像解混技术[D]. 中国科学院自动化研究所. 中国科学院研究生院,2011.
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