【作者】 刘雪松；

【导师】 王斌；

【作者基本信息】 复旦大学 ， 电路与系统， 2011， 硕士

【摘要】 由于遥感拍摄仪器空间分辨率的限制,高光谱遥感图像中的一个像元对应的地面区域通常覆盖了多种地物,其像元的光谱实际上是几种纯净地物光谱的混合,这种像元被称为混合像元。将混合像元分解为典型的地物(即端元)和它们的混合比例(即丰度),可以获取亚像元级的信息,提高地物识别的精度,实现定量遥感。因此,混合像元分解对于基于高光谱遥感图像的高精度地物分类以及地面目标的检测有着重要的意义。目前在该领域中,国际上不断有新思路新方法涌现。如何用无监督的方法在高光谱遥感数据中将混合像元更快更精确的进行盲分解,已经成为当今遥感领域里的一个研究热点。作为一种先进的统计学方法,非负矩阵分解方法能够保证结果的非负性,因而在近年来得到了广泛研究。然而它还存在局部极小很多和运算量大的缺点,本论文针对非负矩阵分解在高光谱遥感图像混合像元分解中的应用作了大量研究,主要工作与创新包括以下几部分：1.提出一种基于有约束非负矩阵分解的混合像元分解方法。传统非负矩阵分解算法的目标函数具有大量的局部极小,在进行高光谱图像的光谱解混时,受初始值的影响很大。本研究通过在目标函数中引入丰度分离性和平滑性的约束条件来解决这一问题。同时该算法能够满足混合像元分解问题所要求的丰度值非负以及和为一的约束。模拟和实际数据实验结果表明,所提出的算法能够很好地克服局部极小的问题,从而得到更优的解。同时该算法表现出了较强的抗噪声能力,并且能够适用于无纯像元数据的混合像元分解。2.针对非负矩阵分解运算速度较慢的问题,本文提出一种使用多核并行处理来提高其效率的方法。本方法对有约束非负矩阵分解方法的步骤进行分解,根据高光谱图像的特点选择合适的并行处理方案。实验结果表明,使用并行处理后,多核CPu的效能被充分利用,混合像元分解的速度得到了大幅度的提高。3.由于高光谱图像波段之间的相关性极高,而非负矩阵分解是一种统计学方法,当直接使用原始的数据时,势必会带来大量的冗余信息。基于这一问题,本文提出一种适用于非负矩阵分解的,基于最大信息量的高光谱图像波段选择方法。它以保留的波段中所包含的信息量最大为目标,通过迭代方法逐个移除波段。实验结果表明,该方法能在大幅降低高光谱图像维度,从而提高运算效率的同时,仍然得到精确的分解结果。4.通常对混合像元分解方法的实验研究只局限于数量较为有限的免费数据,它们大多是由美国研制的成像仪拍摄,并且年代较为久远。本文在保留这些数据的同时,也对由国产成像仪拍摄于近几年的高光谱图像进行了实验,以验证混合像元分解方法在实际应用中的有效性。更多还原

【Abstract】 Due to the resolution limitation of the sensors and the variability of the ground surface, the observation of one pixel in a hyperspectral remote sensing image may contain several disparate substances, causing it to be a "mixed pixel". In order to utilize the hyperspectral data, these mixed pixels have to be decomposed into a set of constituent spectra, called endmember signatures, and their corresponding proportions, called abundances. Mixed pixels exist in almost all hyperspectral images, thus how to unmix these pixels has become an important problem of hyperspectral imagery, for whose application in the identification and detection of ground targets. In recent years, new ideas and approaches for hyperspectral unmixing are springing up. Using unsupervised method to decompose mixed-pixels more efficiently and accurately has become a hotspot in the research area of remote sensing. As an advanced statistical approach, Nonnegative Matrix Factorization (NMF) has been frequently researched recently for hyperspectral unmixing, because it can ensure the nonnegativity of results. However, it has some disadvantages, such as large amount of local minima and high computational complexity. Focusing on the application of nonnegative matrix factorization in hyperspectral unmixing, this thesis has made a lot of research, and the main works and innovations are as follows:1. A constrained NMF approach has been proposed. Because of the local minima in the objective function, the traditional NMF algorithm is sensitive to the initial value when being applied to hyperspectral unmixing. In order to solve the problem, a new approach based on constrained NMF is proposed for decomposition of mixed pixels by introducing constraints of abundance separation and smoothness into the objective function of NMF. The algorithm can also satisfy the abundance nonnegative and sum-to-one constraints, which are necessary for hyperspectral unmixing. Experimental results on simulated and real hyperspectral data demonstrate that the proposed approach can overcome the shortcoming of local minima, and obtain better results. Meanwhile, the algorithm performs well for noisy data, and can also be used for the unmixing of hyperspectral data in which pure pixels do not exist.2. On account of the low computational speed of NMF, a multi-core parallel processing method is proposed to increase its efficiency. The method decomposes the constrained NMF algorithm to several blocks, and choose appropriate parallel strategy according to the features of hyperspectral imagery. Experimental results show that, the multi-core CPU is utilized sufficiently after the execution of parallel processing, and the computational speed of unmixing has got an obvious improvement.3. The correlation between neighboring bands of hyperspectral imagery is very high, while NMF is a statistical approach, thus a large amount of redundant information will be processed if the original data is used directly. In order to solve this problem, a maximum-information-based band selection approach for NMF is proposed. It aims at preserving the maximum amount of information, and removes bands one by one iteratively. Experimental results show that, the proposed approach can obviously reduce the number of dimensions of hyperspectral imagery and increase the processing efficiency, and obtain accurate results at the same time.4. Some general free datasets are usually used for the experimental research of hyperspectral unmixing. These datasets are mostly captured over a decade ago by U.S.-made devices. In this thesis, datasets lately obtained by China-made devices are also used for experiments, in order to evaluate the performance of algorithms for practical application.更多还原