基于核稀疏和空间约束的高光谱目标检测方法研究

发布时间：2017-12-27 18:37

  本文关键词：基于核稀疏和空间约束的高光谱目标检测方法研究　出处：《哈尔滨工业大学》2016年硕士论文　论文类型：学位论文

  更多相关文章： 高光谱目标检测 稀疏表示 核方法 空间信息 联合检测

【摘要】：高光谱遥感是一种能对地物进行精细观测的信息获取手段,高光谱图像的目标检测具有较高的研究价值。在军事应用方面,主要用于对重点目标进行检测与识别;在民用方面,高光谱目标检测在环境监测、矿产资源定位、精细农业等领域也有广泛的应用。论文主要针对高光谱数据的典型特征,在信号稀疏表示理论的基础上综合利用其蕴含的光谱信息和空间信息,实现高光谱图像的目标检测。论文首先从信号稀疏表示的基础理论起步,以信号稀疏表示的数学模型为前提,明确了使用范数松弛求解稀疏方程时数据应满足的条件,并给出求解稀疏方程的基本方法。后续分析高光谱图像具有的稀疏性、“图谱合一”等特征,将稀疏表示模型应用于高光谱图像数据,由信号的稀疏重构过渡到基于稀疏表示的高光谱目标检测方法,并给出基于实验数据的高光谱目标检测初步结果。其次,由于非线性和“图谱合一”是高光谱图像具有的重要特征,因此着重研究光谱与空间信息在高光谱目标检测中的优化利用方法。针对高光谱数据的非线性特征可能导致的线性目标检测算法的欠佳结果,使用核方法对基于稀疏表示的高光谱目标检测基本方法进行改进,使其具有处理非线性数据的能力,从而优化利用高光谱数据中的光谱信息。针对高光谱数据蕴含的空间信息,使用边缘描述和水平集描述对目标对象的空间特征进行提取,从而深入挖掘高光谱数据图像域信息,进而为后续的空间信息应用提供数据基础。最后,考虑高光谱数据中目标对象光谱信息和空间信息的统一性和整体性,建立光谱-空间信息联合稀疏模型,力求更为全面准确地描述目标对象。在联合稀疏模型的基础上,借助空间域信息辅助目标检测,提出了基于光谱信息的目标检测结果的修正算法。论文利用AVIRIS高光谱数据,研究了稀疏度和核参数对目标检测结果的影响。同时,由正常数据集衍生构建了弱完整性数据,并在正常数据集和弱完整性数据集上测试经典目标检测方法、基于稀疏表示的目标检测方法和基于光谱-空间信息联合稀疏模型的目标检测方法,现有数据条件下的实验结果表明,利用高光谱数据的光谱和空间信息的目标联合检测方法性能最为优越,对弱完整性数据的适应性也更强。
[Abstract]:Hyperspectral remote sensing (hyperspectral remote sensing) is a kind of information acquisition means for precise observation of ground objects. The target detection of hyperspectral images has high research value. In military applications, it is mainly used for detecting and identifying key targets. In civilian aspect, hyperspectral target detection is widely applied in environmental monitoring, mineral resource location, precision agriculture and other fields. Aiming at the typical characteristics of hyperspectral data, based on the theory of sparse representation, the paper uses the spectral information and spatial information contained in hyperspectral data to achieve target detection in hyperspectral images. Starting from the basic theory of sparse representation, the paper first defines the mathematical model of signal sparse representation, and specifies the condition that data should satisfy when using norm relaxation to solve sparse equations, and gives the basic method to solve sparse equations. Subsequent analysis of hyperspectral image is sparse, "one map" and other characteristics of the sparse representation model is applied to hyperspectral image data, hyperspectral target detection method based on sparse representation by sparse reconstruction signal is given to the transition, the preliminary results of hyperspectral target detection based on experimental data. Secondly, due to the nonlinear and "spectral unification" is the important feature of hyperspectral image. Therefore, the optimal utilization of spectral and spatial information in hyperspectral target detection is mainly studied. The poor results of linear target detection algorithm for the nonlinear characteristics of hyperspectral data may lead to the use of nuclear methods to improve the basic method of hyperspectral target detection based on sparse representation, which has the ability to deal with nonlinear data, so as to optimize the use of spectral information in hyperspectral data. Aiming at the spatial information contained in hyperspectral data, edge description and level set description are used to extract the spatial characteristics of target objects, so as to dig out hyperspectral data and image domain information, and provide data basis for subsequent spatial information application. Finally, considering the uniformity and integrity of the spectral and spatial information of target objects in hyperspectral data, we establish a joint sparse model of spectral spatial information, and strive to describe target objects more comprehensively and accurately. On the basis of joint sparse model and space domain information aided target detection, a correction algorithm for target detection results based on spectral information is proposed. Using AVIRIS hyperspectral data, the influence of sparsity and kernel parameters on target detection results is studied in this paper. At the same time, the construction of weak integrity data derived from the normal data set, and the normal data set and weak integrity data set test method, the classical sparse representation of the object detection method and detection method based on spectral spatial information combined with sparse model based on existing data conditions. The experimental results show that the target joint the detection method uses the spectral and spatial information of hyperspectral data is the most superior performance, the weak data integrity adaptable.
【学位授予单位】：哈尔滨工业大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TP751
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本文编号：1342738