【作者】 梅锋；

【导师】 赵春晖；

【作者基本信息】 哈尔滨工程大学 ， 信号与信息处理， 2009， 博士

【摘要】 高光谱遥感图像具有很高的光谱分辨率,是一种图谱合一的新型遥感数据。借助其丰富的光谱信息,可以反映目标间的细微差异,使人们可以发现用纹理、边缘等空间特征无法或难以探测的地面目标,这对于目标检测提供了有利的支持。异常检测算法能够在没有先验光谱信息的情况下检测到与周围环境存在光谱差异的目标,具有较强的实用性,成为了高光谱目标检测领域的一个研究热点。由于高光谱图像的高数据维,波段间非线性相关性,混合象素,同物异谱等特点,使得传统线性高光谱异常检测算法的检测性受到影响。核方法是处理非线性信息的有效方法,已得到广泛应用。基于核方法的非线性高光谱异常检测方法仍存在很多不足与有待解决的问题。本文以高光谱异常目标检测技术为研究对象,以核机器学习为方法,以改善和提高高光谱异常目标检测性能为目的,重点解决基于核机器学习的非线性高光谱异常目标检测算法中存在的问题。本文的主要创新点与研究成果如下：首先,针对传统高光谱数据特征提取(或降维)方法仅仅利用了高光谱波段间的线性相关信息,损失了波段间非线性信息的问题,提出了基于非线性独立特征提取的高光谱异常检测算法。通过核主成分分析完成数据白化后在特征空间进行独立特征提取,针对高光谱异常检测的特殊性和非线性独立特征的随机性问题,提出了基于局部负熵度量的非线性独立特征优化选择方法,通过选择具有最大局部负熵值的非线性独立特征,有效地提取出适合高光谱异常检测的特征,进而采用RX算子进行高光谱异常检测,很好的抑制了虚警概率。其次,针对核RX算法中因背景数据中混入异常点而造成的背景核矩阵退化,从而使得漏检率上升,检测性能下降的问题,提出一种基于空域滤波的核RX算法。考虑到高光谱图像图谱合一的性质,其同一波段相邻像素点在空间上具有很强相关性以及不同波段的对应像素为相同地物的不同波长辐射响应的特点,提出了分波段空域滤波的方法来优化背景数据分布,降低了异常数据对背景核矩阵的影响,使得背景核矩阵能更好的描述实际的背景分布状态,进而提高了检测概率。再次,针对基于核方法的多数高光谱异常检测算法中的高斯径向基核函数核参数估计困难问题,提出了自适应的核参数估计方法,形成了基于核方法的自适应异常检测算法。由于核参数的选择对于算法的性能影响较大,而传统方法多采用大量实验人为选择,增加了工作量且不能得到客观的优化结果。通过局部背景分波段二阶分布统计,分析核参数与局部背景总体标准差的变化关系,构造随检测背景变化的局部检测核参数,使得检测算法针对不同背景分布自适应地调整检测核参数,克服了传统采用固定核参数带来的复杂背景下检测性能下降的问题。最后,针对现有的基于核方法的高光谱异常检测算法在核函数的选择上过于单调(大多采用高斯径向基核函数)的问题,提出了一种全新的光谱相似度量核函数,并将其应用于高光谱异常检测。针对高斯径向基核的局部适应性强但对于光谱曲线变化分辨力差的弱点,利用光谱曲线相似性度量方式提出了光谱相似度量核函数。对提出的光谱相似度量核作为核函数的确定性进行了理论证明,并推导出了光谱相似度量核的平移不变性质。比较了光谱相似度量核与高斯径向基核对高光谱数据区分能力。通过理论分析与实验验证,结果说明在高光谱异常检测中,光谱相似度量核具有很强的光谱变化分辨能力,能够提高目标尤其是亚像素小目标的检测概率。同时还针对光谱形似度量核对于光谱形状变化敏感性带来的虚警概率较高的问题,结合光谱相似度量核与高斯径向基核函数,形成了混合核函数,同时利用二者的优势,在保证检测概率的情况下降低了虚警概率。更多还原

【Abstract】 Hyperspectral data as a combination of high spectral resolution and two-dimensional image is a new type of remote sensing data. Utilizing the abundance spectral information to distinguish the tiny difference among ground cover, we can detect the targets which can not be detected by texture, edge in the image space. That is very useful for target detection. In spectral anomaly detection algorithms, materials that have a significantly different spectral signature from their surrounding background clutter pixels are identified as spectral anomalies. In spectral anomaly detectors, no prior knowledge of the target spectral signature is utilized or assumed. So it is a focus in the hyperspectral target detection area. The classical linear anomaly detectors are of poor performance due to the high dimensionality, nonlinear correlation between spectral bands, mixed pixels, spectral variety in hyperspectral imagery. Kernel method is regarded as an effective method to processing nonlinear information and is widely used in many research areas. However, there are some shortages and unsolved problems in kernel machine learning based anomaly detection methods in hyperspectral imagery. This dissertation considers anomaly detection technique as the research object, kernel machine learning as the method, improving the performance of anomaly detection algorithms as the goal, and dealing with the problems endured in kernel based nonlinear anomaly detection methods in hyperspectral imagery. The main innovation contributions of this dissertation are as follows.Firstly, in the traditional dimensionality reduction methods, only linear information between spectral bands is used and nonlinear information being wasted. For this problem, a nonlinear independent feature extraction method is proposed and applied to anomaly detection in hyperspectral imagery. With the input data is mapped into an implicit feature space, kernel principal component analysis (KPCA) is performed to whiten data and fully mine the nonlinear information between spectral bands. Then, independent component analysis (ICA) seeks the projection directions in the whitened feature space for making the distribution of the projected data mutually independent. As the nonlinear independent features are extracted randomly, in order to select the best feature for anomaly detection, a local negentropy measurement (LNM) method is proposed for nonlinear independent features selection. After nonlinear independent features ranking by LNM, the nonlinear independent feature with the max LNM score is used for RX anomaly detection. The method suppresses the false alarm rate and improves the performance of anomaly detector.Secondly, the kernel RX detector is of good nonlinear anomaly detection capability, but the degeneration of the background kernel matrix due to the background data blurred by anomaly samples lead to a high miss rate. In this dissertation, a spatial filter based kernel RX anomaly detection algorithm in hyperspectral imagery is proposed. Utilizing the spatial correlation of pixels in a hyperspectral band, the background data is optimized by depressing the anomaly data using spatial filter in every spectral band. The background kernel matrix after spatial filter is a better distribution representation of the real background data. Using this method, the detection probability of kernel RX detector is increased obviously in hyperspectral imagery.Thirdly, Gaussian radial basis function (RBF) kernel is used in most of the kernel base anomaly detection algorithms, but the width factor (or kernel parameter) selection for RBF is very difficult. In most of these algorithms, the kernel parameter is obtained through a large number of experiments. It is time consumed, heavy workload, and can not obtain the optimal kernel parameter objectively. For the problem an adaptive kernel parameter estimation method is proposed. Based on the relationship between kernel parameter and the local second-order statistic information in hyperspectral data, an adaptive kernel parameter estimation method is derived. The parameter estimation of kernel function can be obtained along with the shifting of the background clutter pixels automatically. The degeneration of detection performance brought by a global fixed kernel parameter method in a background of miscellaneous terrain is improved by the proposed algorithm. Numerical experiments are conducted and the results show that the detection probability of the proposed algorithm is better than the classical fixed kernel parameter method at the same false alarm rates. Finally, for the problem of the kernel function scarce in kernel base anomaly detection methods (RBF is selected in most algorithms) in hyperspectral imagery, a novel spectral similarity measurement kernel function is proposed and applied to anomaly detection in hyperspectral imagery. As the RBF is based on the Euclidean distance of two spectral vectors, it is sensitive for distance variations of two spectral vectors, but not for spectral curve variation. In order to dealing with the spectral curves variation of the same materials, a spectral similarity measurement kernel function is proposed according to the spectral curves similarity description. A theoretical analysis is expounded and the shift invariance property of spectral similarity measurement kernel is derived. Numerical experiments are conducted on real hyperspectral imagery. The detection result comparison of Gaussian radial basis function based and spectral similarity measurement (SSM) kernel based anomaly detector shows the SSM kernel can improve the performance of kernel base anomaly detection methods in hyperspectral imagery. Especially for small sub-pixel targets detection, the SSM kernel is of obvious superiority comparing with the RBF kernel. To solve the false alarm rate rising due to the sensitivity of SSM kernel for spectral curve diversification, RBF kernel and SSM kernel are composite to utilize the advantage of each other. Using the composite kernel, the results of numerical experiments show the false alarm rate declines at the same detection probability comparing to solo SSM kernel used.更多还原