【作者】 杜博；

【导师】 张良培； 李平湘；

【作者基本信息】 武汉大学 ， 摄影测量与遥感， 2010， 博士

【摘要】 高光谱遥感影像具有光谱分辨率高、图谱合一的特点,可以提供区分不同物质的诊断性光谱信息,因此在地物目标探测领域具有独特的优势。由于地物分布情况复杂、传感器空间分辨率的限制、目标数目少尺寸小等原因,待探测目标通常与其他地物共同组成混合像元,这时目标以信号比较弱小的亚像元形式存在,问题转化为在像元内部确定目标信号存在性的亚像元小目标探测问题。高光谱遥感影像的亚像元小目标探测问题是高光谱信息处理的前沿问题,也是制约高光谱影像应用的难点问题。本文针对高光谱影像亚像元小目标探测中存在的数据维数较大：目标信号的光谱变化现象严重；目标对背景统计信息的干扰等问题进行深入研究,提出了多种亚像元目标探测方法。具体地,本文的主要研究工作可以概括为：(1)总结了信号检测理论的一般规律,及其在高光谱遥感影像目标探测问题中的应用,推导并分析了三类在高光谱遥感影像目标探测中应用广泛的探测理论：基于广义似然比理论探测器、恒虚警率探测器和匹配滤波器探测方法；总结了线性混合模型反映地物信息方面的物理含义、表达形式和主要局限等,研究了基于光谱分解的目标探测方法,推导了基于主动/被动子集合方法的全约束最小二乘数值解法,通过实验指出了基于光谱分解目标探测方法的缺陷。(2)提出一种基于最小噪声分离变换的约束能量最小化探测方法,在降低维数的同时分离噪声、保持目标与背景光谱差异,以克服因高光谱遥感数据维数大造成的探测速度过慢问题。实验结果表明,与传统的约束能量最小化方法相比,该方法探测亚像元目标效果更好、运算速度更快。(3)提出一种基于光谱分解的自适应匹配子空间探测方法,将基于光谱分解的组分信息替代子空间参数估计的结果,利用具有物理意义的端元和组分信息建立目标与背景子空间,进而构造子空间匹配探测器,利用子空间策略克服目标光谱变化问题、使用结构化背景提高探测精度。实验证明,与自适应匹配子空间探测器等方法相比,该方法可以更好地表达影像的背景信息、提高目标与背景的可分离度。(4)提出一种基于局域信息的正交子空间投影探测方法,利用局域内的相邻像元构建正交投影算子,与传统正交子空间投影方法中使用影像全局的端元构造投影算子相区别。实验中发现,基于局域信息的正交子空间投影方法与传统正交子空间方法相比,投影后目标相对背景的值域更大,这反映出：利用目标周边局域内的像元作为基本元素构建正交子空间,可以更好地反映目标与背景的差异；此外实验还表明,基于光谱分解的自适应匹配子空间探测方法,与正交子空间投影方法、基于局域信息的正交子空间投影方法、和自适应匹配子空间探测方法相比,探测结果更优。(4)提出一种背景非结构化混合探测器,利用统计分布特征表示背景信息,结合光谱分解结果构建广义似然比探测算子。此外,为了克服传统探测器对所有像元的端元数目保持不变的缺陷,引入端元可变的策略判断待探测像元中包含背景端元的种类,分别提出一种基于端元可变的背景结构化混合探测器和一种基于端元可变的背景非结构化混合探测器,提高了光谱分解精度以及背景信息表征的精度。分别利用ROC曲线等评价指标对加入端元可变策略后探测器分离目标和背景效果、对背景端元估计偏差的敏感度等情况进行比较和分析,实验结果验证了端元可变策略的有效性,这也证明了通过提高表征背景精度的方式可以提高亚像元目标的探测效果。(5)提出一种基于随机采样的异常目标探测方法,在缺乏先验目标信息的条件下,解决目标探测问题。该方法利用多个局域位置选取的像元作为背景信息,结合局域特征和全局特征,通过多次选取背景信息来建立不同的探测算子,最后融合多次探测结果,利用异常目标呈现异常次数最多的特点,确定异常目标。实验证明,该方法可以较好地抑制目标对背景信息的干扰,提高目标与背景的可分度。此外,还提出了一种实时探测的随机采样异常目标探测方法,实验证明其可以减少不同背景交界处的虚警数目。更多还原

【Abstract】 Imaging spectrometer can provide images with very high spectral resolution by collecting many spectrally continuous images, named hyperspectral images, at the same time. Different materials are distinguishable by the spectral difference revealed in hyperspectral images. The spatial pattern of the images is also helpful to position the potential target with spectral difference from the background. These characteristics make hyperspectral images suitable for target detection task. However, due to the complex distribution of different ground objects and the limited spatial resolution of the hyperspectral images, a pixel in the hyperspectral images is usually composed of different land objects and the targets usually reside in the sub-pixel scale. Sub-pixle target detection is a difficulty for extracting information from hyperspectral images and has been of great interest to the researchers in target detection domain since the end of last century. This thesis focuses on the following problems in sub-pixel target detection from hyperspectral images:detection with low dimension data, target spectral variety, contamination caused by targets to the background statistics.The main research work and the corresponding contributions of this dissertation are as following:Target detection in hyperspectral images originates from the singal detection theory. So the singal processing theory is summarized. The signals received by the imaging spectrometer come from the array of multiple senors acquiring different radiances from the same ground scene. This is the array signal processing in hyperspectral images. Besides, the three most important methods for constructing the target detectors are detailed:generalized likelihood ratio test, constant false alarm rate detector and matched detector theory.Linear mixture model is the most widely used model for target detection in hyperspectral images. The physical basis, the description and the limitation of the model are summarized. Based on the linear mixture model, the linear spectral unmixing based target detection method is introduced. The fully constrained least squares method is used to solve the problem and an active/passive sets based numerical calculation method is researched on. Experiments show the limitation of the method. Besides, a constrained energy minimization method based on minimum noise fraction transformation (MNF-CEM) is proposed. In this method, minimum noise fraction (MNF) reduces the dimension of the hyperspectral imageries and separates the noise from the hyperspectral images. Then a finite impulse response (FIR) filter is used to detect the sub-pixel targets in the low dimension images. In this way, the computations of ill-conditional matrix inverse and virtual dimension of the hyperspectral imageries are unnecessary. Experiments show that this method can restrain the influence of the noise and is an effective sub-pixel target detection method for hyperspectral imageries.Subspace based methods can solve the spectral variety problem. Two subspace based detection methods are proposed in this paper. Traditional matched subspace detectors rely on the estimated endmembers and the according abundance to model the background and construct a generalized likelihood ratio test based detector. However, these endmembers and abundances have no physical meanings. So we use the abundances from the spectral unmixing so as to introduce the physically meaningful endmembers and abundances into the matched subspace detector. The proposed method is called spectral unmixing based matched subspace detector. The other method is the local OSP (Orthogonal Subspace Projection) method which uses the local neighbor pixels to represent the background information of the pixels under observation instead of the endmembers from the whole image. Experiments show that with physically meaningful endmembers and the according abundances the proposed matched subspace detector models the background better and performs better than its counterpart detector and the other OSP detectors; local OSP output a better result than OSP which proves that the pixel is related more with the local information than the whole set of pixels in the image, so it is more suitable to model the pixel’s background with its neighboring pixels.To further investigate the performance with more accurate background information, two hybrid detectors are proposed. Both of them make use of abundances from spectral unmixing, and a background endmembers selection procedure is introduced into the detectors. The aim of the background endmembers selection is to use the correct kinds of background endmembers in the detection, while traditional methods use the same endmembers in the detection of each pixel. A structured background hybrid detector and an unstructured background hybrid detector are developed. First, the endmembers selection procedure is performed to choose the correct kinds of background endmembers in each pixel. Second, spectral unmixing is done with the endmembers selection information to get more accurate abundances. Then, the abundances are used to construct the structured background hybrid detector and the unstructured background hybrid detector and the endmembers selection information is taken into consideration in the structured detector again. Experiments show that the hybrid detectors with endmembers selection procedure perform better than the hybrid ones without endmembers selection procedure. It is concluded that with more accurate information to model the reality of the background ground objects the detection performance would be improved.Current anomaly detection methods are susceptiable to the anomalies in the background statistics. And they either use a local background statistics or a global statistics. A random selection based anomaly detector is proposed in this thesis. The method uses a random selection procedure to get background statistics from the image. Eachtime, not only the local neighboring pixles but also a number of blocks containting the same number of pixels are selected from random positions of the image. In this way, both local and global statistics of the pixel under observation are taken into consideration. The selection procedure is performed several times and each time the detection procedure is done successively with the constructed background statistics. Finally, the detection results are confused. Besides, the real-time version of the random selection based detector is implemented. Experiments show that the separability between targets and background by the proposed random selection based anomaly detector is larger than that by SSRX detector. And the performance of the real-time version detector also shows better detection and less false alarms on the boundaries of different backgrounds than current real-time anomaly detectors..更多还原