提出一种基于低秩表示和学习字典的高光谱遥感图像异常探测算法.相对于其它低秩矩阵分解方法如鲁棒主成分分析, 低秩表示方法更为契合高光谱图像的线性混合模型.该算法将低秩表示模型应用到高光谱图像异常探测问题上来, 引入表征背景信息的学习字典, 大大增强了低秩表示模型对初始参数的鲁棒性.仿真和实际高光谱数据的实验结果表明, 所提出的算法有效地提高了异常的探测率, 同时对初始参数具有较好的鲁棒性, 可以作为一种解决高光谱图像异常探测的有效手段.

A geneeralized constant false alarm rate (CFAR) detection algorithm is developed for detecting the presence of an optical signal of non-zero intensity in J signal-plus-noise bands or channels. For many applications this new algorithm is more flexible and practical than previous ones. If J = 1, the resulting test reduces to the standard normalized matched filter test for finding a signal in clutter of unknown and varying intensity. Both theoretical and computer simulation results show that the SNR improvement gain of this new algorithm using multiple band scenes over the single scene of maximum SNR is always greater than one and in some cases it can be substantial. The database used to simulate this new adaptive CFAR test are actual Landsat image scenes. The present results for optical detection are extendable to radar target detection and to other related detection problems.

Anomaly detection is an important task for hyperspectral data exploitation. A standard approach for anomaly detection in the literature is the method developed by Reed and Xiaoli, also called RX algorithm. A variation of this algorithm consists of applying the same concept to a local sliding window centered around each image pixel. The computational cost is very high for RX algorithm and it strongly increases for its local versions. However, current advances in high performance computing help to reduce the run-time of these algorithms. So, for the standard RX, it is possible to achieve a processing time similar to the data acquisition time and to increase the practical interest for its local versions. In this paper, we discuss several optimizations which exploit different forms of acceleration for these algorithms. First, we explain how the calculation of the correlation matrix and its inverse can be accelerated through optimization techniques based on the properties of these particular matrices and the efficient use of linear algebra libraries. Second, we describe parallel implementations of the RX algorithm, optimized for multicore platforms. These are well-known, inexpensive and widely available high performance computing platforms. The ability to detect anomalies of the global and local versions of RX is explored using a wide set of experiments, using both synthetic and real data, which are used for comparing the optimized versions of the global and local RX algorithms in terms of anomaly detection accuracy and computational efficiency. The synthetic images have been generated under different noise conditions and anomalous features. The two real scenes used in the experiments are a hyperspectral data set collected by NASA's Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) system over the World Trade Center (WTC) in New York, five days after the terrorist attacks, and another data set collected by the HYperspectral Digital Image Collection Experiment (HYDICE). Experimental results indicate that the proposed optimizations can significantly improve the performance of the considered algorithms without reducing their anomaly detection accuracy.

p/ pWe present an unsupervised anomaly detection method for hyperspectral imagery (HSI) based on data characteristics inherit in HSI. A locally adaptive technique of iteratively refining the well-known RX detector (LAIRX) is developed. The technique is motivated by the need for better first- and second-order statistic estimation via avoidance of anomaly presence. Overall, experiments show favorable Receiver Operating Characteristic (ROC) curves when compared to a global anomaly detector based upon the Support Vector Data Description (SVDD) algorithm, the conventional RX detector, and decomposed versions of the LAIRX detector. Furthermore, the utilization of parallel and distributed processing allows fast processing time making LAIRX applicable in an operational setting./p

Anomaly detection is an active topic in hyperspectral imaging, with many practical applications. Reed-Xiaoli detector (RXD), a widely used method for anomaly detection, uses the covariance matrix and mean vector to represent background signals, assuming that the background information adjusts to a multivariate normal distribution. However, in general, real images present very complex backgrounds. As a result, in many situations, the background information cannot be properly modeled. An important reason is that that background samples often contain also anomalous pixels and noise, which lead to a high false alarm rate. Therefore, the characterization of the background is essential for successful anomaly detection. In this paper, we develop two novel approaches: weighted-RXD (W-RXD) and linear filter-based RXD (LF-RXD) aimed at improving background in RXD-based anomaly detection. By reducing the weight of the anomalous pixels or noise signals and increasing the weight of the background samples, W-RXD can provide better estimations of the background information. In turn, LF-RXD uses the probability of each pixel as background to filter wrong anomalous or noisy instances. Our experimental results, intended to analyze the performance of the newly developed anomaly detectors, indicate that the proposed approaches achieve good performance when compared with other classic approaches for anomaly detection in the literature.

We present a nonlinear version of the well-known anomaly detection method referred to as the RX-algorithm. Extending this algorithm to a feature space associated with the original input space via a certain nonlinear mapping function can provide a nonlinear version of the RX-algorithm. This nonlinear RX-algorithm, referred to as the kernel RX-algorithm, is basically intractable mainly due to the high dimensionality of the feature space produced by the nonlinear mapping function. However, in this paper it is shown that the kernel RX-algorithm can easily be implemented by kernelizing the RX-algorithm in the feature space in terms of kernels that implicitly compute dot products in the feature space. Improved performance of the kernel RX-algorithm over the conventional RX-algorithm is shown by testing several hyperspectral imagery for military target and mine detection.

高光谱遥感是对地观测的重要手段,高光谱图像处理与信息提取技术则是高光谱遥感领域的核心研究内容之一。本文简要介绍了高光谱遥感的主要特点,系统梳理了高光谱图像处理与信息提取面临的关键问题和主要研究方向,在此基础上,从噪声评估与数据降维方法、混合像元分解方法、图像分类方法、目标探测与异常探测方法等4个方面对高光谱图像处理与信息提取的理论发展过程和最新前沿进展进行了综述。另外,还对高光谱图像处理与信息提取中的高性能处理技术进行了总结和分析。未来,伴随着智能化信息分析和高性能硬件处理技术发展,高光谱遥感卫星系统也将步入智能化时代。针对这一趋势,本文指出高光谱图像处理与信息提取方法要注重多学科交叉,充分利用机器学习、人工智能等领域的新成果;要重视软硬件结合,发展高光谱图像高性能实时处理技术;要紧密结合应用需求,发挥高光谱遥感的优势和特点,发展新理论和新方法。

A novel method for anomaly detection in hyperspectral images (HSIs) is proposed based on low-rank and sparse representation. The proposed method is based on the separation of the background and the anomalies in the observed data. Since each pixel in the background can be approximately represented by a background dictionary and the representation coefficients of all pixels form a low-rank matrix, a low-rank representation is used to model the background part. To better characterize each pixel's local representation, a sparsity-inducing regularization term is added to the representation coefficients. Moreover, a dictionary construction strategy is adopted to make the dictionary more stable and discriminative. Then, the anomalies are determined by the response of the residual matrix. An important advantage of the proposed algorithm is that it combines the global and local structure in the HSI. Experimental results have been conducted using both simulated and real data sets. These experiments indicate that our algorithm achieves very promising anomaly detection performance.

In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l(2)-norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed-Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low computational cost.

传统高光谱异常探测器的背景统计信息易受异常目标干扰，鲁棒性较差，且难以探测非线性混合的异常目标.针对此问题，运用核特征投影理论，在异常探测器的背景信息构建中引入鲁棒性分析方法，提出了一种在核特征空间中具有鲁棒性的异常探测方法.该方法可以在不需要确定具体的非线性映射函数下，将高光谱数据从低维空间映射到高维特征空间，背景和目标在特征空间中可以用线性模型表示，并在特征空间中构造鲁棒性的探测器.该方法揭示了地物光谱间的高阶特性，可以较好地反映地物分布复杂的目标光谱特性.通过高光谱真实影像和模拟数据的实验证明：1)本文提出的异常探测方法具有更优的受试者工作特征曲线和曲线下面积统计值，目标和背景的分离度更大；2)在核特征空间内，排除异常目标对背景统计信息的干扰，有助于进一步提高探测准确度；3)特征提取可以更好地利用目标和背景的光谱区分性，是异常探测的重要步骤.

提出了一种基于张量学习机的遥感影像目标探测方法.该方法基于张量数据模型和张量代数运算,针对遥感影像数据多维或高维的特点,将基于向量的监督法学习机扩展为基于张量的监督法学习机,然后利用凸函数最优化理论和交互投影迭代法求得张量学习机的最优解.最后分别以高光谱遥感影像和高分辨率遥感影像为例,使用张量学习机进行目标探测.实验表明,与支持向量机等方法相比,本文的方法在保持较高探测成功率的同时更好的抑制了虚警.

传统的实时异常探测算法需对高维的背景样本统计矩阵进行求逆运算,数值稳定性差、时间复杂度高。而基于Cholesky分解,将高维矩阵的求逆运算转换为求解下三角线性系统,采用Cholesky分解因子的一阶修正方法快速更新背景统计信息,降低逐像元实时处理的时间复杂度并且保持数值稳定性。由于算法仅涉及下三角矩阵的更新过程,压缩了数据存储空间,适用于机载或星上实时处理。采用3维接收器曲线（3D-ROC）以及计算机实际处理时间对实验结果进行定量化分析,结果表明,该算法在不降低异常探测精度的同时,对当前时刻像元的实时处理时间约缩短为基于QR分解算法的0.4%—0.65%,或减少至基于Woodbury矩阵引理算法的27%—33%,有效提高实时高光谱异常探测器的计算性能,并且保持处理过程中的数值稳定性。

This letter discusses the robustness issue of kernel principal component analysis. A class of new robust procedures is proposed based on eigenvalue decomposition of weighted covariance. The proposed procedures will place less weight on deviant patterns and thus be more resistant to data contamination and model deviation. Theoretical influence functions are derived, and numerical examples are presented as well. Both theoretical and numerical results indicate that the proposed robust method outperforms the conventional approach in the sense of being less sensitive to outliers. Our robust method and results also apply to functional principal component analysis.

In this paper, we address the subspace clustering problem. Given a set of data samples (vectors) approximately drawn from a union of multiple subspaces, our goal is to cluster the samples into their respective subspaces and remove possible outliers as well. To this end, we propose a novel objective function named Low-Rank Representation (LRR), which seeks the lowest rank representation among all the candidates that can represent the data samples as linear combinations of the bases in a given dictionary. It is shown that the convex program associated with LRR solves the subspace clustering problem in the following sense: When the data is clean, we prove that LRR exactly recovers the true subspace structures; when the data are contaminated by outliers, we prove that under certain conditions LRR can exactly recover the row space of the original data and detect the outlier as well; for data corrupted by arbitrary sparse errors, LRR can also approximately recover the row space with theoretical guarantees. Since the subspace membership is provably determined by the row space, these further imply that LRR can perform robust subspace clustering and error correction in an efficient and effective way.

Kernel principal component analysis (kernel-PCA) is an elegant nonlinear extension of one of the most used data analysis and dimensionality reduction techniques, the principal component analysis. In this paper, we propose an online algorithm for kernel-PCA. To this end, we examine a kernel-based version of Oja's rule, initially put forward to extract a linear principal axe. As with most kernel-based machines, the model order equals the number of available observations. To provide an online scheme, we propose to control the model order. We discuss theoretical results, such as an upper bound on the error of approximating the principal functions with the reduced-order model. We derive a recursive algorithm to discover the first principal axis, and extend it to multiple axes. Experimental results demonstrate the effectiveness of the proposed approach, both on synthetic data set and on images of handwritten digits, with comparison to classical kernel-PCA and iterative kernel-PCA.

In hyperspectral image analysis, the principal components analysis (PCA) and the maximum noise fraction (MNF) are most commonly used techniques for dimensionality reduction (DR), referred to as PCA-DR and MNF-DR, respectively. The criteria used by the PCA-DR and the MNF-DR are data variance and signal-to-noise ratio (SNR) which are designed to measure data second-order statistics. This paper presents an independent component analysis (ICA) approach to DR, to be called ICA-DR which uses mutual information as a criterion to measure data statistical independency that exceeds second-order statistics. As a result, the ICA-DR can capture information that cannot be retained or preserved by second-order statistics-based DR techniques. In order for the ICA-DR to perform effectively, the virtual dimensionality (VD) is introduced to estimate number of dimensions needed to be retained as opposed to the energy percentage that has been used by the PCA-DR and MNF-DR to determine energies contributed by signal sources and noise. Since there is no prioritization among components generated by the ICA-DR due to the use of random initial projection vectors, we further develop criteria and algorithms to measure the significance of information contained in each of ICA-generated components for component prioritization. Finally, a comparative study and analysis is conducted among the three DR techniques, PCA-DR, MNF-DR, and ICA-DR in two applications, endmember extraction and data compression where the proposed ICA-DR has been shown to provide advantages over the PCA-DR and MNF-DR.

The singular value decomposition is among the most important tools in numerical analysis for solving a wide scope of approximation problems in signal processing, model reduction, system identification and data compression. Nevertheless, there is no straightforward generalization of the algebraic concepts underlying the classical singular values and singular value decompositions to multilinear functions. Motivated by the problem of lower rank approximations of tensors, this paper develops a notion of singular values for arbitrary multilinear mappings. We provide bounds on the error between a tensor and its optimal lower rank approximation. Conceptual algorithms are proposed to compute singular value decompositions of tensors.

We present an efficient approach to forming feature maps. The method involves three stages. In the first stage, we use the K-means algorithm to select N2 (i.e., the size of the feature map to be formed) cluster centers from a data set. Then a heuristic assignment strategy is employed to organize the N2 selected data points into an N x N neural array so as to form an initial feature map. If the initial map is not good enough, then it will be fine-tuned by the traditional Kohonen self-organizing feature map (SOM) algorithm under a fast cooling regime in the third stage. By our three-stage method, a topologically ordered feature map would be formed very quickly instead of requiring a huge amount of iterations to fine-tune the weights toward the density distribution of the data points, which usually happened in the conventional SOM algorithm. Three data sets are utilized to illustrate the proposed method.

<p>介绍了基于聚类分析的自组织特征映射神经网络分类方法,神经网络的输出层结构选用了3D结构,可以更好地保持多波段遥感数据中的内在拓扑结构;并选择天津大港地区的ASTER数据中的9个波段作为试验数据,通过对验证点的统计,分类精度达到了94%以上。</p>

基于最新的非线性降维方法——流形学习的理论,从高光谱遥感数据内在的非线性结构出发,采用全局化的等距映射(Isomap)方法进行降维,取得了优于常用的MNF方法的结果。把光谱角和光谱信息散度与测地距离相结合用于Isomap算法,结果在冗余方差和光谱规范化特征值方面优于采用传统欧氏距离计算邻域的Isomap方法。实验表明,流形学习是一种有效的高光谱遥感数据特征提取方法。

环境一号卫星（HJ-1）CCD影像光谱波段较少，地物之间的准确分类识别有一定困难。采用分形纹理辅助地物分类识别是一种有效方法，而波段选择是提高分类识别精度的关键。本文以江西赣州定南县土地利用分类为例，采用双毯覆盖模型对HJ卫星CCD影像6类典型地物的波谱分形特征进行了分析，利用不同地物在不同波段上的分形区分度差异构建了最佳分形波段选择模型，并利用该模型挑选出最佳分形波段来辅助土地利用分类，最后对分类结果进行检验。结果表明：最佳分形波段选择模型能够综合权衡不同地物在不同波段上的分形区分度差异，利用挑选出来的最佳分形波段来辅助分类，其分类总体精度相对于原始影像分类提高了11.77%，相对于第1主成分分形辅助下的分类提高了1.56%。

利用蚁群优化算法解决特征选择问题,以获得能代表问题空间的较优特征子集,并能降低分类系统的搜索空间。以航空纹理影像的特征选择和分类问题为例,利用主分量变换和蚁群优化算法分别对原始纹理影像特征集合进行特征提取、选择和分类。结果表明,本文方法不仅能够降低图像特征空间维数,减少图像分类的工作量,而且还可以提高分类识别的正确率。