Hyperspectral Imaging For Detection Research Articles

An Automatic Approach to Adaptive Local Background Estimation and Suppression in Hyperspectral Target Detection

This paper deals with subspace-based target detection in hyperspectral images. Specifically, it focuses on a general detection scheme where, first, background is suppressed through orthogonal-subspace projection and then target detection is accomplished. An adequate estimation of the background subspace is essential to a successful outcome. The background subspace has been typically estimated globally. However, global approaches may be ineffective for small-target-detection applications since they tend to overestimate the background interference affecting a given target. This may result in a low target residual energy after background suppression that is detrimental to detection performance. In this paper, we propose a novel and fully automatic algorithm for local background-subspace estimation (LBSE). Local background has typically a lower inherent complexity than that of global background. By estimating the background subspace over a local neighborhood of the test pixel, the resulting background-subspace dimension is expected to be low, thus resulting in a higher target residual energy after suppression which benefits the detection performance. Specifically, the proposed LBSE acts on a per-pixel basis, thus adaptively tailoring the estimated basis to the local complexity of background. Both simulated and real hyperspectral data are employed to investigate the detection-performance improvements offered by LBSE with respect to both global and local methodologies previously presented.

A Statistical Approach for Multiclass Target Detection

Fukunaga-Koontz Transform (FKT) is a statistical technique which has many application areas for two-class classification or detection problems. In this paper, we have proposed improved target detection algorithm for hyperspectral imagery (HSI) based on enhanced FKT which gives better results for multi-class target detection problems.Hyperspectral imagery is popular for target detection applications due to its additional properties compare to multispectral images. It presents one dimensional (spectral) and two dimensional (spatial) features of targets for detection problems. For multi-class target detection in hyperspectral images, we have selected each target's 1D features, called signature of targets, to introduce to enhanced FKT during learning stage. After learning stage of FKT, we have applied our operators, obtained by enhanced FKT, to HSI images to detect more than one target simultaneously.The experimental results show that multi-class FKT has satisfactory performance over 95 percent of true detection rate especially on the pixel sized targets. In addition, multi-class processing ability of the proposed enhanced FKT is very important for many applications such as classification, recognition, clustering, tracking problems in the literature.

Asymptotically CFAR-Unsupervised Target Detection and Discrimination in Hyperspectral Images With Anomalous-Component Pursuit

This paper addresses the problem of anomaly detection in hyperspectral images. We propose and exploit a data model to establish the link between two main approaches in the area of anomaly detection, which are the hypothesis testing (HT) and projection pursuit. We show that combining these two approaches enables one to overcome some limitations of each method when taken separately. Indeed, the resulting detection algorithm, namely, anomalous component pursuit (ACP) has an asymptotically constant false-alarm rate, like HT-based detectors, and enables anomaly spectral discrimination, including the estimation of the number of classes. We assess the ACP algorithm on real-world data, in terms of detection and discrimination, and discuss some theoretical limitations.

Clusters versus GPUs for Parallel Target and Anomaly Detection in Hyperspectral Images

Remotely sensed hyperspectral sensors provide image data containing rich information in both the spatial and the spectral domain, and this information can be used to address detection tasks in many applications. In many surveillance applications, the size of the objects (targets) searched for constitutes a very small fraction of the total search area and the spectral signatures associated to the targets are generally different from those of the background, hence the targets can be seen as anomalies. In hyperspectral imaging, many algorithms have been proposed for automatic target and anomaly detection. Given the dimensionality of hyperspectral scenes, these techniques can be time-consuming and difficult to apply in applications requiring real-time performance. In this paper, we develop several new parallel implementations of automatic target and anomaly detection algorithms. The proposed parallel algorithms are quantitatively evaluated using hyperspectral data collected by the 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 that collapsed the two main towers in the WTC complex.

Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images

Anomaly detection in hyperspectral images has proven valuable in many applications, such as hazardous material and mine detection. The benchmark anomaly detector is the Reed-Xiaoli (RX) detector, which is based on the local multivariate normality of background. The RX algorithm, along with its many modified versions, has been widely explored, and the main concerns identified are related to local background covariance matrix estimation. The small sample size, local background nonhomogeneity, and the presence of target pixels within the estimation window are factors that can deeply affect local background covariance matrix estimation. These critical aspects may occur together in the same operational scenario, and they may strongly impair the detection performance. However, due to their intrinsic difference, these aspects have been typically discussed within different frameworks, disregarding the possible existing connections while developing different approaches to solution. We investigate these critical aspects, along with their impact on the detection process, from an operational detection perspective. The approaches to solution are critically analyzed, discussing possible links and connections. Real hyperspectral data are employed for assessing if the algorithms, designed ad hoc to solve a specific problem, can either handle more complex situations, or bring about further complications.

Anomaly Detection for Hyperspectral Images Based on Robust Locally Linear Embedding

In this paper, anomaly detection in hyperspectral images is investigated using robust locally linear embedding (RLLE) for dimensionality reduction in conjunction with the RX anomaly detector. The new RX-RLLE method is implemented for large images by subdividing the original image and applying the RX-RLLE operations to each subset. Moreover, from the kernel view of LLE, it is demonstrated that the RX-RLLE is equivalent to introducing a locally linear embedding (LLE) kernel into the kernel RX (KRX) algorithm. Experimental results indicate that the RX-RLLE has good anomaly detection performance and that RLLE has superior performance to LLE and principal component analysis (PCA) for dimensionality reduction in the application of anomaly detection.

Robust high-order matched filter for hyperspectral target detection

A robust high-order matched filter (RHMF) for automatic target detection in hyperspectral images is proposed. The classical detection methods mainly focus on second-order statistics and do not take intrinsic uncertainty or variability of target spectral signatures into account. For automatic target detection in a hyperspectral image, most interesting targets usually occur with low probabilities and small population and they generally cannot be described by second-order statistics. Also, one difficult point in target detection is the inherent variability in target spectral signatures. Under such circumstances, the RHMF algorithm uses high-order statistics, and takes variability into consideration, and has been shown by presented experiments to be more effective than classical detection methods.

Target Detection With Semisupervised Kernel Orthogonal Subspace Projection

The orthogonal subspace projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel methods (KMs) makes the method nonlinear, helps to combat the high-dimensionality problem, and improves robustness to noise. This paper presents a semisupervised graph-based approach to improve KOSP. The proposed algorithm deforms the kernel by approximating the marginal distribution using the unlabeled samples. Two further improvements are presented. First, a contextual selection of unlabeled samples is proposed. This strategy helps in better modeling the data manifold, and thus, improved sensitivity-specificity rates are obtained. Second, given the high computational burden involved, we present two alternative formulations based on the Nystroumlm method and the incomplete Cholesky factorization to achieve operational processing times. The good performance of the proposed method is illustrated in a toy data set and two relevant hyperspectral image target-detection applications: crop identification and thermal hot-spot detection. A clear improvement is observed with respect to the linear and the nonlinear kernel-based OSP, demonstrating good generalization capabilities when a low number of labeled samples are available, which is usually the case in target-detection problems. The relevance of unlabeled samples and the computational cost are also analyzed in detail.

Real-time anomaly detection in hyperspectral images using multivariate normal mixture models and GPU processing

Hyperspectral imaging, which records a detailed spectrum of light arriving in each pixel, has many potential uses in remote sensing as well as other application areas. Practical applications will typically require real-time processing of large data volumes recorded by a hyperspectral imager. This paper investigates the use of graphics processing units (GPU) for such real-time processing. In particular, the paper studies a hyperspectral anomaly detection algorithm based on normal mixture modelling of the background spectral distribution, a computationally demanding task relevant to military target detection and numerous other applications. The algorithm parts are analysed with respect to complexity and potential for parallellization. The computationally dominating parts are implemented on an Nvidia GeForce 8800 GPU using the Compute Unified Device Architecture programming interface. GPU computing performance is compared to a multi-core central processing unit implementation. Overall, the GPU implementation runs significantly faster, particularly for highly data-parallelizable and arithmetically intensive algorithm parts. For the parts related to covariance computation, the speed gain is less pronounced, probably due to a smaller ratio of arithmetic to memory access. Detection results on an actual data set demonstrate that the total speedup provided by the GPU is sufficient to enable real-time anomaly detection with normal mixture models even for an airborne hyperspectral imager with high spatial and spectral resolution.

Kernel Eigenspace Separation Transform for Subspace Anomaly Detection in Hyperspectral Imagery

This letter proposes a nonlinear version of the eigenspace separation transform (EST) for subspace anomaly detection in hyperspectral imaging. The EST is defined in terms of the eigenvectors of the difference correlation matrix (DCOR) obtained using the data from the two classes. Using ideas found in the machine learning literature (i.e., the kernel trick), a nonlinear version-kernel EST (KEST)-is achieved by expressing the DCOR in terms of dot products in feature space and replacing all dot products with a Mercer kernel function that is defined in terms of input data space. Experimental results indicate that KEST outperforms many other commonly used subspace anomaly detection algorithms.

Analytical Comparison of the Matched Filter and Orthogonal Subspace Projection Detectors for Hyperspectral Images

In this paper, we perform an analytical comparison of two well-known detectors-the matched filter detector (MFD) and the orthogonal subspace projection (OSP) detector for the subpixel target detection in hyperspectral images under the assumption of the linear mixing model. The OSP detector (equivalent to the least-squares estimator) is a popular detector utilizing background signature information. On the other hand, the MFD is intended for a model without background information, and it is often used for its simplicity. The OSP detector seems to be more reliable because it removes the interference of background signatures. However, it has been demonstrated in the literature that sometimes the MFD can be more powerful. In this paper, we show an innovative approach to the evaluation of the detectors' performance. We prove the analytical results explaining the relationship between the two detectors beyond the anecdotal evidence from specific hyperspectral images or simulations. We also give some guidelines on when the MFD may be more beneficial than the OSP and when the OSP is better because of being more robust against a wide range of conditions. A major contribution of this paper is a development of a new approach to the comparison of detectors.

A support vector method for anomaly detection in hyperspectral imagery

This paper presents a method for anomaly detection in hyperspectral images based on the support vector data description (SVDD), a kernel method for modeling the support of a distribution. Conventional anomaly-detection algorithms are based upon the popular Reed-Xiaoli detector. However, these algorithms typically suffer from large numbers of false alarms due to the assumptions that the local background is Gaussian and homogeneous. In practice, these assumptions are often violated, especially when the neighborhood of a pixel contains multiple types of terrain. To remove these assumptions, a novel anomaly detector that incorporates a nonparametric background model based on the SVDD is derived. Expanding on prior SVDD work, a geometric interpretation of the SVDD is used to propose a decision rule that utilizes a new test statistic and shares some of the properties of constant false-alarm rate detectors. Using receiver operating characteristic curves, the authors report results that demonstrate the improved performance and reduction in the false-alarm rate when using the SVDD-based detector on wide-area airborne mine detection (WAAMD) and hyperspectral digital imagery collection experiment (HYDICE) imagery

On the impact of covariance contamination for adaptive detection in hyperspectral imaging

Adaptive detection has been a staple in areas such as radar and sonar for a number of years. A key tenet of adaptive detectors is their use of a noise only covariance estimate from a secondary data set. When samples bearing the target signal are included in this covariance estimate, it becomes contaminated. The impact of covariance contamination is evaluated for three popular adaptive detection statistics. Multiple test cases are presented using real data, not simulations, and it is shown that significant contamination occurs with as few as eight target samples included in a 100 000+ sample covariance estimate. In addition to number of samples, sensitivity of the detection statistic to contamination from targets present also depends on algorithm type. Hyperspectral imaging (HSI) data offer insight into the impact of covariance contamination on adaptive detection and how it is related to signal-to-noise ratio (SNR) and spectral similarity.

1255 Supervised walking program for cancer patients

Fukunaga-Koontz Transform (FKT) is a statistical technique which has many application areas for two-class classification or detection problems. In this paper, we have proposed improved target detection algorithm for hyperspectral imagery (HSI) based on enhanced FKT which gives better results for multi-class target detection problems.Hyperspectral imagery is popular for target detection applications due to its additional properties compare to multispectral images. It presents one dimensional (spectral) and two dimensional (spatial) features of targets for detection problems. For multi-class target detection in hyperspectral images, we have selected each target's 1D features, called signature of targets, to introduce to enhanced FKT during learning stage. After learning stage of FKT, we have applied our operators, obtained by enhanced FKT, to HSI images to detect more than one target simultaneously.The experimental results show that multi-class FKT has satisfactory performance over 95 percent of true detection rate especially on the pixel sized targets. In addition, multi-class processing ability of the proposed enhanced FKT is very important for many applications such as classification, recognition, clustering, tracking problems in the literature.

A comparative study for orthogonal subspace projection and constrained energy minimization

We conduct a comparative study and investigate the relationship between two well-known techniques in hyperspectral image detection and classification: orthogonal subspace projection (OSP) and constrained energy minimization. It is shown that they are closely related and essentially equivalent provided that the noise is white with large SNR. Based on this relationship, the performance of OSP can be improved via data-whitening and noise-whitening processes.

Real-time constrained linear discriminant analysis to target detection and classification in hyperspectral imagery

In this paper, we present a constrained linear discriminant analysis (CLDA) approach to hyperspectral image detection and classification as well as its real-time implementation. The basic idea of CLDA is to design an optimal transformation matrix which can maximize the ratio of inter-class distance to intra-class distance while imposing the constraint that different class centers after transformation are along different directions such that different classes can be better separated. The solution turns out to be a constrained version of orthogonal subspace projection (OSP) implemented with a data whitening process. The CLDA approach can be applied to solve both detection and classification problems. In particular, by introducing color for display the classification is achieved with a single classified image where a pre-assigned color is used to display a specified class. The real-time implementation is also developed to meet the requirement of on-line image analysis when the immediate data assessment is critical. The experiments using HYDICE data demonstrate the strength of CLDA approach in discriminating the targets with subtle spectral difference.

Unsupervised target detection in hyperspectral images using projection pursuit

The authors present a projection pursuit (PP) approach to target detection. Unlike most of developed target detection algorithms that require statistical models such as linear mixture, the proposed PP is to project a high dimensional data set into a low dimensional data space while retaining desired information of interest. It utilizes a projection index to explore projections of interestingness. For target detection applications in hyperspectral imagery, an interesting structure of an image scene is the one caused by man-made targets in a large unknown background. Such targets can be viewed as anomalies in an image scene due to the fact that their size is relatively small compared to their background surroundings. As a result, detecting small targets in an unknown image scene is reduced to finding the outliers of background distributions. It is known that "skewness," is defined by normalized third moment of the sample distribution, measures the asymmetry of the distribution and "kurtosis" is defined by normalized fourth moment of the sample distribution measures the flatness of the distribution. They both are susceptible to outliers. So, using skewness and kurtosis as a base to design a projection index may be effective for target detection. In order to find an optimal projection index, an evolutionary algorithm is also developed to avoid trapping local optima. The hyperspectral image experiments show that the proposed PP method provides an effective means for target detection.