Anomalies In Hyperspectral Images Research Articles

HYPERSPECTRAL ANOMALY DETECTION WITH AN IMPROVED APPROACH: INTEGRATION OF GO DECOMPOSITION ALGORITHM AND LAPLACIAN MATRIX MODIFIER

In this study, a hyperspectral anomaly detection method based on Laplacian matrix (HADLAP) is proposed. This paper addresses the problem of determining covariance matrix inversion in high-dimensional data and proposes a new approach for identifying anomalies in hyperspectral images (HSIs). The study’s goals are to find anomalous locations in HSIs and to deal with the problem of calculating the inversion of the covariance matrix of high dimensional data. The method is centered on two main concepts. One of them is decomposition process. The other one is detection process. First, HSI data is decomposed as a low rank and sparse matrices. Second, the sparse component of the data is used to build Mahalanobis Distance (MD). In this study, go decomposition (GoDec) algorithm is employed to decompose the data. Then, the distance is calculated by obtained matrix with aim of detection of anomalous pixels in the HSIs. The method differs from previous studies that covariance matrix in the distance is computed with Laplacian matrix and MD. Experiments conducted on three hyperspectral datasets present the superiority and effectiveness of the proposed framework in terms of detection performance with respect to state-of-the-art methods.

Multi-Dimensional Low-Rank with Weighted Schatten p-Norm Minimization for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is an important unsupervised binary classification problem that aims to effectively distinguish between background and anomalies in hyperspectral images (HSIs). In recent years, methods based on low-rank tensor representations have been proposed to decompose HSIs into low-rank background and sparse anomaly tensors. However, current methods neglect the low-rank information in the spatial dimension and rely heavily on the background information contained in the dictionary. Furthermore, these algorithms show limited robustness when the dictionary information is missing or corrupted by high level noise. To address these problems, we propose a novel method called multi-dimensional low-rank (MDLR) for HSI anomaly detection. It first reconstructs three background tensors separately from three directional slices of the background tensor. Then, weighted schatten p-norm minimization is employed to enforce the low-rank constraint on the background tensor, and LF,1-norm regularization is used to describe the sparsity in the anomaly tensor. Finally, a well-designed alternating direction method of multipliers (ADMM) is employed to effectively solve the optimization problem. Extensive experiments on four real-world datasets show that our approach outperforms existing anomaly detection methods in terms of accuracy.

Hyperspectral Anomaly Detection via Dual Dictionaries Construction Guided by Two-Stage Complementary Decision

Low rank and sparse representation (LRSR) with dual-dictionaries-based methods for detecting anomalies in hyperspectral images (HSIs) are proven to be effective. However, the potential anomaly dictionary is vulnerable to being contaminated by the background pixels in the above methods, and this limits the effect of hyperspectral anomaly detection (HAD). In this paper, a dual dictionaries construction method via two-stage complementary decision (DDC–TSCD) for HAD is proposed. In the first stage, an adaptive inner window–based saliency detection was proposed to yield a coarse binary map, acting as the indicator to select pure background pixels. For the second stage, a background estimation network was designed to generate a fine binary map. Finally, the coarse binary map and fine binary map worked together to construct a pure background dictionary and potential anomaly dictionary in the guidance of the superpixels derived from the first stage. The experiments conducted on public datasets (i.e., HYDICE, Pavia, Los Angeles, San Diego-I, San Diego-II and Texas Coast) demonstrate that DDC–TSCD achieves satisfactory AUC values, which are separately 0.9991, 0.9951, 0.9968, 0.9923, 0.9986 and 0.9969, as compared to four typical methods and three state-of-the-art methods.

Sparse Representation Based Hyperspectral Anomaly Detection via Adaptive Background Sub-Dictionaries

Hyperspectral anomaly detection has drawn much attention in recent years. In this paper, in order to effectively extract anomalies in hyperspectral images, a novel sparse-representation based hyperspectral anomaly detection method via adaptive background sub-dictionaries is proposed. Firstly, a background estimation strategy is proposed to provide representative background information. Based on the estimated background, a global dictionary is constructed by utilizing K-means clustering algorithm. Next, Several active atoms are selected from the global dictionary to form a sub-dictionary to adaptively approximate the local region in each dual-window. This sub-dictionary construction strategy can remove potential anomaly contamination in local regions. Finally, a re-weighting strategy is proposed to enhance the performance of sparse-representation-based anomaly detector. Experimental results demonstrate that our method can effectively extract anomalies and suppress background simultaneously.

An Algorithm for an Accurate Detection of Anomalies in Hyperspectral Images With a Low Computational Complexity

Anomaly detection (AD) is an important technique in hyperspectral data analysis that permits to distinguish rare objects with unknown spectral signatures that are particularly not abundant in a scene. In this paper, a novel algorithm for an accurate detection of anomalies in hyperspectral images with a low computational complexity, named ADALOC2, is proposed. It is based on two main processing stages. First, a set of characteristic pixels that best represent both anomaly and background classes are extracted applying orthogonal projection techniques. Second, the abundance maps associated to these pixels are estimated. Under the assumption that the anomaly class is composed of a scarce group of image pixels, rare targets can be identified from abundance maps characterized by a representation coefficient matrix with a large amount of almost zero elements. Unlike the other algorithms of the state of the art, the ADALOC2 algorithm has been specially designed for being efficiently implemented into parallel hardware devices for applications under real-time constraints. To achieve this, the ADALOC2 algorithm uses simple and highly parallelized operations, avoiding to perform complex matrix operations such as the computation of an inverse matrix or the extraction of eigenvalues and eigenvectors. An extensive set of simulations using the most representative state-of-the-art AD algorithms and both real and synthetic hyperspectral data sets have been conducted. Moreover, extra assessment metrics apart from classical receiver operating characteristic curves have been defined in order to make deeper comparisons. The obtained results clearly support the benefits of our proposal, both in terms of the accuracy of the detection results and the processing power demanded.

High-Dimensional Data Modeling Techniques for Detection of Chemical Plumes and Anomalies in Hyperspectral Images and Movies

We briefly review recent progress in techniques for modeling and analyzing hyperspectral images and movies, in particular for detecting plumes of both known and unknown chemicals. For detecting chemicals of known spectrum, we extend the technique of using a single subspace for modeling the background to a “mixture of subspaces” model to tackle more complicated background. Furthermore, we use partial least squares regression on a resampled training set to boost performance. For the detection of unknown chemicals, we view the problem as an anomaly detection problem and use novel estimators with low-sampled complexity for intrinsically low-dimensional data in high dimensions that enable us to model the “normal” spectra and detect anomalies. We apply these algorithms to benchmark datasets made available by the Automated Target Detection program cofunded by NSF, DTRA, and NGA, and compare, when applicable, to current state-of-the-art algorithms, with favorable results.

Exploring the performance–power–energy balance of low-power multicore and manycore architectures for anomaly detection in remote sensing

In this paper, we perform an experimental study of the interactions between execution time (i.e., performance), power, and energy that occur in modern low-power architectures when executing the RX algorithm for detecting anomalies in hyperspectral images (i.e., signatures which are spectrally different from their surrounding data). We believe this is important because, for airborne and spaceborne remote sensing missions, power and/or energy can be in practice as relevant as performance. In this sense, this paper investigates whether several recent low-power multithreaded architectures, from ARM and NVIDIA, can be a practical alternative in this domain to a standard high-performance multicore processor, using the RX anomaly detector as a case study.

Probabilistic anomaly detector for remotely sensed hyperspectral data

Anomaly detection is an important technique for remotely sensed hyperspectral data exploitation. In the last decades, several algorithms have been developed for detecting anomalies in hyperspectral images. The Reed-Xiaoli detector (RXD) is one of the most widely used approaches for this purpose. Since the RXD assumes that the distribution of the background is Gaussian, it generally suffers from a high false alarm rate. In order to address this issue, we introduce an unsupervised probabilistic anomaly detector (PAD) based on estimating the difference between the probabilities of the anomalies and the background. The proposed PAD takes advantage of the results provided by the RXD to estimate statistical information for the targets and background, respectively, and then uses an automatic strategy to find the most suitable threshold for the separation of targets from the background. The proposed technique is validated using a synthetic data set and two real hyperspectral data sets with ground-truth information. Our experimental results indicate that the proposed method achieves good detection ratios with adequate computational complexity as compared with other widely used anomaly detectors.

Towards the mitigation of correlation effects in anomaly detection for hyperspectral imagery

This research involves simulating remote sensing conditions using previously collected hyperspectral imagery (HSI) data. The Reed–Xiaoli (RX) anomaly detector is well-known for its unsupervised ability to detect anomalies in hyperspectral images. However, the RX detector assumes uncorrelated and homogeneous data, both of which are not inherent in HSI data. To address this difficulty, we propose a new method termed linear RX (LRX). Whereas RX places a test pixel at the center of a moving window, LRX employs a line of pixels above and below the test pixel. In this paper, we contrast the performance of LRX, a variant of LRX called iterative linear RX (ILRX), the recently introduced iterative RX (IRX) algorithm, and the support vector data description (SVDD) algorithm, a promising new HSI anomaly detector. Through experimentation, the line of pixels used by ILRX shows an advantage over RX and IRX in that it appears to mitigate the deleterious effects of correlation due to the spatial proximity of the pixels; while the iterative adaptation taken from IRX simultaneously eliminates outliers allowing ILRX an advantage over LRX. Such innovations to the basic RX algorithm allow for the reduction of bias and error in the estimation of the mean vector and covariance matrix, thus accounting for a portion of the spatial correlation inherent in HSI data.

Nonparametric Framework for Detecting Spectral Anomalies in Hyperspectral Images

Over the past few years, hyperspectral data exploitation aimed at detecting spectral anomalies within remotely sensed images has been of growing interest in many applications. In this letter, we are interested in an anomaly detection (AD) scheme for hyperspectral images in which spectral anomalies are defined with respect to a statistical model of the background probability density function (PDF). Among the multitude of PDF estimators discussed in statistics literature, Parzen windowing (PW) has always attracted much attention. However, its ability to estimate the PDF of global background in order to detect anomalies in hyperspectral images has not been investigated yet. Here, we propose the use of PW to provide reliable background PDF estimation. As is widely recognized, PW performance is primarily affected by the choice of the bandwidth matrix, which controls the degree of smoothing for the resulting PDF approximation. In this letter, the bandwidth selection problem is approached, resorting to an unsupervised method based on a Bayesian approach. Once the background PDF is approximated through PW, it is employed to detect anomalous objects within the scene by using the likelihood ratio test.

Complexity-aware algorithm architecture for real-time enhancement of local anomalies in hyperspectral images

Anomaly detection (AD) from remotely sensed multi-hyperspectral images is a powerful tool in many applications, such as strategic surveillance and search and rescue operations. In a typical operational scenario, an airborne hyperspectral sensor searches a wide area to identify regions that may contain potential targets. These regions typically cue higher spatial-resolution sensors to provide target recognition and identification. While this procedure is mostly automated, an on-board operator is generally assigned to examine in real time the AD output and select the regions of interest to be sent for cueing. Real-time enhancement of local anomalies in images of the over flown scene can be presented to the operator to facilitate the decision-making process. Within this framework, one of the ultimate research interests is undoubtedly the design of complexity-aware AD algorithm architectures capable of assuring real-time or nearly real-time in-flight processing and prompt decision making. Among the different AD algorithms developed, this work focuses on those AD algorithms aimed at detecting small rare objects that are anomalous with respect to their local background. One of such algorithms, called RX algorithm, is based on a local Gaussian assumption for background and locally estimates its parameters from each pixel local neighborhood. RX has been recognized to be the benchmark AD algorithm for detecting local anomalies in multi-hyperspectral images. RX decision rule has been employed to develop computationally efficient algorithms tested in real-time operating systems. These algorithms rely upon a recursive block-based parameter estimation procedure that makes their processing and, in turn, their detection performance differ from those of original RX. In this paper, a complexity-aware algorithm architecture fully adaptable to real-time processing is presented that allows the computational load to be reduced with respect to original RX, while strictly following its original formulation and thus assuring the same detection performance. An experimental study is presented that analyzes in detail the complexity reduction, in terms of number of elementary operations, offered by the proposed architecture with respect to original RX. A real hyperspectral image of a scene with deployed targets has been employed to perform a case-study analysis of the complexity reduction to be experienced in different operational scenarios. The real data are also adopted to illustrate a possible strategy for on-board line-by-line enhanced visualization of anomalies for decision support.

Hyperspectral Anomaly Detection With Kurtosis-Driven Local Covariance Matrix Corruption Mitigation

Local background covariance matrix corruption due to outliers in the sample data may be one of the major causes that limit detection performance of those algorithms that detect local anomalies in hyperspectral images on the basis of the Mahalanobis distance. In this letter, an original detection scheme is presented that efficiently embeds covariance corruption mitigation. A kurtosis-based binary hypothesis test is first applied to each pixel to quickly determine the presence of outliers in the local neighborhood. Rejection of the null hypothesis triggers application of a robust-to-outlier covariance estimation technique. Results on real data exhibit good detection performance and robustness to outliers. Contrary to previous works, this is achieved without an unnecessary increase of the procedural complexity.

Some practical issues in anomaly detection and exploitation of regions of interest in hyperspectral images

We address method of detection of anomalies in hyperspectral images that consists in performing the detection when the spectral signatures of the targets are unknown. We show that, in real hyperspectral images, use of the full spectral resolution may not be necessary for detection but that the correlation properties of spectral fluctuations have to be taken into account in the design of the detection algorithm. Anomaly detectors are useful for detecting regions of interest (ROIs), but, as they are prone to false alarms, one must analyze the ROIs obtained further to decide whether they correspond to real targets. We propose a method of exploitation of these ROIs that consists in generating a single image in which the contrast of the ROI is optimized.