Targets In Hyperspectral Imagery Research Articles

Learning Single Spectral Abundance for Hyperspectral Subpixel Target Detection.

Due to the limitation of target size and spatial resolution, targets of interest in hyperspectral images (HSIs) often appear as subpixel targets, which makes hyperspectral target detection still faces an important bottleneck, that is, subpixel target detection. In this article, we propose a new detector by learning single spectral abundance for hyperspectral subpixel target detection (denoted as LSSA). Different from most existing hyperspectral detectors that are designed based on a match of the spectrum assisted by spatial information or focusing on the background, the proposed LSSA addresses the problem of detecting subpixel targets by learning a spectral abundance of the target of interest directly. In LSSA, the abundance of the prior target spectrum is updated and learned, while the prior target spectrum is fixed in a nonnegative matrix factorization (NMF) model. It turns out that such a way is quite effective to learn the abundance of subpixel targets and contributes to detecting subpixel targets in hyperspectral imagery (HSI). Numerous experiments are conducted on one simulated dataset and five real datasets, and the results indicate that the LSSA yields superior performance in hyperspectral subpixel target detection and outperforms its counterparts.

A GLRT-Based Multi-Pixel Target Detector in Hyperspectral Imagery

In hyperspectral imagery, target detection algorithms are usually based on the spectral signature information. Due to the advance of the spatial resolution of hyperspectral sensors, the ground sample distance may be much smaller than the size of targets. As a result, targets often occupy multiple consecutive pixels, which are referred to as multi-pixel targets. In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. Jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.

Target Detection in Hyperspectral Imagery Using Atmospheric-Spectral Modeling and Deep Learning

Target detection (TD) in spectral imagery is an evolving analytical perspective with broader application potential. The perceived distinctness of the spectral signatures of the materials of interest is exploited for detecting targets in hyperspectral imagery. Space-time varying atmospheric perturbances on the radiation reaching a remote sensor are major limitations for designing a successful TD framework. Incorporating atmospheric components into a target detection framework is vital for practical applicability. Considered a general approach for flexibility, scalability, and optimal prediction, deep learning (DL) methods are increasingly used in various remote sensing applications. However, their potential for TD is relatively unexplored. Especially, the ability to provide training data sufficient for DL models and maintaining the functional relevance of the sparsely distributed targets in hyperspectral imagery are crucial for TD frameworks. This letter presents a novel method for training of DL architecture, called Deep Spectral Target Detector (DSTD). The proposed method includes a semi-supervised multi-scenario forward radiative transfer modelling (RTM) for the simulation of spectral signatures of various targets as training data suitable for the functional requirements of a typical DL architecture. We implemented the DSTD on a TD application-specific benchmark AVIRIS-NG airborne hyperspectral imagery acquired over a study site near Ooty, India. Compared to state-of-art statistical target detectors, the detection performance of the DSTD is superior to equivalent. Further, RTM-based training yields a robust model, impervious to the atmospheric mismatches between target collection and TD environments, indicating the potential for a similar approach to developing efficient DL-based methods for TD in the future.

Angle Distance-Based Hierarchical Background Separation Method for Hyperspectral Imagery Target Detection

Traditional detectors for hyperspectral imagery (HSI) target detection (TD) output the result after processing the HSI only once. However, using the prior target information only once is not sufficient, as it causes the inaccuracy of target extraction or the unclean separation of the background. In this paper, the target pixels are located by a hierarchical background separation method, which explores the relationship between the target and the background for making better use of the prior target information more than one time. In each layer, there is an angle distance (AD) between each pixel spectrum in HSI and the given prior target spectrum. The AD between the prior target spectrum and candidate target ones is smaller than that of the background pixels. The AD metric is utilized to adjust the values of pixels in each layer to gradually increase the separability of the background and the target. For making better discrimination, the AD is calculated through the whitened data rather than the original data. Besides, an elegant and ingenious smoothing processing operation is employed to mitigate the influence of spectral variability, which is beneficial for the detection accuracy. The experimental results of three real hyperspectral images show that the proposed method outperforms other classical and recently proposed HSI target detection algorithms.

Training Data Assisted Anomaly Detection of Multi-Pixel Targets In Hyperspectral Imagery

In this paper, we investigate the anomaly detection problem for widespread targets with known spacial patterns under a local Gaussian model when training data are available. Three adaptive detectors are proposed based on the principles of the generalized likelihood ratio test, the Rao test, and the Wald test, respectively. We prove that these tests are statistically equivalent to each other. In addition, analytical expressions for the probability of false alarm and probability of detection of the proposed detectors are obtained, which are verified through Monte Carlo simulations. It is shown that these detectors have a constant false alarm rate against the covariance matrix. Finally, numerical examples using synthetic and real hyperspectral data demonstrate that these training data assisted detectors have better detection performance than their counterparts without training data.

Hyperspectral Anomaly Detection via Background Estimation and Adaptive Weighted Sparse Representation

Anomaly detection is an important task in hyperspectral imagery (HSI) processing. It provides a new way to find targets that have significant spectral differences from the majority of the dataset. Recently, the representation-based methods have been proposed for detecting anomaly targets in HSIs. It is essential for this type of method to construct a valid background dictionary to distinguish anomaly and background accurately. In this paper, a novel hyperspectral anomaly detection method based on background estimation and adaptive weighted sparse representation has been proposed. Firstly, to obtain the effective background dictionary without anomaly information, a new background dictionary construction strategy is designed. Secondly, the sparse representation based on the constructed background dictionary is utilized on the dataset. Anomalies and background are distinguished through the response of the residual matrix. Thirdly, the residual matrix is weighted adaptively from global and local domains, which makes anomalies and background more discriminative. An important advantage of the proposed method is that it considers the properties of anomalies in both spectral and spatial domains. Experiments on three HSI datasets reveal that our proposed method achieves an outstanding detection performance compared with the other anomaly detection algorithms.

Spectral Radiance Modeling and Bayesian Model Averaging for Longwave Infrared Hyperspectral Imagery and Subpixel Target Identification

Hyperspectral imagery (HSI) exploitation typically requires spectral signatures for target detection and identification algorithms. As the longwave infrared (LWIR) region of the electromagnetic spectrum is dominated by thermal emission, spectral radiance measurements are influenced by object temperature, and thus, estimates of target temperature may be necessary for emissivity retrieval to support these algorithms. Therefore, lack of accurate temperature information poses a significant challenge for HSI target detection and identification. Previous studies have demonstrated LWIR hyperspectral unmixing in both radiance and emissivity domains using in-scene target signatures. Here, a radiance-domain LWIR material identification algorithm for subpixel target identification of solid materials is developed by combining spectral radiance and linear mixing models with Bayesian model averaging. Application to experimental LWIR HSI illustrates that the algorithm effectively distinguishes between solid materials with a high degree of spectral similarity and reduces the probability of false alarms by at least one order of magnitude over a standard adaptive coherence estimator detector. Limits of identification are inferred from the imagery and found to depend on material type, target size, and target geometry. For the sensor and materials in this paper, the results imply that targets of nominally 5 m2 in size with strong spectral features can be identified for ground sampling distances (GSDs) on the order of 5–10 m (with abundances as low as ~10%) whereas blackbody-like materials are difficult to distinguish for GSDs larger than approximately 3 m.

Recursive Band Processing of Automatic Target Generation Process for Finding Unsupervised Targets in Hyperspectral Imagery

Automatic target generation process (ATGP) has been widely used for unsupervised target detection. However, as designed, it detects targets using full-band information. Unfortunately, on many occasions, various targets can be detected using varying bands, and ATGP can only provide one-shot target detection with all bands being used. This paper develops a new approach which can implement ATGP bandwise in a progressive manner, called progressive band processing of ATGP (PBP-ATGP) so that ATGP can be carried out band by band. Since PBP-ATGP must repeatedly implement orthogonal projections, recursive equations are further derived for PBP-ATGP, to be called recursive band processing of ATGP (RBP-ATGP) which can implement PBP-ATGP recursively. As a result, many advantages can be benefited from RBP-ATGP. Most importantly, RBP-ATGP can generate 3-D interband progressive profiles from band to band that can be used for progressive target detection, a task for which no target detection techniques using full-band information can provide.

Formulation for min-max clairvoyant fusion based on monotonic recalibration of statistics

A formulation for min-max clairvoyant fusion (also known as continuum fusion) is developed that exploits the invariance of hypothesis testing statistics to monotonic transformations. In addition to generalizing an earlier formulation based on manipulated thresholds, the new formulation leads to efficient algorithms for two of the most widely advocated fusion flavors: one based on combining detectors with the same false alarm rate, and another based on constant detection rate. These algorithms are used to investigate and compare the performance of different detectors for a class of problems that arises from detecting small or weak targets in hyperspectral imagery. The experiments are performed on simulated data from well-defined distributions so as to isolate the effect of different flavors of fusion from the effects of model mismatch.

Progressive Discrimination: An Automatic Method for Mapping Individual Targets in Hyperspectral Imagery

This paper demonstrates a new method called progressive discrimination (PD) for mapping an individual spectral class within an image. Given training data for a target, PD iteratively samples nontarget image pixels using a collapsing distance threshold within the space of an evolving discriminant function. This has the effect of progressively isolating the target class from similar spectra in the image. PD was compared to Bayesian maximum likelihood classification, mixture-tuned matched filtering, spectral angle mapping, and support vector machine methods for mapping three different invasive species in two types of high-spatial-resolution airborne hyperspectral imagery, AVIRIS and AISA. When tested with 20 different randomly selected groups of training fields, PD classification accuracies for the two spectrally distinct plant species in these images had an average of 98% and a standard deviation of 1%. These randomized trials were capable of providing higher classification accuracies than the best results obtained by two expert analysts using existing methods. For the third species that was less distinct, PD results were comparable to the results obtained by experienced analysts with existing methods. Despite requiring less input from the user than many techniques, PD provided more consistent high mapping accuracy, making it an ideal tool for scientists and land use managers who are not trained in image processing.

Anomaly detection in hyperspectral imagery based on maximum entropy and nonparametric estimation

This paper presents several maximum entropy and nonparametric estimation detectors (MENEDs) which belong to two categories to detect anomaly targets in hyperspectral imagery. First, probability density of target is estimated using Principle of Maximum Entropy according to the low-probability occurrence of target, which simplifies the generalize likelihood ratio test to merely testing background likelihood. Then considering the high complexity of hyperspectral data, in conjunction with the low-probability occurrence of target, sample-depended multimode model (SDMM) is presented to obtain the probability density of the background. Finally, the estimated probability density of the background is tested to detect targets. The proposed MENEDs greatly depend on hyperspectral data sample, rather than the statistical model, to extract the statistical information, which alleviates statistical model discrepancy and has explicit physical mechanism on detection. Experimental results on visible/near-infrared hyperspectral imagery of type I Operational Modular Imaging Spectrometer (OMIS-I) demonstrate that MENEDs perform better than several known detectors, including RX detector (RXD), normalized RXD (NRXD), modified RXD (MRXD), correlation matrix based NRXD (CNRXD), correlation matrix based MRXD (CMRXD), unified target detector (UTD) and low probability detection (LPD).

Regularized Spectral Matched Filter for Target Recognition in Hyperspectral Imagery

This letter extends the idea of regularization to spectral matched filters. It incorporates a quadratic penalization term in the design of spectral matched filters in order to restrict the possible matched filters (models) to a subset which are more stable and have better performance than the non-regularized adaptive spectral matched filters. The effect of regularization depends on the form of the regularization term and the amount of regularization which is controlled by a parameter so-called the regularization coefficient. In this letter, the sum-of-squares of the filter coefficients is used as the regularization term, and different values for the regularization coefficient are tested. A Bayesian-based derivation of the regularized matched filter is also described which provides a procedure for choosing the regularization coefficient. Experimental results for detecting targets in hyperspectral imagery are presented for regularized and non-regularized spectral matched filters.

Kernel orthogonal subspace projection for hyperspectral signal classification

In this paper, a kernel-based nonlinear version of the orthogonal subspace projection (OSP) operator is defined in terms of kernel functions. Input data are implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping, which is associated with a kernel function. The OSP expression is then derived in the feature space, which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for detection of roads, roof tops, mines, and targets in hyperspectral imagery, and it is shown that the kernelized OSP method outperforms the conventional OSP approach.