In this paper, a tutorial overview on anomaly detection for hyperspectral electro-optical systems is presented. This tutorial is focused on those techniques that aim to detect small man-made anomalies typically found in defense and surveillance applications. Since a variety of methods have been proposed for detecting such targets, this tutorial places emphasis on the techniques that are either mathematically more tractable or easier to interpret physically. These methods are not only more suitable for a tutorial publication, but also an essential to a study of anomaly detection. Previous surveys on this subject have focused mainly on anomaly detectors developed in a statistical framework and have been based on well-known background statistical models. However, the most recent research trends seem to move away from the statistical framework and to focus more on deterministic and geometric concepts. This work also takes into consideration these latest trends, providing a wide theoretical review without disregarding practical recommendations about algorithm implementation. The main open research topics are addressed as well, the foremost being algorithm optimization, which is required for embodying anomaly detectors in real-time systems.

Very high resolution inverse synthetic aperture radar (ISAR) imaging of maneuvering targets is a complicated task. In fact, the conventional range Doppler (RD) ISAR technique does not work properly when target motions generate terms higher than the first order in the phase of the received signal relative to each scatterer. This effect typically happens when at least one of these situations occur: (1) very high resolution images are required; (2) the target maneuvers; and (3) the target undergoes significant angular motions (roll, pitch, and yaw). A novel ISAR technique, named range instantaneous Doppler (RID), has been proposed for the reconstruction of very high resolution images of maneuvering targets. In this paper, we analytically show that the RID technique works properly when high-resolution ISAR images are required of maneuvering and/or rolling, pitching, and yawing targets; we also quantify the performance improvement of the RID technique with respect to the RD technique. The problem is tackled from an analytical point of view. First, we define a new model of the ISAR received signal that is valid for maneuvering targets, then we derive and compare the analytical expression of the point spread function (PSF) for the two techniques. Furthermore, we perform a statistical analysis to evaluate the improvement of the RID technique versus the RD technique in terms of spatial resolution. Finally, we prove the effectiveness of the RID technique by simulating the imaging process for two different targets: (1) a ship that undergoes roll, pitch and yaw motions and (2) a fast maneuvering airplane.

This paper reviews well-known classic algorithms and more recent experimental approaches for distinguishing the weak signal of a target (either known or anomalous) from the cluttered background of a hyperspectral image. Making this distinction requires characterization of the targets and characterization of the backgrounds, and our emphasis in this review is on the backgrounds. We describe a variety of background modeling strategies-Gaussian and non-Gaussian, global and local, generative and discriminative, parametric and nonparametric, spectral and spatio-spectral-in the context of how they relate to the target and anomaly detection problems. We discuss the major issues addressed by these algorithms, and some of the tradeoffs made in choosing an effective algorithm for a given detection application. We identify connections among these algorithms and point out directions where innovative modeling strategies may be developed into detection algorithms that are more sensitive and reliable.

In this paper, a novel method to characterize random noise sources in hyperspectral (HS) images is proposed. Noise is described using a parametric model that accounts for the dependence of noise variance on the useful signal. Such model takes into account the photon noise contribution and is therefore suitable for noise characterization in the data acquired by new-generation HS sensors where electronic noise is not dominant. A new algorithm is developed for the estimation of noise parameters which consists of two steps. First, the noise and signal realizations are extracted from the original image by resorting to the multiple-linear-regression-based approach. Then, the model parameters are estimated by using a maximum likelihood approach. The new method does not require the intervention of a human operator and the selection of homogeneous regions in the scene. The performance of the new technique is analyzed on simulated HS data. Results on real data are also presented and discussed. Images acquired with a new-generation HS camera are analyzed to give an experimental evidence of the dependence of random noise on the signal level and to show the results of the estimation algorithm. The algorithm is also applied to a well-known Airborne Visible/Infrared Imaging Spectrometer data set in order to show its effectiveness when noise is dominated by the signal-independent term.

The performance of two adaptive detection schemes developed in the literature, Kelly's generalized likelihood ratio test (GLRT) and the adaptive linear-quadratic (ALQ) detector, are tested on real sea clutter data recorded by the IPIX experimental radar. The results of first- and second-order statistical analyses performed on two data sets are reported. Amplitude analysis has been carried out by checking the fitting to Weibull, log-normal, K, and generalized K models. The results show good agreement between performance prediction based on the generalized K model, with texture strongly correlated among primary and secondary data, and the performance obtained by processing the real sea clutter data.

The central problem in hyperspectral remote sensing is characterizing the material components of a scene based on the spectral radiance observed in the image pixels. What makes this challenging is the fact that the spectral response for a given material exhibits considerable variability from a variety of causes: intrinsic (depending on the composition or morphology of the material), extrinsic (depending on the size of an object or the concentration of the material), or environmental (due to illumination, atmospheric distortion, and so on). In this article, we survey many of the causes of spectral variability, describe spectral models for this variability, and outline some signal processing and target detection strategies for analyzing hyperspectral data in a way that is more robust to this variability.