1,721,022 research outputs found
Improved covariance matrix estimation: interpretation and experimental analysis of different approaches for anomaly detection applications
No full textThe benchmark anomaly detection algorithm for hyperspectral images is the Reed-Xiaoli (RX) Detector, which is based on the Local Multivariate Normality of background. 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. Besides the well-known small-sample size problem, other limitations have been found affecting covariance matrix estimation, e.g. local background non-homogeneity and contamination from adjacent targets. These critical aspects are deeply different in nature, like the situations from which they arise, and hence they have been typically discussed within different frameworks, disregarding possible existing links while developing different approaches to solution. Nevertheless, these critical aspects may occur together in reality, and all of them have to be taken into consideration when approaching anomaly detection, since they may strongly affect detection performance. Therefore, an analysis of the possible existing connections seems crucial in order to asses if existing algorithms, maybe designed ad-hoc to solve a specific problem, can handle more complex situations. In this work, the aforementioned limitations have been investigated from an anomaly detection perspective, and the corresponding approaches to improved covariance matrix estimation have been analyzed by using real hyperspectral data
Metodi di rivelazione di materiali in immagini iperspettrali relative a scenari complessi
No full textImpact of Signal Contamination on the Adaptive Detection Performance of Local Hyperspectral Anomalies
No full textThe effects of signal contamination of secondary data are investigated in the framework of adaptive target detection in remotely sensed hyperspectral images. In contrast to previous studies on signal contamination, the focus of this paper is the detection of targets with unknown spectral signatures (i.e., anomalies) and adaptive detection methods based on a local estimation of the background covariance matrix. Contamination due to the target signal is expected to have a more severe impact when the number of secondary data is limited. An analytical model for signal contamination is developed that allows variability in the extent of contamination. Several parameters, such as the contamination fraction of secondary data and the contaminating signal energy, are introduced, and a contaminating signal-to-interference-plus-noise ratio is derived as an objective measure of contamination. The proposed model is employed to experimentally evaluate signal contamination effects and the impact of its variability on the performance of adaptive detection of local anomalies. The outcomes of the experimental study are substantiated by validation with real hyperspectral data. The results obtained highlight the relevance that the impact of signal contamination, assessed with respect to different system parameters, may have for practical applications. This paper represents a starting point for the development of detection performance forecasting models that consider signal contamination
Improved estimation of local background covariance matrix for anomaly detection in hyperspectral images
No full textAnomaly 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
Underwater Material Discriminability with Fluorescence Lidar in Unknown Environmental Conditions
No full textDiscriminating different underwater objects based on their material is of great interest in many maritime applications, such as safe navigation and seafloor mapping. Fluorescence Light Detection And Ranging (LIDAR) systems allow different materials to be discriminated based on their spectral fluorescence properties. However, acquisition conditions play an important role in underwater material discriminability, and a scarce and/or inaccurate knowledge of such conditions may impair final performance. In this work, we investigate underwater material discriminability in unknown environmental conditions. Experimental results obtained with synthetic data reveal the potential of the explored approach
An Overview of Background Modeling for Detection of Targets and Anomalies in Hyperspectral Remotely Sensed Imagery
No full textThis 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
Automated Underwater Object Recognition by Means of Fluorescence LIDAR
No full textThis paper focuses on automated recognition of underwater objects by means of light detection and ranging (LIDAR) systems. Differently from most works involved in underwater object recognition with LIDAR, where objects are recognized by their shape, here the interest is distinguishing objects on the basis of physical/chemical properties of object materials. To this aim, laser-induced fluorescence (LIF) spectroscopy is exploited, and an ad hoc signal processing chain is presented to effectively analyze the LIF spectra extracted at the detected object-range. Specifically, the goal is that of automatically recognizing the detected object with respect to a database (DB) of objects of interest, which have been previously spectrally characterized by means of laboratory fluorescence measurements. To this aim, suitable physics-based methodologies are proposed to compensate the signal for water-column effects. A decision-theory-based framework is developed to approach spectral recognition of the detected object with respect to the object DB. Experimental results from a laboratory test-bed show that the proposed processing chain is effective at automatically recognizing objects submerged in an artificial water column at different depths, based on a diverse DB of sample materials. The presented approach is shown to provide great potential for automated object recognition in marine and other water environments
Hyperspectral Airborne 'Viareggio 2013 Trial' Data Collection for Detection Algorithm Assessment
No full textFor many years, the entire target detection scientific community has felt the urge for fully ground-truthed hyperspectral imagery data sets expressly released for testing and comparing detection algorithms. Although a few excellent data-sharing efforts have been carried out in the last decade, the use of either restricted or not well ground-truthed imagery still remains a common practice in the target detection literature. In this paper, we provide an overview of a new hyperspectral data set that we release to the scientific community with the specific goal of fostering unbiased comparison and scientific discussions of anomaly detection (AD), object detection, and anomalous change detection (ACD) algorithms. The data set is fully ground-truthed and documented and includes scenarios and experiments specifically conceived for detection algorithm comparison and benchmarking. Insights about the various possible data exploitation tasks are provided by making reference to noise estimation and reduction, AD, spectral signature-based target detection (SSBTD), and ACD. Experimental results concerning ACD and SSBTD are presented and highlight the usefulness of this new data set from the data sharing and algorithmic comparison perspectives
AFRODiTE: A FluoRescence lidar simulator for underwater object detection applications
No full textA FluoRescence lidar simulator for underwater Object DeTEction applications (AFRODiTE) is proposed to generate inelastic backscattering signals returned from a water column both in the presence and in the absence of an underwater object. The simulator models the interaction of the transmitted laser pulse with the water medium, an underwater object, and the bottom. Specifically, AFRODiTE enables simulation of fluorescence backscattering signals for a variety of light detection and ranging (lidar) system characteristics, acquisition geometries, and water environmental conditions. With respect to models for the elastic backscattering lidar signals developed in the literature, AFRODiTE may be used to test and improve not only underwater object detection methodologies based on time-resolved lidar waveform analysis but also object recognition methodologies based on spectral analysis of the object fluorescence spectral signature. Experimental comparison with real signals measured by an advanced prototypal fluorescence lidar in a laboratory artificial water column shows that AFRODiTE is effective at reproducing the inelastic backscattering signals received by a lidar system from an underwater object. Furthermore, simulations of the received signals obtained reproducing immersion of objects in the waters of the Gulf of Mexico and the North Atlantic Ocean highlight AFRODiTE potential and flexibility for generating fluorescence lidar signals acquired in different operational scenarios on the basis of various system parameters, acquisition geometries, and water environments
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