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This article deals with the problem of improving the spatial resolution of hyperspectral (HS) data from the PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission. For this purpose, higher spatial resolution data from the Sentinel-2 (S2) mission are exploited. Particularly, 10 S2 bands at 10 and 20 m spatial resolution are used to accomplish the PRISMA super-resolution (SR) task. The article presents a new end-to-end procedure, called PRISMA-SR, that starting from the S2 data and the low-resolution PRISMA image, provides a super-resolved image with a spatial resolution of 10 m and the same spectral resolution as the PRISMA HS sensor. The first step of the PRISMA-SR procedure consists in fusing S2 data at different spatial resolutions to obtain a synthetic MS image with 10 m spatial resolution and 10 spectral bands. Then, an unsupervised procedure is applied to coregister the fused S2 image and the PRISMA image. Finally, the two images at different spatial resolutions are properly combined in order to obtain the super-resolved HS image. Solutions for each step of the PRISMA-SR processing chain are proposed and discussed. Simulated data are used to show the effectiveness of the PRISMA-SR scheme and to investigate the impact on its performance of each step of the processing chain. Real S2 and PRISMA images are finally considered to provide an example of the application of the PRISMA-SR. [ABSTRACT FROM AUTHOR]

In this work, we deal with the problem of atmospheric compensation (AC) of hyperspectral data collected in the visible and near-infrared (VNIR) spectral range. We propose the “learning-based” approach which uses artificial intelligence algorithms to directly estimate the spectral reflectance from the observed at-sensor radiance image. It uses a parametric regressor whose parameters are learned by means of a strategy based on synthetic data. Such data are generated taking into account 1) the radiative transfer in the atmosphere; 2) the variability of the surface spectral reflectance; and 3) the effects of signal-dependent random noise and spectral miscalibration errors. According to this general framework, we propose a specific multilinear regressor that starting from the knowledge of the atmospheric visibility compensates the water absorption and provides the spectral reflectance of each pixel of the analyzed image. Furthermore, a specific image-based procedure is presented for visibility estimation. The experiment over simulated data is presented and discussed. The test on simulated data aims at showing the effectiveness of the proposed strategy in a completely controlled environment. Experiments are also carried out on three real hyperspectral images acquired by two hyperspectral sensors. The obtained results confirm the effectiveness of the proposed approach by comparing the retrieved reflectance spectra with in-situ measurements or with those obtained by using a well-known commercial AC software. [ABSTRACT FROM AUTHOR]

The generalized likelihood ratio test (GLRT) is here combined with the nonparametric approach to derive a new adaptive detector for subpixel targets in hyperspectral images. Specifically, a variable bandwidth kernel density estimator (KDE) is employed for estimating the conditional probability density functions composing the GLRT. Although KDE has generally a low mathematical tractability, an approximated closed-form solution is here derived, thanks to an innovative and uncommon choice for the kernel function. Experimental results in subpixel target detection scenarios show that the proposed detector represents not only the natural evolution of but also a successful alternative to both very widely employed and very recently proposed GLRT-based detectors. [ABSTRACT FROM AUTHOR]

This article presents an automated underwater material recognition methodology for fluorescence light detection and ranging (LIDAR) invariant to environmental conditions (ARTEMIdE). Contrary to other state-of-the-art methods for submerged object recognition, ARTEMIdE can be applied when no a priori knowledge about environmental conditions is available and without resorting to any additional data besides the received signal and the fluorescence spectral signatures of the materials of interest. Experimental results over synthetic and real data show that ARTEMIdE is effective at automatically recognizing various object materials submerged at different depths within the water column. The presented approach reveals to provide great potential for many marine and submarine applications. [ABSTRACT FROM AUTHOR]

This tutorial follows and completes a previous work concerning how to solve the factors of ambiguity intrinsic in the definition of rotation matrices, to describe the relative orientation of one or more cameras acquiring images from different points of view. The two works, jointly, aim to provide an error-avoiding methodology to describe the whole relative acquisition geometry (pose). This is a crucial task for those aerospace applications based on the use of imaging devices, like flying objects detection and tracking, automatic guidance, tridimensional reconstruction, images georegistration, and change detection. To describe the pose, not only the orientation but also the relative shift should be taken into account. This can be conveniently modeled through a translation vector. Using it together with a rotation matrix it is possible to achieve the goal, but it is also prone to ambiguity. In this contribution, the main factors that result in such ambiguity are addressed. Through a detailed analysis, it is shown how to solve them, in order to manage the interaction between translation vectors and rotation matrices properly. This avoids the errors that frequently occur in practical applications, whenever it is required to find the transformation that makes the reference system of a camera coincident with the reference system of another, or to switch from the expression of the coordinates of a point of the scene in the reference system of a camera to its expression in the reference system of another. One of the reasons why errors are likely to be made in describing the pose is the lack of a commonly adopted choice for the camera reference system. To obviate this lack, the manuscript also presents a recommended definition for it. For the sake of completeness, the definition includes a second reference system for identifying points on the camera sensor plane, in order to permit describing also the projective transformation operated by the camera during the image formation process. [ABSTRACT FROM AUTHOR]

Rotation matrices are a convenient and intuitive way to describe algebraically the relative orientation of multiple cameras or of the same camera shooting from different points of view. However, the definition of a rotation matrix is prone to intrinsic ambiguity, which often leads to a mismatch with the physical rotation one wants to describe, even if the definition is mathematically correct. This is a common source of errors whenever it is required to compute a rotation matrix from camera orientation data, or vice versa, to recover such data from a given rotation matrix. This tutorial aims to describe and solve the main factors that generate the ambiguity in using rotation matrices and to permit dealing with them properly both in theory and in practice. Through a detailed analysis of these factors, which ranges from basic mathematical aspects to the notation used to refer to them, it is shown how to avoid errors in the algebraic description of the relative orientation of different cameras by means of rotation matrices. This work is followed by another contribution, in which the interaction between rotation matrices and translation vectors (used to describe the shifts between pairs of cameras) is also analyzed, and a recommendation on how to define a common reference system coherent with a camera (a crucial aspect to model the camera acquisition geometry) is given. The two contributions jointly embrace the entire description of the relative acquisition geometry of images taken from different points of view and provide a complete and error-free methodology to recover it or to extract useful data from it. This topic is particularly important in a wide variety of aerospace applications, which often rely on multiple imaging sensors whose information should be merged, or on imaging devices carried by manned or unmanned vehicles. Such applications range from flying object detection to tridimensional reconstruction by using aerial or satellite images to drone automatic navigation, to change detection for area monitoring to georegistration by ground-to-aerial image matching. [ABSTRACT FROM AUTHOR]

This letter presents a new method to compute alpha residuals (AR) in longwave infrared hyperspectral images. AR allow emissivity-based target detection being them a quantity, related to the target emissivity, which is less prone to errors in the estimated temperature. The proposed method improves existing approaches taking into account also the contribution of the atmospheric emitted radiance reflected by the object. Experimental results over synthetic data show the better performance of the new approach. [ABSTRACT FROM PUBLISHER]

This paper proposes an automatic target recognition (ATR) methodology for airborne hyperspectral imagery in which regions of interest (ROIs) containing anomalous objects are inspected for recognition of specific targets. ROI-by-ROI processing is carried out in a fully automated fashion and does not need operator intervention, thus being suitable for in-flight applications. The ROI-based ATR methodology is developed within a multiple hypotheses testing framework, and its key strengths are in the use of a bank of flexible and robust nonparametric detectors combined with an automated method for null hypothesis discrimination and an effective decision support system. Experimental results over multiple real hyperspectral images show the effectiveness of the proposed methodology for automatic recognition of several different targets embedded in various kinds of background. [ABSTRACT FROM PUBLISHER]

An analytical end-to-end model is developed to predict the performance of underwater object recognition by means of light detection and ranging (lidar) fluorosensors, as an aid to underwater lidar mission planning and system design. The proposed Performance prediction mOdel for Submerged object dEtection and recognitIon by liDar fluOrosensors in the marine eNvironment (POSEIDON) reproduces the overall end-to-end fluorescence lidar system chain—from signal generation, to signal propagation, acquisition, and processing. The goal is assessing the performance that may be obtained for spectral recognition of an underwater object in various operational scenarios in terms of several different performance metrics. In addition to the performance prediction models developed in the literature for airborne lidar bathymetry, POSEIDON embeds a novel comprehensive signal simulator that accounts for inelastic scattering phenomena as well as a signal processing module designed ad hoc to accomplish spectral recognition of an underwater object with respect to a data base of objects of interest spectrally characterized by their fluorescence spectral signatures. Test cases with a lidar system arranged in two configurations and several objects submerged at various depths in different Cases I and II waters were reproduced and explored. Results obtained within a Monte Carlo simulation framework provide proof-of-concept of POSEIDON performance forecasting capabilities for underwater object recognition. [ABSTRACT FROM PUBLISHER]

Exploitation of temporal series of hyperspectral images is a relatively new discipline that has gained a lot of attention from the image processing scientific community. In this paper, we consider the specific problem of anomalous change detection (ACD) in hyperspectral images and discuss how images taken at two different times can be processed to detect changes caused by insertion, deletion, or displacement of small objects in the monitored scene. We introduce the ACD problem using an approach based on the statistical decision theory and we derive a common framework including different ACD approaches. Far from being inclusive of all the methods proposed in the literature, this tutorial overview places emphasis on techniques based on the multivariate Gaussian model that allows a formal presentation of the ACD problem and the rigorous derivation of the possible solutions in a way that is both mathematically more tractable and easier to interpret. The unification of different approaches under a single rigorous statistical scheme provides both a tutorial overview of ACD techniques, and a useful instrument for researchers already familiar with the ACD problem. Dedicated preprocessing methods aimed at improving the robustness of the ACD process are also discussed. Real data are exploited to test and compare the presented methods, highlighting advantages and drawbacks of each approach. The tutorial aspect of the paper has suggested the use of a freely available data set. This should hopefully motivate the interested reader to experiment with the processing methods and performance evaluation chain presented herein. [ABSTRACT FROM PUBLISHER]

For 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. [ABSTRACT FROM PUBLISHER]

This paper presents an automated approach for peach fruit maturity grading that, by exploiting fiber-optic spectroscopy-based sensors and multivariate processing techniques, minimizes the operator intervention while reducing discharge and waste. The use of a spectroscopic sensor complies with the so-called nondestructive measurement method, which enables fast repeated measurements to be performed at the single fruit level while avoiding fruit damage and loss. Maturity grading is accomplished by retrieving estimates of the fruit flesh firmness by means of multivariate retrieval techniques applied to the reflectance spectra acquired with the spectrometer and by processing the retrieved values within the framework of a maturity fuzzy classifier. A decision support system is developed to provide the user with maturity category decision and associated reliability. Experimental results show that the approach is effective for automated maturity grading of peach fruits affected by a high degree of variability. This paper lays the foundations for the realization of easy-to-use sustainable automated maturity grading systems. [ABSTRACT FROM PUBLISHER]

A 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. [ABSTRACT FROM AUTHOR]

This 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. [ABSTRACT FROM PUBLISHER]

The 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. [ABSTRACT FROM PUBLISHER]

We propose a local anomaly detection strategy for multi-hyperspectral images in which the background probability density function is estimated with a kernel density estimator and locally adaptive information extracted from the image is injected into the bandwidth selection process. Results for multispectral images of different scenarios show the benefits of the proposed strategy regarding its effectiveness both at detecting anomalies and at avoiding the crucial issue of properly selecting the kernel-width parameter. [ABSTRACT FROM PUBLISHER]

This letter presents a scheme for detecting global anomalies, in which a likelihood ratio test based decision rule is applied in conjunction with an automated data-driven estimation of the background probability density function (PDF). The latter is reliably estimated with a nonparametric variable-band width kernel density estimator (VKDE), without making any distributional assumption. With respect to conventional fixed bandwidth KDE (FKDE), which lacks adaptivity due to the use of a bandwidth that is fixed across the entire feature space, VKDE lets the bandwidths adaptively vary pixel by pixel, tailoring the amount of smoothing to the local data density. Two multispectral images are employed to explore the potential of VKDE background PDF estimation for detecting anomalies in a scene with respect to conventional nonadaptive FKDE. [ABSTRACT FROM PUBLISHER]

Anomaly detection (AD) in remotely sensed hyperspectral images has been proven to be valuable in many applications. In this paper, we propose a scheme for detecting global anomalies in which a likelihood ratio test-based decision rule is applied in conjunction with automated data-driven estimation of the background probability density function (PDF). Specifically, the use of both semiparametric (finite mixtures) and nonparametric (Parzen windows) models is investigated for background PDF estimation. Although such approaches are well known in multivariate data analysis, they have been very seldom applied to estimate the hyperspectral image background PDF, mostly due to the difficulty of reliably learning the model parameters without operator intervention. In this paper, semi and nonparametric estimators have been successfully employed to estimate the image background PDF with the aim of detecting global anomalies in a scene benefiting from the application of ad hoc Bayesian learning strategies. Two real hyperspectral images have been used to experimentally evaluate the ability of the proposed AD scheme resulting from the application of different global background PDF models and learning methods. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the problem of signal subspace identification (SSI) and dimensionality reduction in hyperspectral images. We consider two recently proposed SSI algorithms: the Maximum Orthogonal Complement Analysis (MOCA) algorithm and the Robust Signal Subspace Estimator (RSSE) algorithm. Such algorithms are robust to the presence of rare signal components and are particularly effective in reducing the number of features in the preprocessing step for small target detection applications. In this paper, MOCA and RSSE are briefly revisited and integrated in a common theoretical framework in order to better highlight and understand their peculiarities. Furthermore, their performances are compared in terms of computational complexity and of their ability to address both the abundant and the rare signal components. A modified version of the MOCA is also introduced, which is computationally more efficient than the original algorithm. Results on simulated data are discussed, and a case study is presented concerning real Airborne Visible/Infrared Imaging Spectrometer data. [ABSTRACT FROM AUTHOR]

A new frontier of nondestructive measurement exploitation is explored in this paper as concerns the food traceability and control arena. Presented is a method for nondestructive automated peach tree rootstock classification by means of high spectral resolution spectroscopy and multivariate signal processing. Many studies have shown that rootstock has significant impact on the quality and maturity of peach fruits. Rootstock knowledge not only enables fruit traceability but also helps in selecting the best storage condition and marketing channel. A novel automated method is presented to classify peach fruits with respect to their rootstock based on multivariate signal processing of fruit skin reflectance spectra, which are highly affected by physical and biochemical phenomena associated with fruit quality and maturity. The experimental results exploiting measurements of fruit skin reflectance spectra acquired in the visible and near infrared ranges with a high-resolution spectrometer show that automated rootstock classification by spectroscopic measurements and signal processing techniques is feasible and effective and has great potential in horticultural engineering. [ABSTRACT FROM PUBLISHER]