This paper provides an overview of the main activities and results of HYPERHEALTH project (funded by the Italian Space Agency). Specific focus of this paper is hyperspectral PRISMA data exploitation, mostly as regards PRISMA-based atmospheric constituent estimation and allergenic vegetation monitoring.

PRISMA is a hyperspectral pushbroom sensor, launched by the Italian Space Agency in 2019. PRISMA collects the reflected Earth signal from VNIR to the SWIR with 230 spectral bands with a variable FWHM according to the prism dispersion element. This work intends to develop a procedure suitable to monitor the consistency of photon and thermal noise components across a times series of L1 radiance images collected on different Mediterranean scenarios (i.e. rural and coastal). To improve the retrieval of the useful signal and the random noise on PRISMA images the spatial variability of the scenes has been considered in the new version of the HYperspectral Noise Parameters Estimation (HYNPE) algorithm. The procedure, tested on two PRISMA time series, has assessed quite stable and coherent values for the retrieved noise coefficients, not significantly affected by seasonal radiance variations and scene characteristics

HYPERHEALTH project is co-funded by Italian Space Agency (ASI) in the framework of the "PRISMA Scienza" program. The program supports R&D projects proposed by experts in hyperspectral remote sensing sector from national public research institutions to industries, also in the framework of international partnerships. The aim is designing, developing and testing innovative methods, techniques and algorithms for exploitation of hyperspectral data, with reliable perspectives as to engineering and pre-operational development, thus contributing to the improvement of socio-economic benefits of the end-user community. This paper outlines HYPERHEALTH main goals and activities.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

The rapid growth of hyperspectral satellite missions and the subsequent availability of hyperspectral images have encouraged the remote sensing community to investigate their potential in estimating the concentration of gases, as CH4and CO2, which are related to the greenhouse effect. Though satellite hyperspectral sensors are not specifically designed for this purpose, they are expected to complement more specific satellite missions, such as NASA’s OCO-2 and OCO-3, both in terms of enriched temporal sampling and improved spatial resolution. In this work, we present a new method to estimate the column-averaged dry-air mole fraction of CO2from hyperspectral data on a per-pixel basis. The method, which is here tailored to PRISMA images, leverages the spectral radiance samples collected in the SWIR spectral region around the CO2absorption band at 2000 nm. By assuming a linear model to describe the dependence of the observed radiance on the CO2concentration, the estimation problem is reduced tomatched filteringand can be effectively implemented in compliance with the low computational burden required to perform a pixel by pixel analysis. The performance of the presented method is investigated by means of a rigorous, physically based simulator that accurately reproduces the at-sensor radiance allowing one to check the validity of the assumptions and to assess the algorithm accuracy. The results show that the presented algorithm outperforms a benchmark CIBR based approach, which has been proposed in the literature to get fast per-pixel estimates of CO2concentration.

A Bayesian Likelihood Ratio Test (LRT) detector is analytically derived here for the replacement target model and using the non-parametric variable-bandwidth kernel density estimator to model the hyperspectral background. The detector is compared to the recent Generalized LRT detector, based on the same non-parametric model for the background. Experimental results obtained on two hyperspectral sub-pixel target detection scenarios reveal the great potential of the proposed detector and set the basis for future investigations.

In satellite hyperspectral sensors, a standard procedure based on homogeneous reference sources is used to regularly update the map of defective pixels (DPs). Unfortunately, this procedure often fails to detect some DPs both because they may arise in the interval between two standard calibration steps and because they may be characterized by subtle or unexpected signal values. The resulting hyperspectral image is affected by residual nonuniformity noise which correlates in the along track direction. This noise source reduces the quality of hyperspectral products, such as classification, unmixing and material detection. In this paper, we present a new procedure to find the location of the residual DPs in the detection matrix and we also propose an effective method to estimate the missing radiance values inferring them from the image pixels by leveraging both spatial and spectral correlation. The procedure, here tailored to PRISMA hyperspectral images, is quite general and can be easily adapted to process images recorded by any satellite pushbroom hyperspectral sensor. The improved image quality yielded by the proposed procedure is first demonstratedqualitatively, by comparing the GRX maps on the original image with those obtained after detection of the DPs and correction of their radiance values. The image quality improvement is thenquantifiedon a set of nine PRISMA images, recorded in an interval of about two years, using twoad-hocdefined indexes. The analysis is carried out separately for the VNIR and SWIR spectrometers of the PRISMA mission.

In this work we extend the recently proposed Learning-Based approach to Atmospheric Compensation (LBAC) with respect to two different aspects: 1) the platform for data acquisition and 2) the spectral range covered by the sensor. Particularly, we propose the extension of LBAC to spaceborne hyperspectral sensor operating in the Visible Near InfraRed (VNIR) and Short-Wave InfraRed (SWIR) portion of the electromagnetic spectrum. We specifically refer to the sensor of the PRISMA (PRecursore IperSpettrale della Missione Applicativa) mission, the recent Earth Observation mission of the Italian Space Agency that offers a great opportunity to improve the knowledge on the scientific and commercial applications of spaceborne hyperspectral data. Results obtained on PRISMA hyperspectral images are presented and discussed.

This work deals with submerged object recognition methodologies with fluorescence LIDAR that can be applied when no prior information about environmental conditions is available. Previous invariant methods rely upon conventional unconstrained and constrained subspace projection concepts. This paper investigates application of sparsity-based concepts within this framework. A method enforcing both L1and L2norm penalties is investigated. Both synthetic and real data are employed to evaluate the potential of the method. Experimental results reveal that sparsity-based methods can be useful in this context and deserve further investigation.

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.

Water quality assessment plays an important role in sustainable development of natural resources. Fluorescence LIDAR is among the preferred remote sensors used in water quality monitoring. This work presents an underwater fluorescence LIDAR simulator for water quality assessment developed to design, test, and validate methodologies for the retrieval of key biophysical parameters or to discriminate dissolved substances and pollutants. Experimental results illustrating the usefulness of the simulator are provided focusing, as a case-study, on a key water quality indicator playing a major role in the global carbon balance, namely the Chromophoric/colored Dissolved Organic Matter (CDOM).

This work examines classical, more recent, and new hyperspectral detection algorithms that stem from the common framework of the decision-theory based statistical likelihood ratio test (LRT). Within this context, the tradeoffs involve improving models of target spectral variability, accurately characterizing the background, and producing a detector with closed-form solution. There is no algorithm that has shown universally best performance, but each of the algorithms can be specifically suited to deal with a given target detection scenario. Experimental results featuring real hyperspectral data are shown to compare the detection performance of the examined algorithms on two case-study target detection scenarios.

This letter presents a new method to detect materials with known spectral emissivity in data acquired by longwave infrared hyperspectral sensors. The proposed approach differs from existing methods because it takes into account the uncertainty of the downwelling radiance. Such uncertainty is addressed assuming that the downwelling radiance spans a low-rank subspace whose basis matrix is learned, regardless of the analyzed image, from MODTRAN simulated spectra. The analysis, carried out over data simulated by considering different atmospheric conditions, surface temperatures, and emissivity spectra, shows the effectiveness of the proposed algorithm.

Estimation of the total column water vapor (CWV) content of the atmosphere plays an important role in the atmospheric compensation (AC) of remotely sensed hyperspectral images collected in the visible and near infrared (VNIR) spectral range. Most of the proposed CWV retrieval methods provide accurate estimates as long as other significant atmospheric parameters are known. Those parameters are not generally available and must in turn be estimated. In this article, a new approach based on deep learning is proposed that allows the estimation of CWV without the knowledge of the atmospheric visibility, the solar zenith angle, and the atmospheric point spread function (PSF). The proposed approach includes a training strategy based on synthetic data that are generated according to an accurate radiative-transfer model, and by exploiting reflectance spectral libraries and the MODTRAN radiative-transfer code. Experiments on simulated data are carried out to analyze the performance of the proposed deep neural network with reference to both aerial and satellite applications. Furthermore, an example of the results provided by the network in a real application is shown. For this purpose, the network is applied to data acquired by an airborne hyperspectral sensor operating in the VNIR spectral range.

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.

This work presents a submerged object recognition method with fluorescence LIDAR that can be applied when no a priori information about environmental conditions is available. Whereas conventional methods require the availability of either LIDAR measurements of water samples or accurate knowledge about environmental conditions, the approach investigated here remove such assumptions. Experimental results on real data acquired in laboratory show the potential of the approach.

This work presents a novel target detector that combines a nonparametric approach for conditional probability density function (pdf) estimation and an adaptive estimation of the target strength of the additive model it is based on. The variable bandwidth kernel density estimator is employed for pdf estimation within the Generalized Likelihood Ratio Test (GLRT) framework and a closed-form solution is found. Experimental results featuring hyperspectral data of a real subpixel target detection scenario reveal the potential of the proposed approach.

In this paper, we investigate the temperature and emissivity separation (TES) problem from hyperspectral data acquired in the long-wave infrared region (LWIR) of the electromagnetic spectrum. We derive a general class of TES algorithms [subspace-based TES (SBTES)] relying on the assumption that the emissivity spectra of natural and man-made materials can be well represented in a given subspace of the original data space. Specifically, by exploiting the subspace representation and the Gaussian model for the noise affecting LWIR hyperspectral data, we approach TES under a statistical perspective by obtaining the maximum likelihood estimates of both the temperature and the spectral emissivity. The proposed approach originates several algorithms whose specific form depends on the particular basis matrix adopted to address the emissivity subspace. We study the performance of the presented class of algorithms and derive theoretical bounds on the accuracy of the temperature and emissivity estimators. Furthermore, by specifying two basis matrices for the emissivity subspace, we propose two different algorithms within the SBTES class. Finally, we present the results of an extensive experimental analysis carried out over simulated data to assess and compare the performance of the two presented algorithms.

This paper deals with atmospheric correction in hyperspectral data acquired in the long-wave infrared (LWIR) spectral range. Atmospheric compensation (AC) is approached from a new perspective in that it is reformulated as an estimation problem on a low-rank subspace. Specifically, a subspace-based model is exploited to represent both the atmospheric transmittance and the upwelling radiance. Taking into account the inherent correlation between these two quantities, we adopt a subspace model that constrains them to vary in a physically consistent way. Exploiting such a model, AC is formulated as a nonlinear least-squares estimation problem. An automatic procedure is proposed to estimate the unknown parameters, which is based only on the image analysis and does not need any atmospheric measurements. Results of an extensive experimental analysis carried out on simulated data are used to discuss the performance of the algorithm with respect to different atmospheric conditions. Finally, experimental evidence over a real LWIR hyperspectral image is provided.

In this paper we propose novel semi- and non- parametric detectors to be used with the additive target signal model within the general detection framework of the likelihood ratio test. In the semi-parametric detector, the Gaussian mixture model is employed to estimate a lower dimensional approximation of the background probability density function (PDF), whereas a multivariate kernel density estimator is employed to estimate the PDF in the multidimensional space within the non-parametric approach. Target detection experiments are carried out using the hyperspectral airborne “Viareggio 2013 trial” data set. The detectors are shown to provide promising results for the detection of the targets of interest deployed in the scene and outperform the well-known Adaptive Matched Filter detector.

In this work a novel target detector is proposed that is nonparametric in terms of conditional probability density function (pdf) estimation and parametric with respect to the target strength of the additive model it relies upon. The variable bandwidth kernel density estimator is employed to estimate the conditional pdfs, whereas the target strength is estimated via the Maximum Likelihood approach. Experimental results over real hyperspectral data show that the detector succeeds in detecting target objects embedded in a complex background and in providing reasonable estimates for the target strengths.

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.

Atmospheric compensation (AC) is a fundamental and critical step for quantitative exploitation of hyperspectral data. It is the means by which the reflectance of an object/material is estimated from the measured at-sensor radiance. Such reflectance is the inherent signature that is used to identify various materials in a monitored scene. AC is quite complex and is hampered by the large amount of uncontrollable variables that play a role: just think about the spatial variability of some atmospheric constituents such as water vapor and aerosols, or to the rapidly spatially varying effects of the radiation coming from adjacent areas. Though, in principle, some atmospheric parameters and radiometric quantities such as solar irradiance and sky irradiance can be measured during the flight, in practice such measures are rarely available in an operational framework or are taken at a single point of the surface ignoring their spatial variation. Thus, a prompt quantitative exploitation of hyperspectral data for operational purposes, such as material identification and object detection, requires unsupervised and accurate AC procedures that can learn from the image itself the parameters of the inversion model and follow their variability within the scene. In this framework, we present a new unsupervised methodology for AC of airborne hyperspectral images in the visible and near-infrared spectral range. The proposed methodology relies on a radiative transfer model accounting for the adjacency effect and allows the estimation of relevant atmospheric parameters. Specifically, it embeds two new algorithms for the estimation of: 1) aerosol and atmospheric visibility and 2) the water vapor content of the atmosphere accounting for the spatial variability of such a parameter. The two algorithms significantly differ from those adopted by existing state-of-the-art approaches or in commercial packages such as fast line-of-sight atmospheric analysis of spectral hypercubes and airborne atmospheric and topographic correction algorithm. In this paper, we present the detailed description of the new AC methodology, and we analyze the results provided by the algorithm over real data.

The knowledge of atmospheric column water vapor concentration is crucial for compensating water absorption effects in remote sensing data. Several algorithms for the estimation of such a parameter were proposed in the past. One of the most effective algorithms is the atmospheric precorrected differential absorption (APDA) technique. APDA relies on a simplified radiative transfer model (RTM) that does not account for the spatial variability of the adjacency effects. In this paper, we study the impact of the simplified RTM assumption on the performance of the algorithm by exploiting a more realistic and well-established RTM. Starting from such a model, we derive a new water retrieval algorithm called low-rank subspace projection-based water estimator. It exploits the high degree of spectral correlation experienced in the reflectances of most of the existing materials. An extensive experimental analysis is carried out on simulated data in order to assess and compare the performance of the two algorithms. Simulation results allow the critical analysis of the two algorithms by highlighting their strengths and drawbacks.

Target detection experiments with a novel non-parametric detector are carried out exploiting the availability of a new hyperspectral data set featuring a suburban scene with several different targets. Benefiting from its non-parametric nature and from its data adaptivity deriving from the variable-bandwidth approach, the detector is shown to provide promising results for the detection of the targets of interest both in global and local configurations.

Detecting and recognizing underwater objects is a topic of great interest in many maritime applications, such as harbor security, safe navigation of autonomous underwater vehicles, and safety of the littoral zone [1]. Fluorescence Light Detection And Ranging (LIDAR) systems play an important role in this context. In this work, a framework for predicting the performance of underwater object recognition by means of fluorescence LIDAR is proposed with the aim of assisting the user during operations such as mission planning and LIDAR system design. Experimental results obtained within a Monte Carlo simulation framework reveal the potential of the proposed framework.

An experimental study is presented with the goal of investigating application of the forward modeling (FM) approach to target detection to hyperspectral images acquired over an urban scenario, where the effects of variability and uncertainty of atmospheric and illumination conditions are enhanced. The study exploits the availability of a new hyperspectral data set acquired in May 2013 over the city of Viareggio, Italy. A physics-based model is assumed for radiation transfer in the atmosphere and variability around nominal acquisition conditions is accounted for by means of multiple executions of a radiative transfer code and a subspace-based target detection framework. Experimental results featuring several ground-truthed targets in an urban scenario confirm the effectiveness of the FM target detection approach.

This paper presents a new procedure to make the at-sensor radiance comparable with the object reflectance spectrum in order to perform object detection and identification. A new transformation is proposed to account for the non-perfect knowledge of illumination, viewing and atmospheric conditions. The transformation, which is applied to both the image data and the object spectrum, takes into account the uncertainty of the acquisition conditions by resorting to a subspace based approach. The subspace is obtained by using a parametric physics-based radiative transfer model and by resorting to the MODerate resolution TRANmission (MODTRAN) radiative transfer code. The procedure is entailed in a target detection and identification scheme and is applied to real hyperspectral data acquired during a recently performed measurement campaign.

This paper presents a multi-temporal approach to compensate the atmospheric effects in spaceborne hyperspectral images. It focuses on applications where a hyperspectral sensor periodically acquires images of the same scene and such images have to be classified. The method assumes that a reference reflectance image of the region of interest, equipped with an accurate classification map, is available. Such an image and the corresponding classification map represent the database that will be a reference for the classification of temporally subsequent images over the same scene. The multi-temporal approach allows the classification to be performed on at sensor radiance data and it does not require the knowledge of the atmospheric conditions at the acquisition time.

This paper presents an end-to-end fluorescence LIDAR system modeling approach for predicting the performance of underwater object recognition. The overall LIDAR system chain is modeled, starting from LIDAR pulse generation up to collection and processing of the inelastic backscattering signal returned by the underwater object. A comprehensive simulator to reproduce in-water LIDAR propagation phenomena is included in the model. The proposed approach is capable of predicting underwater object recognition performance under various different fluorescence LIDAR system characteristics as well as viewing geometries and environmental conditions. Experimental results featuring immersion of an object in Case 1 and Case 2 waters show the potential of the proposed modeling approach.

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

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.

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.

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.

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.

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.

The main objectives of the PRISMA (Hyperspectral Precursor of the Application Mission) mission are: the implementation of an Earth Observation pre-operative payload, the in-orbit demonstration and qualification of an Italian state-of-the-art hyperspectral/panchromatic technology and the validation of end-to-end data processing system able to support the development of new applications based on high spectral resolution images. The aim of the paper is to provide an overview of the PRISMA mission by describing the current status of the program and giving a brief outline of the work done till now in the framework of the SAP4PRISMA project scientific studies in supporting the exploitation of the future PRISMA hyperspectral images for environmental applications.

Orthogonal subspace projection (OSP) is a powerful tool for dimensionality reduction (DR) in hyperspectral images (HSIs). In the OSP approach, the basis of the signal subspace must be estimated from the data themselves. Such estimation task is referred to as signal subspace identification (SSI). Most of the SSI methods in the literature are based on the analysis of the data second-order statistics (SOS) and have two main drawbacks: 1) They do not take into account the rare signal components (or rare vectors), 2) they assume that noise is spatially stationary. Rare vectors are those signal components that are present in pixels scarcely represented in the image and linearly independent on the signal components characterizing the rest of the image pixels. SOS-based SSI algorithms estimate the signal subspace addressing mostly the background and ignoring the presence of the rare pixels. This may be detrimental for the performance of detection algorithms when DR is adopted as a pre-processing step in small target detection applications. In this paper, a new technique for SSI in HSIs is presented. The algorithm is developed to account for both the abundant and the rare signal components. The method is derived by assuming a signal-dependent model for the noise affecting the data. This makes the SSI algorithm particularly suitable for the processing of images acquired by new generation sensors where, due to the improved sensitivity of the electronic components, noise includes a signal-dependent term. Results on simulated data are discussed, and the comparison with a recently proposed technique based on the analysis of SOS is performed. Furthermore, the results obtained by applying the SSI algorithm to a real HSI affected by signal-dependent noise are presented and discussed.

In this paper, we investigate the constant false-alarm rate (CFAR) property of the RX anomaly detector which is widely used for the analysis of hyperspectral data. The RX detector relies on an adaptive scheme where the mean vector and the covariance matrix of the background are locally estimated from the image pixels themselves. First, demeaning is accomplished by removing the estimated local background mean value, and then, the covariance matrix is estimated in a homogeneous neighborhood of each pixel. In principle, if the local mean is perfectly removed and the covariance matrix is estimated from background pixels sharing the same covariance matrix, the RX algorithm has the CFAR property, which is highly desirable in practical applications. The CFAR behavior of the algorithm also requires the spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new-generation sensors, such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal level, is not negligible. This has motivated us to analyze the behavior of the RX algorithm with respect to the CFAR property in data affected by signal-dependent (SD) noise. In this paper, we show both theoretically and experimentally that the SD noise is one of the causes of the non-CFAR behavior of the RX detector that we have experienced in many practical situations. We propose a strategy to enhance the robustness of the anomaly detection scheme with respect to the CFAR property based on an adaptive nonlinear transform aimed at reducing the dependence of the noise on the signal level. Experiments on simulated data and real data collected by a new hyperspectral camera are also presented and discussed.

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.

In this paper we investigate the effects of the signal dependent noise on the CFAR property of the RX anomaly detector. The CFAR behaviour of the algorithm was proved under the assumption of spatial stationarity of the random noise affecting the hyperspectral image. In data collected by new generation sensors such an assumption is not valid because photon noise contribution, which depends on the spatially varying signal levels, is not negligible. In this paper, experiments on real data collected by a new hyperspectral camera are discussed in order to show that the signal dependent noise is one of the causes of the non-CFAR behaviour of the RX detector we have experienced in many practical situations.

This work investigates the effects of target signal contamination over local RX anomaly detection performance. An analytical model for signal contamination is introduced where several parameters, such as the contamination fraction and the contaminating signal strength, are employed to derive an objective measure of contamination. Results from Monte Carlo simulations show the relevance that signal contamination may have in practical applications.

The SAP4PRISMA is a four year research project which aims at developing algorithms and products for the future PRISMA mission. The project started on May 2010 and is now entering his full activities as the ”PRISMA like” data set has been defined and the test areas were selected. The paper describes the main project objectives and the activities realized in the first 9 months of the project.

In the framework of the monitoring of structures and infrastructures from environmental disasters, the COSMO-SkyMed constellation has a huge potential, thanks to up to metric spatial resolution, short revisit time, and the day/night all-weather acquisition capability ensured by SAR. This paper focuses on the scientific results of the project “Development and validation of multitemporal image analysis methodologies for multirisk monitoring of critical structures and infrastructures,” funded by the Italian Space Agency. Several change-detection, data-fusion, and feature-extraction techniques, which were developed and experimentally validated in the project for COSMO-SkyMed imagery and for their integration with other data sources (including very high resolution optical data), are described and examples of processing results are discussed.

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.

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.

In this paper, a new algorithm for striping noise reduction in hyperspectral images is proposed. The new algorithm exploits the orthogonal subspace approach to estimate the striping component and to remove it from the image, preserving the useful signal. The algorithm does not introduce artifacts in the data and also takes into account the dependence on the signal intensity of the striping component. The effectiveness of the algorithm in reducing striping noise is experimentally demonstrated on real data acquired both by airborne and satellite hyperspectral sensors.

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.

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.

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.

This paper proposes a fully unsupervised anomaly detection strategy in hyperspectral imagery based on mixture learning. Anomaly detection is conducted by adopting a Gaussian mixture model (GMM) to describe the statistics of the background in hyperspectral data. One of the key tasks in the application of mixture models is the specification in advance of the number of GMM components, the determination of which is essential and strongly affects detection performance. In this work, GMM parameters estimation was performed through a variation of the well-known expectation maximization (EM) algorithm that was developed within a Bayesian framework. Specifically, the adopted mixture learning technique incorporates a built-in mechanism for automatically assessing the number of components during the parameter estimation procedure. Then, generalized likelihood ratio test (GLRT) is considered for detecting anomalies. Real hyperspectral imagery acquired by an airborne sensor is used for experimental evaluation of the proposed anomaly detection strategy.

This paper deals with the problem of signal subspace estimation for dimensionality reduction (DR) in hyperspectral images in the presence of rare pixels, i.e., pixels that are scarcely represented in the image and containing spectral components that are linearly independent of the background. Most of the classical methods proposed in the literature are based on the analysis of second-order statistics (SOS), which are weakly influenced by the rare signals. Therefore, such techniques estimate the signal subspace addressing mostly the background and ignoring the presence of rare pixels. This may reduce the target/background spectral contrast, thus decreasing the detection performance when DR is adopted as preprocessing task in small-target detection applications. In this paper, a new robust algorithm, namely, robust signal subspace estimation (RSSE), is developed, which preserves both abundant and rare signal components. It combines the analysis of SOS and a recent approach based on the analysis of thel2infinnorm. The novel contribution of this paper is twofold. First, the RSSE algorithm is presented, which includes a new iterative procedure to derive the signal subspace and an original statistical method to estimate the data dimensionality. Second, anad hocsimulation strategy is proposed to assess the performance of signal subspace estimation methods in the presence of rare signal components. The procedure is adopted to compare the RSSE algorithm with a classical technique based on the analysis of SOS. The results obtained by applying the two methods on a real Airborne Visible Infrared Imaging Spectrometer hyperspectral image are also presented and discussed.

A dual infrared system to assist a driver in bad visibility conditions is studied. The problem of selecting the best multiresolution-based image fusion technique is addressed with reference to automotive scenarios. A new method for objective evaluation of multisensor image fusion strategies is presented for the optimal design of the fusion process. Multiresolution-based fusion methodologies are compared, and experimental results obtained from a prototype dual infrared camera system are shown and analyzed. Numerical results, in terms of the quality of the fused images and of the computational load, are presented and discussed. The effectiveness of the dual infrared system in urban and extraurban automotive scenarios is illustrated with a number of examples.

This paper deals with the problem of signal subspace estimation and dimensionality reduction (DR) in hyperspectral images. A new algorithm is presented which preserves both the abundant and the rare signal components and is therefore suitable for DR in target detection applications. Results obtained by applying the new procedure and a classical method based on the analysis of the second order statistics are presented and discussed with reference to real AVIRIS data.

In this manuscript we investigate the efficient implementation of anomaly detection strategies in hyperspectral images. We especially focus on methods to reduce the computational complexity for a fast implementation of the detection algorithms. In particular, we consider two strategies based on data fusion methods applied to the outputs of the optical heads of the hyperspectral sensor. Furthermore, we consider, two computationally efficient implementations of anomaly detection where the well known RX algorithm is applied to hyperspectral data after dimensionality reduction. The detection performances of the anomaly detection strategies are compared using real data acquired by the MIVIS sensor. An estimate of the reduction of the computational load achieved with the different techniques is also provided.

In this paper a new strategy aimed at reducing the computational complexity in hyperspectral anomaly detection is introduced. It is based on the fusion of the results obtained by applying the RX detector to the data measured by the different optical systems in the adopted hyperspectral sensor. Two feature level fusion criteria are derived and the computational complexity of each of them is evaluated. A comparison among the RX algorithm detection performance and the ones of the proposed anomaly detectors is provided by considering a data set acquired by an airborne hyperspectral sensor.

Video sequence stabilization permits an accurate analysis of the video originate from sources as video cameras or IR sensors. In fact, a system of video stabilization allows the availability of aligned image sequences and, consequently, to improve the operator analysis of the scenario. In this paper different image processing methods are analyzed to remove the camera unwanted motions and reconstruct a stabilized sequence for real time applications. In particular three methodologies are compared in term of computational load and of the expected scenarios to determine the best strategies to be applied in a real time system.

In this paper a methodology for fusion quality evaluation is proposed in the case of many source images to be fused in a single one. Various multiresolution fusion methodologies are compared, and experimental results are shown. Numerical results, in terms of quality of the fused images and of computational load, are presented and discussed with reference to avionic scenarios.

In this paper a new sub-pixel target detector for hyper-spectral images, based on the stochastic mixing model (SMM), is presented. The SMM models a mixed pixel, under the target present hypothesis, as linear combination of target and background spectra. Unlike the linear mixing model (LMM), target and background are modeled as random vectors in order to characterize their spectral variability. By assuming the target spectrum deterministic and known, a SMM based detection strategy is derived by computing the least square mean error (LSME) estimate of the target fraction in the observed pixel. This approach provides a closed form detector statistic as opposite to other SMM based detectors proposed in the literature. The new algorithm and the adaptive matched subspace detector (AMSD), based on the LMM, are applied to a MIVIS data set and the experimental results are compared by means of a suitable performance index.

This work deals with the problem of background removal in infra-red (IR) image sequences. Background removal is a basic step to detect small targets in surveillance systems based on IR images. The paper refers to a general background removal procedure that consists in estimating and subtracting the background in each frame of the IR sequence. The estimation step is accomplished by means of linear and non linear filters. The focus of this work is on techniques adopting four different filters: 1) the 2D window average filter; 2) the 2D MEDIAN filter; 3) the max/median filter; 4) the max/mean filter. In the paper a methodology to experimentally compare the performance of the different techniques is described and the results obtained over real IR data are discussed.

In this manuscript we investigate on the statistical modeling of hyperspectral data. Accurately modeling real data is of paramount importance in the design of optimal classification or detection strategies and in evaluating their performances. In the work three nonGaussian models are considered and their capability in characterizing the statistical behavior of real data is discussed with reference to a data set acquired by the multispectral infrared and visible imaging spectrometer (MIVIS) sensor.

This paper deals with the sub-pixel target detection problem in hyper-spectral images. The problem is approached by modeling the mixed spectrum with both the linear mixing model (LMM) and the stochastic mixing model (SMM). A detection strategy is derived by assuming the SMM. In the proposed algorithm, detection is accomplished by testing the values of the maximum a-priori probability (MAP) estimate of the target's abundance that represent the fraction of the spectrum in the observed pixel due to the target. The algorithm has been applied to experimental images and the results have been compared with the ones obtained by the adaptive matched subspace detector (AMSD) based on the LMM

A new algorithm for hyperspectral image segmentation based on the statistical approach is presented. The algorithm is completely unsupervised and relies only on the spectral information. The hyperspectral image is statistically characterized by means of the Gaussian Mixture Model (GMM). Preliminary results obtained on experimental data are presented and discussed.

In this paper we present an automatic procedure for the detection of long-range airborne targets in maritime naval surveillance systems. The algorithm performance is investigated via simulation and compared to the one of the classical moving window detection algorithm.

An efficient algorithm for endmember selection is illustrated. Endmembers are estimated by an unsupervised segmentation procedure based on spectral analysis. Preliminary results obtained on experimental data are presented and discussed.

Multivariate Principal Component Analysis (MPCA) is used to decompose a series of AVHRR SST maps and SeaWiFS phytoplankton pigment concentration maps relative to the Alboran Sea area (Western Mediterranean Sea) acquired during the period from November 1997 to October 1998. The results of MPCA decomposition are presented and discussed.

In this paper, we describe a fuzzy model for the estimation of sea water optically active constituents concentration in case II water. The model is extracted automatically from the data through a two step procedure. First, a fuzzy clustering algorithm is applied to identify a compact initial rule-based model. Then, the model is optimized by means of a genetic algorithm which tunes the rules so as to minimize the error between desired and predicted outputs. Appropriate constraints maintain the semantic properties of the initial model during the genetic evolution. The fuzzy model has been tailored to the multispectral data format of the MEdium Resolution Imaging Spectrometer (MERIS) on board the ESA-ENVISAT satellite launched in March 2002.

The authors present a new methodology for estimating the concentration of sea water optically active constituents from remotely sensed hyperspectral data, based on generalized radial basis function neural networks (GRBF-NNs). This family of NNs is particularly suited to approximate relationships like those between hyperspectral reflectance data and the concentrations of optically active constituents of the water body, which are highly nonlinear, especially in case II waters. Three main water constituents are taken into account: phytoplankton, nonchlorophyllous particles, and yellow substance. Each parameter is estimated by means of a specific multi-input single-output GRBF-NN. The authors adopt a recently proposed network learning strategy based on the combined use of the regression tree procedure and forward selection. The effectiveness of this approach, which is completely general and can be easily applied to any hyperspectral sensor, is proved using data simulated with an ocean color model over the channels of the medium resolution imaging spectrometer (MERIS), the new generation ESA sensor to be launched in 2001. The authors define the estimation algorithms over waters of cases I, II, and I+II and compare their performance with that of classical band-ratio, single-band, and multilinear algorithms. Generally, the GRBF-NN algorithms outperform the classical ones, except for the multilinear over case I waters. A particular improvement Is over case II waters, where the mean square error (MSE) can be reduced by one or two orders of magnitude over the error of multilinear and band-ratio algorithms, respectively.

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.

MOS-B data corrected by standard calibration procedures are affected by "striping" in most channels. Striping arises because of the imperfect calibration of the detector characteristics. The authors present a method for removing striping, in which the equalization curves are estimated by exploiting a data base of carefully selected homogeneous targets, they present the results obtained on sea target images. Such a case study is of great interest because it is related to ocean color applications, for which the MOS-B sensor was especially designed. They also estimate the reduction of striping noise in the equalized image by means of two appropriate indexes of quality.

A three-antenna synthetic aperture radar interferometer (InSAR) with a statistically optimal data processor for three-dimensional (3D) terrain mapping has been proposed recently to reduce the phase ambiguity and data-noise drawbacks of the conventional two-antenna SAR interferometry technique. In this paper, a numerical simulator is developed to assess the achievable performance and various design tradeoffs of the three-antenna InSAR. The most critical conditions for the new reduced-ambiguity system operating on realistic scenes are taken into account. The phase-unwrapping procedure is included in the simulator to compare the new and the conventional technique in terms of both phase and height-estimation accuracy. The performance achievable by a three-antenna airborne InSAR system on a given site are analyzed, and the parameter optimization of the new system is investigated. The results of several case studies show that the new technique can outperform the conventional one significantly for a typical airborne configuration, especially for high-terrain steepness. It provides reduced-phase aliasing and better estimation accuracy. So, the phase unwrapping Is simplified and high-quality maps of terrain height can be obtained. As a limit, absolute phase retrieval can be achieved with good accuracy and the unwrapping procedure can be avoided.

The authors describe the advantages of the hierarchical implementation of the Hough transform with respect to the labelled one. They demonstrate that the hierarchical Hough is faster than the labelled Hough if simple conditions are satisfied. They then discuss the automatic construction of the hierarchical models for the objects belonging to a completely known set. These models can be seen as oriented graphs whose nodes represent local features or patterns which are subparts of the object silhouette. Some experimental results are presented.>