The spatial resolution of TROPOMI, the sensor mounted on board of the satellite Sentinel-5P to monitor air quality, is much higher than its predecessors. Yet, the high variability of pollutants limits the use of the resulting maps in practical applications. Super-resolution approaches can improve the precision of estimates, but their use is heavily reliant on the ability to precisely tune the parameters of the algorithms. For this reason, the employment of a specific image acquisition model is essential for both learning-based and traditional methods. This contribution leverages real full-scale images for validation of a recently proposed model for the degradation introduced by the TROPOMI instrument, which is applied to both classical and learning-based techniques. The model's validity can be evaluated by analysing the quantitative data and visually inspecting the images that have been generated. This contribution proves that the degradation model is an essential basis for the development of novel approaches as well as for the application of all already available techniques.

Sentinel-5P is a valuable resource for academics and policymakers. The ability of the satellite’s equipment to span the electromagnetic spectrum from ultraviolet (UV) to short-wave infrared (SWIR) frequencies is vital in determining the distribution of important gaseous pollutants on a global scale, a significant turning point for air quality monitoring. In technical terms, Sentinel-5P provides an excellent balance between spatial and spectral resolutions; however, physical limitations keep hindering the quality of its products. S5Net is the first deep-learning-based (DL-based) approach designed to super-resolve Sentinel-5P radiance images. Despite its simplicity, this neural network has showed excellent performance when applied to monochromatic images, particularly when compared to more complex deep neural networks. Yet, this groundbreaking study has a significant limitation: the computational inefficiency of the fine-tuning employed, which must be adequately extended to numerous channels. We hence propose a novel dynamic multidirectional cascade fine-tuning procedure, whose routine is fully governed by the correlation between consecutive spectral channels. Our study is accordingly successful in striking a remarkable balance between spectral coherence and spatial resolution improvement, as well as substantially optimizing computing efficiency. The code is available athttps://github.com/alcarbone/S5P_SISR_Toolbox.

Sentinel-5P provides excellent spatial information, but its resolution is insufficient to characterise the complex distribution of air contaminants within limited areas. As physical constraints prevent significant advances beyond its nominal resolution, employing processing techniques like single-image super-resolution can notably contribute to both research and air quality monitoring applications. This study presents the very first use of such methodologies on Sentinel-5P data. We demonstrate that superior results may be obtained if the degrading filter used to simulate pairs of low- and high-resolution images is tailored to the acquisition technology at hand, an issue frequently ignored in the scientific literature on the subject. Because of this, as well as the fact that these data have never been deployed in any previous studies, most of the work theoretical contribution is the estimation of the degradation model of TROPOMI, the sensor mounted on Sentinel-5P. Leveraging this model—which is essential for applications involving super-resolution—we additionally improve a well-known deconvolution-based strategy and present a brand-new neural network that outperforms both traditional super-resolution techniques and well-established neural networks in the field. The findings of this study, that are supported by experimental tests on real Sentinel-5P radiance images, using both full-scale and reduced-scale protocols, offer a baseline for enhancing algorithms that are driven by the understanding of the imaging model and provide an efficient way of evaluating innovative approaches on all the available images. The code is available at https://github.com/alcarbone/S5P_SISR_Toolbox.

Model-based approaches to pansharpening still constitute a class of widely employed methods, thanks to their straightforward applicability to many problems, dispensing the user from time-consuming training phases. The injection scheme based on an accurate estimation (exploiting regression) of the relationship between the details contained in the PAN image and those required for the enhancement of the MS image represents the most updated approach to this problem, being characterized by both theoretical and practical optimality. We elaborated on this scheme by designing a procedure for estimating the key parameters required for the optimal setting of such regression-based approach. We tested this approach on several datasets acquired by the WorldView satellites comparing the proposed approach with a benchmark consisting of some state-of-the-art pansharpening methods.

The use of hyperspectral (HS) data is growing over the years, thanks to the very high spectral resolution. However, HS data are still characterized by a spatial resolution that is too low for several applications, thus motivating the design of fusion techniques aimed to sharpen HS images with high spatial resolution data. To reach a significant resolution enhancement, high-resolution images should be acquired by different satellite platforms. In this article, we highlight the pros and cons of employing real multiplatform data, using the EO-1 satellite as an exemplary case. The spatial resolution of the HS data collected by the Hyperion sensor is improved by exploiting both the ALI panchromatic image collected from the same platform and acquisitions from the WorldView-3 and the QuickBird satellites. Furthermore, we tackle the problem of assessing the final quality of the fused product at the nominal resolution, which presents further difficulties in this general environment. Useful indications for the design of an effective sharpening method in this case are finally outlined.

Pansharpening techniques allow a detailed reproduction of the Earth surface by fusing a multispectral (MS) and a panchromatic (PAN) image acquired over the same area. Classical pansharpening methods consist in the extraction of the details from the PAN image and their subsequent injection into the MS image through a linear function. In this letter, we propose to apply a nonlinear injection procedure that implements the detail injection through a polynomial function. Optimal polynomial coefficients in the least squares sense can be easily obtained in the closed form, and the consequent pansharpening algorithm is shown to obtain superior performance with respect to the existing linear approaches, especially for MS bands with a reduced wavelength overlap with the PAN channel.

Image fusion is growing interest in recent years, thanks to the huge amount of data acquired everyday by sensors on board of satellite platforms. The enhancement of the spatial resolution of a multispectral (MS) image through the use of a panchromatic (PAN) image, usually called pansharpening, is getting more and more relevant. In this work, we focus on the problem of the estimation of the injection coefficients that rule the enhancement of the spatial resolution of the MS image by properly adding the PAN details. In particular, a statistical analysis of the residuals coming from the linear multivariate regression between details extracted from the PAN image and the MS image is performed. A novel hybrid model is introduced for accurately describing the statistical distribution of these residuals, together with a procedure for efficiently estimating both the parameters of the residual distribution and the injection coefficients. The improvements achieved by the proposed approach are assessed using two very high resolution datasets acquired by the WorldView-3 and Worldview-4 satellites. The benefits of the proposed approach are particularly clear when vegetated areas are involved in the fusion process.

The combination of a multispectral (MS) image and a panchromatic (PAN) image, the so-called pansharpening, allows to produce very appealing images that are useful both for visual interpretation and for feature extraction. The state-of-the-art multiresolution analysis pansharpening algorithms are based on the extraction of spatial details from the PAN image through image filters matched with the MS sensors' modulation transfer function. However, this knowledge is often poor due to measurement inaccuracies and/or its aging. Thus, deconvolution algorithms have been proposed to overcome this limitation. In this paper, we propose a multiband filter estimation (FE) approach to improve the solutions in the literature. The main idea in this paper is to exploit a preliminary pansharpened image to estimate the spatial filter used for detail extraction associated with each spectral band. We demonstrate that the proposed method outperforms the state-of-the-art FE approaches by employing data sets acquired by the IKONOS, the Quickbird, and the WorldView-3 sensors.

Pansharpening regards the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The estimation, at reduced resolution, of injection coefficients through regression is a widespread and powerful approach. In this work, the problem of the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is studied. Multiple approaches (based on guess images or an iterative method) are proposed. These are assessed at reduced resolution by exploiting a real dataset acquired by the IKONOS sensor. The quantitative results clearly demonstrate the superiority of the proposed iterative method.

Pansharpening usually refers to the fusion of a high spatial resolution panchromatic (PAN) image with a higher spectral resolution but coarser spatial resolution multispectral (MS) image. Owing to the wide applicability of related products, the literature has been populated by many papers proposing several approaches and studies about this issue. Many solutions require a preliminary spectral matching phase wherein the PAN image is matched with the MS bands. In this paper, we propose and properly justify a new approach for performing this step, demonstrating that it yields state-of-the-art performance. The comparison with existing spectral matching procedures is performed by employing four data sets, concerning different kinds of landscapes, acquired by the Pléiades, WorldView-2, and GeoEye-1 sensors.

Pansharpening regards the fusion of a high-spatial resolution panchromatic image with a low-spatial resolution multispectral image. One of the most debated topics about pansharpening is related to the quality assessment of fused products. Two main assessment procedures are usually exploited in the literature: the reduced resolution validation and the full-resolution (FR) validation. The former has the advantage to be accurate, but the hypothesis of invariance among scales has to be assumed. The latter overcomes this limitation but paying it with a lower accuracy. In this paper, we will focus on the FR assessment proposing an approach for estimating an overall quality index at FR by using multiscale FR measurements. The problem is recast into the sequential Bayesian framework exploiting a Kalman filter to find its solution. The proposed procedure for quality evaluation has been tested on four real data sets acquired by the Pléiades, the GeoEye-1, the WorldView-3, and the WorldView-4 sensors assessing the quality of 19 pansharpened methods. The proposed approach has demonstrated its superiority with respect to the benchmark consisting of state-of-the-art quality assessment procedures.

Pansharpening is usually related to the fusion of a high spatial resolution but low spectral resolution (panchromatic) image with a high spectral resolution but low spatial resolution (multispectral) image. The calculation of injection coefficients through regression is a very popular and powerful approach. These coefficients are usually estimated at reduced resolution. In this paper, the estimation of the injection coefficients at full resolution for regression-based pansharpening approaches is proposed. To this aim, an iterative algorithm is proposed and studied. Its convergence, whatever the initial guess, is demonstrated in all the practical cases and the reached asymptotic value is analytically calculated. The performance is assessed both at reduced resolution and at full resolution on four data sets acquired by the IKONOS sensor and the WorldView-3 sensor. The proposed full scale approach always shows the best performance with respect to the benchmark consisting of state-of-the-art pansharpening methods.

Inpainting in hyperspectral imagery is a challenging research area and several methods have been recently developed to deal with this kind of data. In this paper we address missing data restoration via a convex optimization technique with regularization term based on Collaborative Total Variation (CTV). In particular we evaluate the effectiveness of several instances of CTV in conjunction with different dimensionality reduction algorithms.

Variational methods are widely used in image processing for problems ranging from denoising to data fusion. In this paper we focus on a recent regularization method, called Collaborative Total Variation, applied to the hyperspectral pansharpening, which deals with the fusion of low resolution hyperspectral and high resolution panchromatic images. The effectiveness of this novel approach is evaluated for different Collaborative Norms and the assessment is performed on the Pavia University dataset.

Classical pansharpening algorithms constitute a class of image fusion methods that have been widely investigated in the literature. They have been developed for combining a single- and a multichannel image (panchromatic (PAN) and multispectral (MS), respectively), but can be adapted to the sharpening of hyperspectral (HS) data, both through companion PAN and MS images. We focus in this letter on the HS/MS fusion, showing that the assignation of the MS channel to each HS band is a key step, and investigate several alternatives to make this choice. The assignment algorithms are tested in conjunction with both component substitution and multiresolution analysis pansharpening methods and assessed on images acquired by the Hyperion and ALI sensors. The numerical evaluation shows that the best results can be obtained by optimizing the spectral angle mapper metric confirming that classical methods represent a reliable basis for the development of novel sharpening algorithms.

Pansharpened images are widely used synthetic representations of the Earth surface characterized by both a high spatial resolution and a high spectral diversity. They are usually generated by extracting spatial details from a high-resolution PANchromatic image and by injecting them into a low spatial resolution multispectral image. The details injection is performed through injection coefficients, whose values can be either uniform for the whole image (global methods) or spatially variant (context-adaptive (CA) approaches). In this paper, we propose a CA approach in which the injection coefficients are estimated over image segments achieved through a binary partition tree segmentation algorithm. The approach is applied to two credited pansharpening algorithms based on the Gram-Schmidt orthogonalization procedure and the generalized Laplacian pyramid technique. The performance assessment is performed using two different data sets acquired by the QuickBird and the WorldView-3 satellites. The validation procedure, both at full and at reduced resolution, shows the suitability of the proposed approach, which reaches a good tradeoff between accuracy and computational burden.

Thermal Sharpening (TS) is usually referred to techniques widely used in several Earth Observation applications in order to increase the spatial resolution of thermal images. Profiting from the particular design of the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor mounted on board of the Suomi National Polar-orbiting Partnership (NPP) satellite, we propose here a new approach for obtaining synthetic thermal data with increased spatial resolution and spectral diversity. The method exploits classical Pansharpening algorithms, which are very popular in the field of Visible and Near-InfraRed (VNIR) image fusion, for combining the VIIRS thermal bands with partially overlapping spectral responses. We evaluate the effectiveness of several algorithms by performing a Reduced Resolution (RR) assessment on VIIRS real data, showing the importance of an adequate knowledge of the sensor characteristics.

Accurate representations of the Earth surface in both spatial and spectral domains are highly desirable in many applications using remotely sensed data. An effective solution is achieved by combining hyperspectral data, which are characterized by a high spectral diversity, with high spatial resolution images, collected by multispectral or panchromatic sensors. In this work, we compare the outcomes provided by fusing single-platform or multi-platform data. We demonstrate that the optimal choice depends on the target spatial resolution to be achieved. To this aim, real images collected by the Hyperion sensor are combined with data acquired by the ALI sensor or the QuickBird sensor assessing the fused outcomes at reduced resolution.

Nonlinear decomposition schemes constitute an alternative to classical approaches for facing the problem of data fusion. In this paper, we discuss the application of this methodology to a popular remote sensing application called pansharpening, which consists in the fusion of a low resolution multispectral image and a high-resolution panchromatic image. We design a complete pansharpening scheme based on the use of morphological half gradient operators and demonstrate the suitability of this algorithm through the comparison with the state-of-the-art approaches. Four data sets acquired by the Pleiades, Worldview-2, Ikonos, and Geoeye-1 satellites are employed for the performance assessment, testifying the effectiveness of the proposed approach in producing top-class images with a setting independent of the specific sensor.

Obtaining radiometric surface temperature information with both high acquisition rate and high spatial resolution is still not possible through a single sensor. However, in several earth observation applications, the fusion of data acquired by different sensors is a viable solution for so called image sharpening. A related issue is the presence of clouds, which may impair the performance of the data fusion algorithms. In this paper we propose a robustified setup for the sharpening of thermal images in a non real-time scenario, capable to deal with missing thermal data due to cloudy pixels, and robust with respect to cloud mask misclassifications. The effectiveness of the presented technique is assessed via numerical simulations based on SEVIRI data.

Pansharpening algorithms enable to produce synthetic data with high spatial details and spectral diversity by combining a panchromatic image with multispectral or hyperspectral data. In classical approaches the details extracted from the panchromatic image are introduced into the original multichannel image through injection gains, which can be spatially variant on the image. In this paper we analyze several methods for partitioning an image into regions in which the pixels will share the same injection coefficients. Gram-Schmidt pansharpening methods are used as paradigmatic examples for assessing the performance of global and local gain estimation strategies, using hyperspectral data acquired by sensors mounted on one (Earth Observing-1) or multiple (PROBA and Quick-bird) satellite platforms.

Pansharpening consists of fusing a multispectral (MS) image together with a panchromatic (PAN) image with the aim of jointly preserving the spectral diversity of the former and the geometric richness of the latter. A crucial step in pansharpening algorithms is the detail extraction. This problem is usually addressed by the means of 2D Gaussian filters matched with the MS sensor's modulation transfer function (MTF). Nevertheless, several issues can affect this characterization (e.g. the MTF's gains at the Nyquist frequency could be not available or unreliable). Thus, in this paper we propose a technique based on blind image deblurring in order to estimate band-dependent spatial detail extraction filters by taking into consideration the possible variability of the MS spatial features along bands. The validation is carried out exploiting two real datasets acquired by the IKONOS and the QuickBird sensors.

The application of sparse representation (SR) theory to the fusion of multispectral (MS) and panchromatic images is giving a large impulse to this topic, which is recast as a signal reconstruction problem from a reduced number of measurements. This letter presents an effective implementation of this technique, in which the application of SR is limited to the estimation of missing details that are injected in the available MS image to enhance its spatial features. We propose an algorithm exploiting the details self-similarity through the scales and compare it with classical and recent pansharpening methods, both at reduced and full resolution. Two different data sets, acquired by the WorldView-2 and IKONOS sensors, are employed for validation, achieving remarkable results in terms of spectral and spatial quality of the fused product.

Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community.

Many powerful pansharpening approaches exploit the functional relation between the fusion of PANchromatic (PAN) and MultiSpectral (MS) images. To this purpose, the modulation transfer function of the MS sensor is typically used, being easily approximated as a Gaussian filter whose analytic expression is fully specified by the sensor gain at the Nyquist frequency. However, this characterization is often inadequate in practice. In this paper, we develop an algorithm for estimating the relation between PAN and MS images directly from the available data through an efficient optimization procedure. The effectiveness of the approach is validated both on a reduced scale data set generated by degrading images acquired by the IKONOS sensor and on full-scale data consisting of images collected by the QuickBird sensor. In the first case, the proposed method achieves performances very similar to that of the algorithm that relies upon the full knowledge of the degrading filter. In the second, it is shown to outperform several very credited state-of-the-art approaches for the extraction of the details used in the current literature.

Pansharpening is a successful application of data fusion to remotely sensed data. It aims at obtaining a detailed representation of an Earth's zone both in terms of spatial and spectral resolution. This is done through the fusion of a panchromatic and a multispectral image (having complementary spatial and spectral resolutions) that are acquired simultaneously by several optical satellites. The result of the fusion is commonly achieved by introducing the spatial details, modulated opportunely by gains, in the multispectral one. The injection gains can be estimated globally over the image, or locally, thus obtaining spatially variant values. The latter approach has been proven to achieve better results and it is based on windowing the analyzed image in squared blocks. In this paper we propose a more elaborated concept of locality, as it is based on an opportune segmentation of the target scene. In greater details, we propose to estimate the local injection gains on regions composed of pixel with similar spectral characteristic, as defined by a segmentation. Such local approach is compared to the global one and to the conventional local estimation based on overlapping and non-overlapping blocks. The performances have been assessed by using three real datasets, the first acquired by WorldView-2 and the other two by Pléiades. The analysis evidences the appreciable improvements of the performances with respect to classical schemes.

In several human activities, such as agriculture and forest management, the monitoring of radiometric surface temperature is key. In particular both high spatial resolution and high acquisition rate are desirable but, due to the hardware limitations, these two characteristics are not met by the same sensor. The fusion of remotely sensed data acquired by sensors with different spatial and temporal resolution is a profitable choice to face this issue. When the real-time requirement is relaxed, the data sequence can be processed as a whole, allowing to improve the final result. Within this framework, we propose a novel batch sharpening strategy, relying on interpolation, data fusion and Bayesian smoothing techniques, and we assess its effectiveness on SEVIRI and MODIS thermal data.

The design of a pansharpening algorithm for enriching a MultiSpectral image with the spatial details of a Panchromatic image should preserve the characteristics of the original dataset. A widely employed quality check consists in verifying the consistency of the fused product, namely the similarity of the original image and a reduced resolution version of the sharpened product. We propose to improve this feature by applying an Iterative Back-Projection algorithm after the fusion procedure. The approach is validated on two datasets, acquired by the Ikonos and WorldView-2 sensors, showing remarkable improvements, especially in conjunction with Component Substitution pansharpening methods.

In this paper state-of-the-art and advanced methods for multispectral pansharpening are reviewed and evaluated on two very high resolution datasets acquired by IKONOS-2 (four bands) and WorldView-2 (eight bands). The experimental analysis allows us to highlight the performances of the two main pansharpening approaches (i.e. component substitution and multiresolution analysis).

Images with high spatial and spectral resolutions are desirable for remote sensing applications. Unfortunately, due to sensor physical constraints, this result cannot be obtained by a single sensor. To overcome these limitations, a great number of data fusion approaches have been developed in the last years. The fusion of panchromatic and multispectral images, also known as Pansharpening, is capturing a lot of attention in the literature. In this paper, we extend and analyze the use of some classical pansharpening techniques, belonging to the MultiResolution Analysis and Component Substitution families, for fusing hyperspectral data instead of multispectral ones. The experimental results, conducted on two real datasets acquired by the Hyperion/ALI and CHRIS-Proba/QuickBird sensors, point out the greater suitability of the algorithms into the MRA class thanks to a better spectral consistency of the final products, which is a desirable feature when the number of bands to fuse increases.

The pansharpening process has the purpose of building a high-resolution multispectral image by fusing low spatial resolution multispectral and high-resolution panchromatic observations. A very credited method to pursue this goal relies upon the injection of details extracted from the panchromatic image into an upsampled version of the low-resolution multispectral image. In this letter, we compare two different injection methodologies and motivate the superiority of contrast-based methods both by physical consideration and by numerical tests carried out on remotely sensed data acquired by IKONOS and Quickbird sensors.

A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms.

State-of-charge and parameters online estimation is one of the key features of battery management systems for hybrid-electric vehicles applications. Using model-based approaches, simultaneous sequential Bayesian estimation of battery state and parameters has been shown to be a very powerful tool for the tracking, even in the presence of non-perfectly known models. Monte Carlo implementations are very suited to strongly nonlinear and unreliable dynamics, such those of batteries. In this framework, current paper proposes the use of a Rao-Blackwellized Particle Filter (RBPF) for the joint estimation of battery state and parameters. The results are compared with the existing approaches, highlighting the appealing features of RBPF, both in terms of performances and robustness.

The continuous time monitoring of the radiometric surface temperature by means of high spatial resolution images is desirable in agricoltural applications, such as irrigation management. Since the requirement of high spatial and temporal resolutions can hardly be met by a single sensor, we resort to a fusion strategy of data from multiple sensors. Specifically we consider the Interacting Sequential Bayesian Estimation strategy, as it is able to deal with the sudden changes observed in the temperature dynamics. The method has been validated on SEVIRI TIR real data, properly spatially degraded in order to mimic sensors with different characteristics.

The battery State-Of-Charge (SOC) and parameters estimation is one of the crucial points to be addressed in the development of innovative electric/hybrid electric vehicles. Extended Kalman Filter (EKF) and Particle Filters (PF) are two possible approaches to the problem. While EKF is attractive for its computational efficiency, it may not be accurate for the non-linearity and for the uncertainties involved in the battery modelling. PF is a promising alternative, even if it is computationally more demanding. In this paper, we compare the EKF and PF performance in the dual Bayesian estimation of battery state and parameters, with particular reference to lithium batteries, showing that PF is attractive, especially in the presence of inaccurate battery models.

The design of multitarget tracking procedures includes, as the most time consuming steps, the definition of the objective class and the formulation of the detection criteria. In this paper we investigate a solution toward an intuitive way for implementing a detector for any ad-hoc application. We capitalize on the OSPA metric to discriminate between the semantic object class of interest and other look-alike classes starting from a short number of unlabeled markers. We propose an illustrative algorithm with a toy example, then we apply it to two real images, the first acquired by SEVIRI, the second by MERIS. In the first case we discriminate between lakes, sea and look-alike clouds, in the other between ground and sea ice. We show how semantic classes with very similar spectral properties can be separated even in the presence of uncertainties or errors in the ground truth.

Pansharpening algorithms aim to enhance low resolution multi-spectral images by means of high resolution panchromatic ones. Several approaches are based on the MultiResolution Analysis (MRA) achieved through the pyramidal decomposition of images. We focus here on the implementation based on Morphological Filters (MF) that are optimized through Genetic Algorithms (GA). The effectiveness of this algorithm is compared with other techniques, among which those based on Wavelet operators, through several quality indices on two different real scenarios.

Temporal correlation has been recently taken into consideration to improve the performances of cloud detection algorithms. We exploit this concept within the Maximum A Posteriori Markov Random Field MAP-MRF framework by adding a penalization term which is determined according to the hystory of cloud masses. Multi Target Tracking of clouds is accomplished by methods of Finite Set Statistics (FISS) and several particle-based implementations are compared among them and with other previous methods both on simulated and real data.

Location based services, as well as security issues, require the knowledge of the user's position within an indoor area. Here, we address the problem of how the location service based on a WLAN can profit of the Received Signal Strengths (RSSs) collected from those Access Points (APs) whose position is not available. For this task, we develop two different algorithms, both relying on a dual estimation approach, in which RSS measures from `unknown' APs are used along with RSS from `known' APs. Simulative and experimental analyses, both corroborating the effectiveness of our solutions, are carried out.

Wireless location of a User Equipment (UE) has received growing attention in recent years. The first step for the design of a wireless location system consists in choosing the system architecture and the localization algorithm that match the requirements of the working scenario. In this paper the area of interest is represented by an indoor parking lot, in which the variable occupancy of motor vehicles alters the electromagnetic propagation and causes large errors in vehicle location estimation. The proposed strategy to deal with this problem is the use of a server-based architecture, that ensures security and scalability and accounts for the system state in terms of number and positions of already present vehicles. This concept of state is shown to be useful to design suitable algorithms, based on simplified electromagnetic models, to improve the localization performance.

A successful mathematical description of natural landscapes relies upon a class of random processes known as fractional Brownian motions (fBms), which may exhibit correlation with long-range dependence (LRD). In remote sensing applications, the sensor observes a certain real sceneBand records dataIfor successive signal processing tasks. Assuming thatBis modeled as an fBm, does the recorded signalIpreserve the LRD character ofB? More in general, can we relate the Hurst coefficient (an index of LRD) of the real scene to that of the recorded data? We address the problem in a simplified setup in which the data are related to (the slope of) the original scene through a zero-memory mapping. A mathematical framework is presented in which the above questions can be answered in the asymptotic regime of infinite data size. The effect of the finite sample size is also investigated. The mathematical model is also validated by real data, which are collected by a synthetic aperture radar that is mounted onboard of ERS-1/2 satellites.

Coherence is the key factor in Synthetic Aperture Radar Interferometry. We study the baseline decorrelation due to antenna spatial diversity in order to take into account the effect of the surface statistic roughness in a more general case. As a model of surface roughness we use the fractional Brownian motion

A self-similar natural scene, characterized by a Hurst coefficient HX>1/2, is remotely sensed. The physical process of data acquisition is modeled as a non-linear zero-memory function of the natural scene slope. Do the data collected by the sensor preserve the long-range-dependence feature? If they do, what about the relationship between the Hurst coefficient of the illuminated scene and that of the collected data? This is our first investigation of the issue; as such, we resort to an extremely simplified setup allowing us to highlight some interesting problem features

Self-similar traffic models have permitted a more realistic description of the network devices behavior. However, the derivation of analytical results turns out to be a very demanding task, also in the single-server case. For the work-conserving switching architectures the characterization of the quality of service (QoS) parameters is even more complicated due to the correlation among the queues, induced by the scheduling policies. In this paper we present a detailed study, based on simulations of some paradigmatic scheduling disciplines, performed with an aim to furnish some useful tools for the design of high-speed network devices.