Our work investigates the potential of high-resolution hyperspectral satellite data for detecting atmospheric methane concentrations. We employ the matched filter with Albedo correction and reweiGhted L1 sparsity Code (MAG1C) algorithm, which integrates a sparsity prior, a matched filter, and albedo correction techniques. For the analysis, we utilize hyperspectral data from the PRISMA mission, leveraging its high spatial resolution to potentially enable more accurate localization of point emission sources. Comparing the methane column estimation resulting from our work with corresponding products provided by both the Sentinel-5P and GHGsat missions, a good agreement was found. In particular, a bias of 5 ppb with respect to the methane abundance estimated from GHGsat was reached.

Land surface temperature (LST) is one of the most important variables for the terrestrial ecosystem. [1] It stands as a fundamental Essential Climate Variable (ECV) [2] and hold paramount significance across different environmental and agricultural domains [3].Temperature estimation from satellites is increasingly widespread, which allows to obtain large-scale and almost real-time informations.However, estimating temperature using satellite data has some limitations: presence of cloud cover has an impact on remotely sensed observations [4].This paper presents a data fusion approach for enhancing Sentinel-3 LST products by replacing cloudy pixels with data from MODIS, GCOM-C and ERA5-Land. The model has been tested on a trial study area, but its versatility allows it to be applied worldwide.The resulting fused LST product is subjected to an evaluation against LANDSAT 8 and 9 LST data. The performance of the data fusion demonstrates the efficacy of the proposed fusion method, with Pearson correlation values ranging from 0.60 to 0.93.The study not only contributes to advancements in LST data quality but also establishes a benchmark for future research in satellite data fusion.

Remote Sensing (RS) is applied for a variety of purposes, thanks to the large availability of heterogeneous data. Furthermore, the growing number of CubeSat missions is encouraging increasingly advanced, flexible, and configurable RS missions, that also include the use of Artificial Intelligence (AI) on-board. Indeed, specific hardware allows advanced processing on-board the satellites even if the computational capability is not the same as on the ground. In the context of on-board processing, the compression of acquired images is crucial because permits to save bandwidth for data transmission. We propose an AI-based lossy image compression algorithm for multispectral images that can be executed on-board a CubeSat. The algorithm is based on a Convolutional AutoEncoder (CAE) Neural Network (NN). In lossy compression part of the information stored in the original image is lost. Therefore, the results evaluation includes the assessment of the usability of the decompressed images for common applications.

This study explores the synergy of electromagnetic data modeling and Artificial Intelligence (AI) algorithms for soil moisture retrieval over agricultural fields using Polarimetric SAR (PolSAR) data. SAR acquisitions are considered as a valuable source for accurate estimation of soil moisture in agricultural areas. However, its retrievals are influenced by various factors, including vegetation cover. In this context, the polarimetric information of SAR acquisitions allows the interpretation of the occurring scattering processes. The proposed approach involves a physics-aware AI algorithm based on two Artificial Neural Networks (ANNs), trained on electromagnetic (EM) model simulations at L-band. Starting from a full polarimetric Covariance Matrix, the first AI model separates the different scattering contributions, estimating surface and double-bounce scattering mechanisms while minimizing the attenuation effects caused by the vegetation layer. Then, the estimated surface and double-bounce components are fed to an additional AI model to retrieve soil moisture. Field campaign data over actual corn fields were considered and ingested by the EM model to generate synthetic SAOCOM-like case studies which were used to validate the approach within the simulated domain.

In this work a standard approach for the collection of multi-angular reflectance measurements by means of UAS (Unmanned Aerial System) is presented, as well as the modelling of the reflectance anisotropy through the Ross-Li-Maignan BRDF (Bidirectional Reflectance Distribution Function) model. The analysis is performed over three different types of surfaces, in particular a wheat field, an asphalted area and a corn field, and the measurements are acquired by means of MAIA multispectral camera on board UAS, which is characterized by the same spectral bands of Copernicus Sentinel-2 MSI (MultiSpectral Instrument). The results are promising being the relative RMSE between modelled and measured reflectances below 10% for all the test sites.

The presented methodology aims to create a daily Aerosol Optical Depth (AOD) fusion product by integrating observations and forecasts from various EO data sources. The data used for this purpose are from the Ocean and Land Colour Instrument (OLCI) and Sea and Land Surface Temperature Radiometer (SLSTR) sensors on Sentinel−3, the Second Generation Global Imager (SGLI) on the Global Change Observation Mission−Climate (GCOM−C), and the forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). Leveraging the strengths of each dataset, the developed algorithm adapts to different formats and resolutions, providing a unified and higher−resolution AOD dataset.The Sentinel−3 AOD product ensures high resolution (4.5 km), the GCOM−C product ensures dataset accuracy, and CAMS forecasts offer predictive insights. The algorithm employs a mathematical averaging technique for coincident pixels, facilitating precise AOD estimates in overlapping regions, while a mosaic technique seamlessly integrates non−coincident areas.This fused dataset enhances spatiotemporal coverage, contributing to a more in−depth understanding of atmospheric composition variations. Emphasizing the importance of complete AOD data, the methodology is versatile and has been validated against Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products, achieving a Pearson coefficient of 0.87.The product was tested on the Italian Po River Basin area for the year 2021. This region represents an area of great interest for the study of air quality and atmospheric dynamics due to its geographic complexity and the significant impact of anthropogenic activities.The final product is an improvement over the reliable SYN−AOD product from Sentinel−3, providing daily data obtainable in less than an hour through an automated algorithm.

The amount of raw data generated by instruments on board Earth Observation (EO) satellites is quite often more than what can be transmitted to the ground, so new advanced on-board processing procedures are required. Artificial Intelligence (AI) and Deep Learning (DL) can provide advanced information from EO data and thanks to specific hardware platforms these algorithms can be used also in space. We present here the Convolutional AutoEncoder (CAE)-based algorithm developed for on-board lossy image compression of the European Space Agency (ESA) Φ-Sat-2 mission. DL algorithms have already been successfully applied for image compression however performance degradation may occur in the context of a representative on-board environment. Therefore, besides analyzing the results for the local hardware environment, we investigate the performance variation for the on-board setting. Moreover, we introduced an applicative metric for the evaluation of the compression to assess the applicability of the reconstructed images for other tasks.

The InnEO’Space PhD program addresses the growing demand for skilled individuals in Earth Observation (EO) data management. It offers modern and transferable courses to enhance researchers' innovation-oriented skills. The program includes InnEO Startech, focusing on entrepreneurship and leadership, and the Machine Learning for Earth Observation InnEO Summer school, emphasizing open science and technical skills. Small Private Online Courses (SPOCs) provide blended learning through a user-friendly platform. The InnEO PRO section facilitates interaction among PhD students and professionals, offering various resources and self-assessment tests. The program's success has led to collaborations with UNIVERSEH and FabSpace, while AI4AGRI aims to utilize its resources for AI in EO research.

Soil moisture represents a significant guiding factor for agricultural activities, especially for smart irrigation and crop yield estimation. In this context, SAR data is one of the most valuable sources of information for accurate and continuative estimation of soil moisture in agricultural areas. However, SAR-derived soil moisture retrievals are affected by several factors, including the vegetation cover, which is responsible for additional signal attenuation and scattering mechanisms. Concerning the algorithms for soil moisture estimation, Machine Learning (ML) has proved to be a valuable instrument for finding relations between SAR data and the soil dielectric properties. For this purpose, this study aims to synergically adopt electromagnetic data modelling and a ML algorithm to estimate the scattering contributions associated with the ground and demonstrate that they are more sensitive to soil moisture than the total received signal. To apply such approach to real SAR data, airborne acquisitions at L-band will be considered.

This work addresses the Bidirectional Reflectance Distribution Function (BRDF) characterization by means of MAIA multispectral camera onboard an Unmanned Aerial System (UAS). The proposed procedure relies on the design and execution of UAS flight plan for multi-angular acquisitions, which can be automatically repeated over different land cover types. Then, the inversion of the RossThick-LiSparse (Ross-Li) BRDF model is pursued in order to retrieve the fundamental parameters, allowing the complete characterization of the considered surface in terms of reflectance distribution for each band. A key point of this work is the challenge we face related to the development of an optimum strategy for the collection of ground-based dataset for BRDF model inversion.

The growing amount of data currently collected by earth observation satellites requires new processing procedures able to manage huge quantity of information. Among these, data reduction techniques represent a viable solution. In particular, data reduction on-board is significant because allows to save on-board storage space and bandwidth for data transmission to the ground. However, the algorithm used for compression must be able to preserve the key information contained in the acquired data, so that the applicability of the collected information is still guaranteed in the different fields of work. Artificial intelligence, and in particular deep learning, are well suited for this purpose because of their ability to extract valuable information from complex data. This work proposes a lossy image compression procedure based on a Convolutional Autoencoder (CAE) that can be performed on-board the satellite. The images acquired by the sensor can be compressed through the algorithm, stored, and sent to the ground where they are reconstructed, saving space and bandwidth for data transmission. The performance of the compression algorithm will be evaluated in terms of original-reconstructed image similarity and also with regard to the applicability of the reconstructed images to common applicative cases. The algorithm here proposed has been idealized and is currently in development in the context of the European Space Agency's (ESA) PhiSat-2 mission, that aims at demonstrating the advantages of the Artificial Intelligence (AI) on-board for Earth observation applications.

We present a physics-based machine learning (ML) approach for estimating corn plant height from simulated synthetic aperture radar (SAR) data. The proposed study intends to demonstrate the physical awareness of datadriven approaches such as ML. In this regard, a multilayer perceptron (MLP) artificial neural network (ANN), designed for corn plant height estimation, was trained with simulated C- and L-band SAR data generated using a state of the art electromagnetic model for microwave backscattering from terrain covered with vegetation. Here we show how the most significant connections between the nodes composing the network and the most relevant input variables can be detected, demonstrating the physical meaning behind the mapping criteria of the network itself.

In this work the classification of Sentinel-3 Sea and Land Surface Temperature (S3-SLSTR) images with a focus on volcanic cloud detection through a Neural Networks (NNs) approach is presented. Since the hazardous nature of eruptions, a fast and reliable method to monitor the evolution of volcanic clouds in real time is of primary interest. NNs represent a suitable tool for this purpose given their short processing time once trained, and their ability to solve complex problems as those related to natural events. The present research starts from the generation of the training patterns by means of MODerate resolution Imaging Spectroradiometer (MODIS) data collected during the 2010 Eyjafjallajokull (Iceland) eruption, it goes through the training of the NN, and ends with the application of the NN-based model to SLSTR data collected during the 2019 Raikoke (Kuril Island, Russia) eruption.

Different technologies currently applied for monitoring transport infrastructures show promising results in providing responses for their particular field of application, but they are still missing to be assimilated in an integrated solution that would exploit the advantages of each of them. Aiming at filling this gap, here we propose an approach based on the fusion of nondestructive measurements from satellite and insitu surveys, for a full-assessment of transport infrastructures. In particular, we describe the preliminary results obtained by the space segment, including the application of satellite SAR interferometry technique and satellite SAR and multispectral land cover classification over an area west of the city of Salerno, south Italy, that is characterized by severe instability and human pressure. In addiction, this work provides a first formalization of the proposed data-fusion scheme. Identified methods show promising results and will be further developed in the context of Italian research Project of Relevant National Interest (PRIN) already ongoing.

In this paper we describe the experimental set-up of a study aiming at testing the capability of UAV (Unmanned Aerial Vehicle) multispectral imagery for the calibration of the electromagnetic quantities measured by the medium resolution satellite Sentinel-2, launched by European Space Agency. This is made feasible by mounting on the UAV a camera characterized by acquisition bands which are designed in order to mimic those on the satellite. Preliminary analysis over a vegetated area shows encouraging results because the spectral signatures of the two instruments appear quite consistent. Theoretical modelling of BRDF (bidirectional reflectance distribution function) is also considered in order to guide the acquisition plan of the UAV measurements

This work focuses on the development of a fully-automated integration approach, which seeks to combine data from multiple sources with the aim of implementing a dependable soil moisture estimation framework based on Neural Networks (NNs). Several papers have dealt with inverse modeling of soil moisture using NNs, often trained by way of Synthetic Aperture Radar (SAR) data generated through the Integral Equation Model (IEM); our approach is designed to harness the newer IEM calibrated version modified by Baghdadi (IEM_B), integrating synthetic data with real data in order to monitor possible improvements in NNs estimation efficiency. The experiment involves two steps: a first NN is trained with SAR Sentinel-1 data (taken from the Google Earth Engine, GEE, Catalog and granted freely by the European Union, EU, under the Copernicus programme) and in situ soil moisture measurements, taken from the International Soil Moisture Network (ISMN); in the second part, a further NN is trained by enlarging the training dataset with IEM_B generated SAR data. Combination of the two large-scale data sources and IEM_B generated data may lead to an improvement in separating soil/vegetation contributions, possibly improving NN soil moisture estimation accuracy; the study presented may serve as a baseline demonstration for future exploitation of the large-scale data sources for both general and field-specific soil moisture estimation.

Machine learning can be considered as a very important area within artificial intelligence and it is characterized by algorithms and techniques that learn by examples. In the last decade, mainly due to the improvements obtained in the field of high performance computing, such as the enhanced exploitation of cloud technology and of graphics processing units (GPU), machine learning models have gained considerable progress as far as remote sensing and Earth Observation (EO) applications are concerned. However, the need of huge quantities of data necessary for the training phase, may be still a limiting factor especially in problems addressing the quantitative estimation of geo-physical parameters. In this paper, we report about the design and the development of a new platform capable of meeting the requirements of scientists and researchers who are attracted by the use of machine learning but meet difficulties in the generation of reliable data sets. The platforms relies on the implementation of radiative transfer models, plus a bunch of appropriate functionalities, in order that simulated data can be added to those available by ground-truth campaigns.

In recent years many studies concerning the monitoring of volcanic activity have been carried out to develop ever more accurate and refine methods which allow to face the emergencies related to an eruption event. In our work we present different approaches for the volcanic ash cloud detection and retrieval using Sentinel-3 Sea and Land Surface Temperature Radiometer (SLSTR) data. As test case the SLSTR image collected on Raikoke volcano the 22 June 2019 at 00:07 UTC has been considered. A neural network based algorithm able to detect and distinguish volcanic and meteorological clouds, and the underlying surfaces, has been implemented and compared with two consolidated approaches: the RGB (Red-Green-Blue) and the Brightness Temperature Difference procedures. For the ash retrieval parameters (aerosol optical depth, effective radius and ash mass), three different methods have been compared: the reliable and consolidated LUTp(Look Up Table) procedure, the very fast VPR (Volcanic Plume Retrieval) algorithm and a neural network based model.

During a volcanic eruption, large quantities of Sulphur dioxide (SO2) are sometimes emitted into the atmosphere. Rapid detection and tracking ofvolcanic SO2 clouds might be beneficial to air traffic security and to predict any correlated impact on the environment; for example, the possibility of acid rain events. Within the presented work, we exploited Sentinel-5p radiance data (Level 1 b) to detect and retrieve SO2 volcanic emissions through a neural network based algorithmthat produces rapid SO2 vertical column estimates. The dataset used for training the net was composed of 13 TROPOMI Level 2 “Offline” SO2 data collected during the Etna Volcano eruption that occurred in 2018 from 22 December to 1 January. Experimental results are very encouraging and open to the perspective ofmake available a new and stable product for monitoring atmospheric SO2 clouds on a global scale based on Sentinel-5p acquisitions.

Studies of oil slicks in the ocean environment with synthetic aperture radar (SAR) have found that one of the most complex challenges to oil spill detection is the separation of mineral oil spills from slicks that are biogenic in origin. The possible occurrence of multiple scattering mechanisms beyond Bragg scattering for the sea surface, with or without biogenic or mineral oil slicks, and even under low to moderate wind conditions, has also been a subject of debate because the measured signals from these radar-dark surfaces can be contaminated easily by noise. Therefore, the use of noise-uncontaminated data is required for oil spill study in order to avoid significant alteration in the measured radar backscatter, which can lead to misinterpretation and misclassification of the scattering mechanisms involved. To this end, this study uses uninhabited aerial vehicle SAR data, with a noise-equivalent sigma zero as low as −53 dB, to investigate slick classification within a deep learning framework in order to assess deep architectures’ capabilities for providing a reliable and accurate three-state classifier capable of separating mineral oil films from biogenic slicks and from the clean sea. The study exploits parameters with sensitivity to the dielectric constant and ocean wave damping properties, and convolutional neural networks’ (CNNs’) capability for learning nonlinear features, shapes, and textural and statistical patterns, in order to obtain significant classification accuracy. Very high accuracy results have been achieved, with values up to 0.91, 0.94, 0.98, and 0.99 under the most probable real-world spill acquisition conditions.

In this work we present a new coastline extraction approach, which seeks to enhance performances and to provide automation in shoreline generation with SAR (Synthetic-aperture radar) data. Our approach is designed to harness Multi-Pol SAR acquisitions, while single-pol acquisitions are used in most of the approaches in this area, employing an Autoassociative Neural Network (AANN) for data fusion purposes and a Pulse-Coupled Neural Network (PCNN) for the generation of a final coastline. Using RADARSAT-2 data, main findings are shown, exhibiting better and comparable results with respect to consolidated approaches and with a recent automated method which may be regarded as within the state-of-the art methods in the field of coastline extraction from SAR data.

Synthetic Aperture Radar (SAR) images taken over a certain area at different bands and also with a short time interval are now more widely available. This is due to the increase of SAR acquisitions following the last space missions, such as Sentinel 1 and COSMO-SkyMed (CSK). New paradigms capable of performing effective analysis and synthesis stemming from such a type of information are then required in order to exploit better and disseminate the information contained in the data. In this letter, a data fusion technique between CSK and Sentinel-1 data is described. To this purpose, an ad hoc Nonlinear Principal Component Analysis (NLPCA) with Auto-Associative Neural Networks (AANNs) algorithm is designed and developed. The network extracts the most relevant features from the combination of the different scattering mechanisms. The extracted features are then used as inputs for a land cover classification exercise. A comparison between the results obtained with the original images and those yielded by the new synthesized data, with lower dimensionality, demonstrates the ability of the algorithm to generate useful final products.

In this paper a new approach based on the use of Synthetic Aperture Radar COSMO-SkyMed products to verify urban change detection and to observe new constructions is presented. SLC products information has been exploited, since the proposed procedure combines backscattering coefficient and coherence information as extracted from two interferometric data, acquired in a short time interval. The algorithm exploits the information from backscatter intensity and interferometric coherence. Firstly, the interferometric SAR couple is processed by an unsupervised Neural-Networks, particularly PCNN (Pulse Coupled Neural Network) is applied to create a preliminary changes map based on the difference of backscatter intensity information. Then, accuracy is further improved by the fusion with the coherence information. The achieved results shown as the combination of backscattering and coherence information, extracted from Very High Resolution SAR data, allows to provide very accurate urban change detections with a fast and unsupervised procedure. The latter is particularly suitable to process quickly huge amount of SAR data since its lower computational requirements with respect to e.g. supervised algorithms.

This study aims at analysing local government efficiency using EO data to focus on the construction of new buildings in burned areas. We detected all forests and pastures of Sardinia (Sardinia) that witnessed at least a one-hectare wildfire, for biennium 2005-2006, using Landsat-TM data. We then monitored all the burned areas for a period of at least 10 years following the wildfire, up until 2016, by using Landsat products and VHR imagery provided by the Sardinian Region. The idea was to verify compliance with the Italian Framework Law on forest fires, which prohibits the construction of any structure or infrastructure aimed at civil settlements on woods and pastures affected by a fire for the next ten years. We retrieved as many as 748 burned areas, over which 148 violations were detected. These data have been used to carry out an econometric pilot analysis. Results suggest that, in general, the emergence of violations is inversely related to wildfire dimensions and that, on average, law-breaking occurs within the first five years.

SAR images from Italian COSMO-SkyMed mission can have a significant impact on the production and updates of land cover maps. However, for the full exploitation of the data and their application to nationwide extensions, robust automatic procedures need to be designed. In this paper we present the preliminary results obtained by the implementation of a processing scheme using COSMO-SkyMed images to provide, and regularly update every six months, land cover maps for the whole Italian territory. Most of the automatic processing is based on Neural Networks (NN) algorithms. In particular PCNN (Pulse Coupled NN) have been considered for change detection purposes while Multi-Layer Perceptrons (MLP) have been used for classifying the pixels belonging to a detected changed area.

Spectral unmixing provides information on a sub-pixel level, which is extremely useful for studying the urban areas. Nevertheless, the high spatial diversity of man-made structures, the spectral variability of urban materials and the three-dimensional structure of the cities makes the sub-pixel mapping of urban surfaces one of the most challenging tasks of remote sensing science. In this study, these issues are addressed using an artificial neural network trained with endmember and non-linearly mixed synthetic spectra to inverse the pixel spectral mixture in high resolution multispectral imagery. A spectral library is built, consisting of endmember spectra collected from the images and synthetic spectra, produced using a non-linear model specifically developed for urban scenes. The proposed method is easily transferable to any city and fast in terms of computations, which makes it ideal for implementation with operational services for cities.

The use of COSMO-SkyMed constellation has been tested for post-fire monitoring activities. A typical Mediterranean ecosystem, seriously damaged by a wildfire, has been selected as study area. The multitemporal and multipolarization capabilities of COSMO-SkyMed have been exploited for automatic burnt area detection and monitoring of vegetation regrowth. Different imaging configurations have been tested to define the proper monitoring strategy in Mediterranean areas. Results showed X-band suitability for Mediterranean maquis post-fire monitoring.

Concurrent availability of VHR (Very High Resolution) images at both optical and microwave bands opens new challenges in many applicative scenarios of Earth Observation (EO). In particular this is true for precision farming activities where the retrieval on the metric scale of biophysical parameters and of information regarding vegetation spatial distributions can be very effective in supporting farmers during the production cycles. However, the inversion problem giving the value of the desired variable from the measured electromagnetic quantities (the image data) can be very complex and the nonlinear relationships involved need to be handled by suitable algorithms. In this paper a complete processing scheme providing quantities of interest for precision viticulture from data provided by WorldView-2 (WV2) and COSMOSkyMed (CSK) space platforms is presented. Once the appropriate season time was selected, the satellite data have been acquired over the test area within a limited time window and concurrently with the collection of the groundtruth. The workflow, besides adequate pre-processing steps, includes two neural networks (NN) modules, one is dedicated to the extraction of a restricted number of nonlinear components from the WV2 data, the other one to the actual inversion problem. The obtained results seem to be satisfactory with respect to the requirements provided by the users.

In this paper, we propose a novel Bayesian procedure to update the probability distribution for a set of possible atmospheric states, once ground measures of temperature, pressure, humidity, and tropospheric delay of Global Navigation Satellite System (GNSS) signals are made. It is based on a representative dataset of matching pairs of reanalysis atmospheric states and ground measures. By applying the basic rules of probability theory and logic inference, a computable expression for the conditional probability of the states given the measures is found. This allows us to select the most plausible atmospheric conditions, consistent with ground observations. Compared with more conventional techniques, the proposed approach has the advantage of always giving a result, even if not all the measures are available. Moreover, it provides the probability distributions of the retrieved quantities, which collapse to the corresponding prior distributions in the worst case of no significant measures. In any case, the final uncertainties are fully quantified, as needed for many meteorological applications, including data assimilation and ensemble forecasts for a numerical weather model. In addition to the theoretical details, a practical example of operational application, using a ten-year dataset on a Mediterranean test site, is also presented. The most probable retrieved atmospheric profiles of water vapor and temperature, as well as the corresponding values of precipitable water, are compared with balloon measurements on such a test site, showing good agreement and a significant improvement when the GNSS delay measure is added. In particular, the precipitable water retrieval turns out at least as accurate as that obtained with conventional approaches.

The operational level reached by polarimetric data processing techniques has been demonstrated during the last decade. The next generation of spaceborne Synthetic Aperture Radar satellites will implement full- or dual- polarimetric capabilities. In few years a huge amount of data will have to be processed in a fast and reliable way, implementing polarimetric decompositions or accurate classifications. Two neural network approaches for fast and accurate processing of polarimetric data are presented. In the first approach a neural network based processing chain for fast model based polarimetric decomposition is developed, while in the second approach a Non-Linear Principal Component Analisys of polarimetric data has been performed using an Auto Associative Neural Network. The results show a considerable reduction of computational effort and a substantial data compression with a minimun loss of information.

Synthetic Aperture Radar interferometry (InSAR) is an established technique for retrieving digital elevation models. Last generation of metric resolution SAR systems promoted interest in investigations about InSAR potential for urban observation. It has been shown that accurate InSAR retrieval of the vertical dimension of buildings could be partially impaired by layover. Within a single baseline framework, firstly this paper aims at a further insight into the consequence of layover effect on InSAR observation of buildings, by working a suitable model. Secondly, following the model indications, a processing strategy is proposed to improve height estimation, exploiting the backscattering from highly reflecting structures which are frequently present on the roofs of buildings, at least in some geographic areas like Italy. A preliminary validation of the procedure is carried out using Italian Space Agency (ASI) COSMO-SkyMed Spotlight data acquired on the Tor Vergata University Campus test site in Rome.

Recent X-band SAR missions, such as COSMO-SkyMed (CSK), which is able to provide very high spatial resolution images of an area of interest with a short revisit time, are expected to be quite useful sources of information for monitoring the terrestrial environment and its changes. On the other hand, the huge amount of data involved, as well as the need to promptly act in case of emergency, requires the development of automatic change detection tools. This paper reports on a novel automatic change detection algorithm combining multilayer perceptron neural networks (NNs) and pulse coupled NNs, which has been implemented and tested on pairs of Stripmap and Spotlight CSK images acquired on the Tor Vergata University area in the southeast outskirts of Rome, Italy, where a significant and continuous urbanization process is occurring.

In this Part 1 paper concerning a new Cloud Dynamics and Radiation Database (CDRD) algorithm, improvements in obtaining satellite retrievals of rainfall from multispectral passive microwave (PMW) radiometer measurements are obtained by transforming a conventional Cloud Radiation Database (CRD) algorithm. The improvements arise by combining parameter constraints derived from model-based dynamical-thermodynamical-hydrological (DTH) meteorological profile variables and additional geographical-seasonal (GS) factors, together with multispectral PMW brightness temperatures (TBs), into a specialized knowledge database underpinning a Bayesian retrieval algorithm. The so-called knowledge variables are produced by a high-resolution nonhydrostatic cloud-resolving model (CRM). The associated knowledge TBs are produced by a calibrated PMW radiative-transfer-equation model system (RMS) that relates CRM environments to expected satellite-view top-of-atmosphere TBs. By first applying the RMS to thousands of meteorological-microphysical situations simulated by the CRM and then by marshaling into the specialized database all the concomitant modeled microphysical profiles, TBs, and linked DTH/GS profiles/factors (from which optimal constraint tags can be derived), it becomes possible to use the database for the Bayesian interpretation of analogous measured TBs and tags. The main purpose of the new algorithm is to reduce ambiguity (nonuniqueness) effects that plague predecessor CRD algorithms. Such schemes restrict the interpretation of observed TBs by ignoring observable DTH/GS parameters that help constrain the influence microphysical profile sets (i.e., the associated hydrometeors, their size distributions, and their concomitant vertical distributions) that feed into the retrieval solutions. A Version 1 CDRD algorithm is tested against its CRD predecessor on two case studies of precipitation over Italy's Lazio region which were observed with various satellite PMW radiometers. The measured TBs and corresponding tags obtained from gridded operational global model analyses are used in juxtaposition to produce the final rainfall retrievals. The retrievals are verified against coincident precision polarimetric C-band radar measurements. Skillful improvement is found for a case of intense convective rainfall where even CRD-type algorithm accuracy should be expected, as well as for a case of mixed convective-stratiform rainfall where either algorithms might otherwise be expected to be somewhat inaccurate.

Aim of this paper is to show how fusing SAR images having different characteristics can improve the classification accuracy, in spite of the geometrical problems rising in the fusion operation. To this end, we fused multiple-frequency (Cand X-band), multiple-polarization (HH, HV, VH and VV) and multi-resolution images. The classification has been carried out by a neural network algorithm (NN), in which the backscattering coefficients at each polarization for each image have been fused to form the input to the classifier. The evaluation of the classification accuracy has been performed in terms of overall accuracy, per class accuracy and Kappa coefficient. The obtained results show not only an enhancement of the classification accuracies, but also that more land cover classes can be better identified with respect to a single acquisition.

The novel instruments of the COSMO-SkyMed (CSK) Earth Observation programme, offer an opportunity to explore at various resolutions the information content of X-band signal backscattered with different polarizations. In spite of their potential to render additional information about an area of interest, speckle noise and artifacts make X-band acquisitions difficult to interpret. This is a motivating scenario to explore what (semi-)automatic procedures might be able to offer. This paper is first attempt to process CSK Stripmap PingPong data using two well-known artificial neural network techniques: the supervised backpropagation multilayer perceptron and the unsupervised self-organizing map.

We have analysed the seismic source of the active fault generated Van Mw=7.1 earthquake occurred in Eastern Turkey the 23rdOctober 2011. To this aim the surface displacement field has been measured applying SAR Interferometry (InSAR) technique to the available dataset of coseismic COSMO-SkyMed image pairs. The seismic source model has been obtained by the use of a data inversion procedure based on the concurrent application of InSAR techniques and Neural Networks. The proposed approach elaborates the information on the coseismic deformation pattern stemming from available differential interferograms. The interferogram is the expression of the active fault at depth, thus its shape, size and its features somehow refer to the geometry and slip of the fault generating the seism. A Neural Network has been trained to recognize some fault parameters (Length, Width, Strike, Dip, Depth) from the unwrapped interferogram. The retrieval exercise consists in estimating these parameters from the coseismic interferogram exploiting Neural Networks.

The goal of this work is to investigate on the enhancement, in terms of accuracy and number of classes, of the vegetation mapping through the joint use of multi-sensors data. Several stacks of Spotlight COSMO-SkyMed, acquired both in HH and VV polarization, and Multispectral World-View2 images, taken in the same or different seasons, have been compounded and exploited to identify six types of natural surfaces by means of a Neural Network classifier. While the information content of the eight bands of the multispectral data may be sufficient to discriminate the classes of interest, the single polarization of each SAR image has to be integrated by extracting further features, such as textural parameters. The assessment of the provided vegetation maps has been carried out in terms of per class accuracy, overall accuracy and K coefficient. The achieved results demonstrate the improvement of the classifications obtained by fusing more information from multi-sensors acquisitions.

The new generation of spaceborne instruments, capable of capturing a large amount of very-high resolution images within a short revisit time, is allowing remote sensing researchers and final users to receive huge amounts of data in rather short times. Such a scenario makes it mandatory the development of techniques, as much as possible automatic, for the understanding and the effective exploitation of the available information. This contribution deals with the features extraction from Spotlight Cosmo-SkyMed SAR imagery (1 m spatial resolution) by means Multi Layer Perceptron Neural Network (MLP-NN) algorithms. For a better pixel characterization, textural parameters have been also considered as additional information for the classification procedure.

The basic idea of this paper relies on the concurrent exploitation of the capabilities of neural networks (NNs) and SAR interferometry (InSAR) for the characterization of a seismic source and the estimation of its geometric parameters. When a moderate-to-strong earthquake occurs, we can apply the InSAR technique to compute a differential interferogram. The earthquake is generated by an active seismogenic fault having its own specific geometry. The corresponding differential interferogram contains, in principle, information concerning the geometry of the seismic source that the earthquake comes from. To perform the inversion operation, a novel approach based on NNs is considered. This requires the generation of a statistically significant number of synthetic interferograms necessary for the network training phase. Each of them corresponds to a different combination of fault geometric parameters. After the training, the network is ready to perform, in real time, the inversion on new differential interferograms. This paper illustrates such a methodology and its validation on a set of experimental data.

Feature extraction for the dimensionality reduction of hyperspectral data is performed by means of Auto-Associative Neural Networks. The algorithm performance is compared to the Principal Component Analysis and the Maximum Noise Fraction ones. Results of land cover pixel-based maps yielded by the reduced vector and a dedicated neural network classification algorithm are also reported.

Information mining from heavy SAR images is considered from the point of view of the procedure automatization. Two schemes based on Neural Networks are evaluated, one based on the Self Organizing Map method exploiting polarimetric information and oriented to land cover classification, the other based on the Pulse-Coupled Neural Networks aiming at characterizing the imaged buildings.

The basic idea of this paper relies on the concurrent exploitation of the capabilities of neural networks and SAR interferometry for the characterization of a seismic source and the estimation of its geometric parameters. When a moderate-to-strong earthquake occurs we can apply SAR Interferometry (InSAR) technique to compute a differential interferogram. The earthquake has been generated by an active, seismogenic, fault having its own specific geometry. Therefore each differential interferogram contains in principle information concerning the geometry of the seismic source the earthquake comes from. To perform the inversion operation an approach based on neural networks can be considered. The paper illustrates such a methodology and its assessment on experimental data.

Since a few months TerraSAR-X has been acquiring X-band SAR images of the earth surface from space. This contribution reports on a study carried out to understand the main textural features of the X-band radar return from various kinds of surfaces and in particular to assess the potential of images acquired by X-band space borne radars in mapping fire scars and in classifying suburban/agricultural land cover. To this end, a novel unsupervised neural network algorithm, the Textural Self-Organizing Map (TexSOM), based on the textural features of the radar image, has been worked out and tested on areas in Greece and Italy.

This paper reports the study of supervised neural network algorithm for classification purposes. SPOT 5 and TerraSAR-X dataset are analyzed. Classification results are critically discussed and compared to ground truth map and unsupervised neural classification of the same area. The aim is to demonstrate the capability of neural networks in managing heterogeneous dataset and the accuracy improvement obtained by the use of the textural object based layers fused with the optical and radar data.

In the follow-up of the BACCHUS project, aimed at establishing a reference high quality geographic information system for vineyards, an airborne SAR survey has been carried out in fall 2005 in the Frascati area, near Rome (Italy) to investigate on the potential of radar remote sensing in vineyard monitoring. This contribution reports the joint use of high resolution polarimetric SAR data and QuickBird optical data in order to evaluate the potential of remote sensing in vineyard detection and bio-physical parameters retrieval.

A concurrent exploitation of the capabilities of neural networks and SAR interferometry is considered for the characterization of a seismic source and the estimation of its geometric parameters. This requires the generation of an appropriate number of synthetic interferograms necessary for the network training phase. After the training the network is ready to perform the inversion on new interferograms. The paper illustrates the assessment of such a methodology over a significant set of experimental data.

Synthetic aperture radar signal processing issues related to the exploitation of a pulse-to-pulse encoding transponder using pseudorandom codes discussed analytically. Namely the focusing algorithm, the code synchronization procedure and the properties of the code induced gain against non-encoding point scatterers and distributed ones. A time-domain processing algorithm and a code synchronization procedure are proposed and validated on simulated data and on a European Remote Sensing Satellite-2 data set containing prototypes of such a device. The interaction of the transponder signal with terrain backscattering is analyzed by deriving parameters that are useful for performance assessment. These are related to the relevant parameters in radiometric calibration, interferometric applications, and tagging.

Analysis of L-band polarimetric SAR data has not been extensively carried out for undulating, heterogeneous and fragmented landscapes, where classification can become quite challenging. This paper reports results of a study on the pixel-by- pixel unsupervised classification of very-high resolution polarimetric images by self-organizing neural networks.

In the follow-up of the BACCHUS project, aimed at establishing a reference high quality geographic information system for vineyards, an airborne SAR survey has been carried out in fall 2005 in the Frascati area, near Rome (Italy) to investigate on the potential of radar remote sensing in vineyard monitoring. This contribution reports on the polarimetric very- high resolution C and L-band SAR data acquisition campaign supported by ESA and carried out by the DLR E-SAR on two dates in October 2005. The possible relation between the observed variations of backscattering at different polarizations and the harvest of the grapes is discussed.

The effectiveness of multilayer perceptron (MLP) networks as a tool for the classification of remotely sensed images has been already proven in past years. However, most of the studies consider images characterized by high spatial resolution (around 15–30 m) while a detailed analysis of the performance of this type of classifier on very high resolution images (around 1–2 m) such as those provided by the Quickbird satellite is still lacking. Moreover, the classification problem is normally understood as the classification of a single image while the capabilities of a single network of performing automatic classification and feature extraction over a collection of archived images has not been explored so far. In this paper, besides assessing the performance of MLP for the classification of very high resolution images, we investigate on the generalization capabilities of this type of algorithms with the purpose of using them as a tool for fully automatic classification of collections of satellite images, either at very high or at high-resolution. In particular, applications to urban area monitoring have been addressed.

In continuation of the BACCHUS project, aimed at establishing a reference high quality geographic information system for vineyards, an airborne SAR survey has been carried out in fall 2005 in the Frascati area, near Rome (Italy) to demonstrate the potential of airborne radar remote sensing in vineyard characterization. This contribution reports on the polarimetric L-band and dual polarization C-band SAR data acquisition campaign supported by ESA and carried out on two dates in October 2005 (the first during the grape harvest and the other after the vintage completion).

This paper refers to a study on the pixel-by-pixel unsupervised classification of a polarimetric SAR image of a Central Italy landscape. The polarimetric data have been processed by self-organizing neural networks to test their performance in classifying a complex landscape. The discrimination accuracy attained by the self-organizing map method is compared both against that of H/A/alpha-Wishart unsupervised procedure and of a supervised scheme.

Baseline calibration is a needed step in all applications of SAR interferometry and differential interferometry. A new approach for baseline calibration is proposed, based on the idea of maximizing the correlation between the original complex interferogram and reference values of it obtained from ground control points. The main advantage with respect to traditional techniques is that the method does not require the phase to be unwrapped in advance, and therefore the results are not affected by possible unwrap errors. In addition, successive phase unwrap is facilitated by the better phase flattening possible after baseline calibration. The method is computationally more demanding than traditional techniques, though the requested computational time is comparable with that of other processing steps of SAR interferometry. Tests performed on real ERS SAR images confirm the validity of the proposed approach.

In the recent years InSAR (Synthetic Aperture Radar Interferometry) technique showed its wide potentialities to detect the surface displacement field due to an earthquake. Of great interest and usefulness in this context is the solution of the inverse problem that means to recover the source parameters from the knowledge of InSAR surface displacement field. In this work a novel approach for the solution of such a problem is presented.

Several studies have reported the potentialities of high resolution multi-spectral imagery for classifying and monitoring urban areas [A. K. Shackelford et al. (2003)], [M. Pesaresi et al. (2000)], [G. Schiavon et al. (2003)]. In this paper we present the results obtained by processing high resolution multispectral QuickBird images of an urban area. The high resolution QuickBird data have been used for two different purposes: for an automatic image classification using neural network techniques and for a change detection analysis. In the first case, we have carried out a pixel-based classification procedure aimed at the discrimination among 4 main classes: buildings, roads, vegetated areas, bare soil; then we have examined the potentialities of Kohonen maps for discovering new subclasses within those already established: e.g. for the asphalt category, different subclasses such as highways pixels and the other different types of roads such as secondary street pixels have been identified. In the second case we have processed multitemporal QuickBird images for detecting major changes occurred over the selected test area, like news buildings not visible in the first image.

We report on an investigation aimed at evaluating the performance of a neural-network based crop classification technique, which makes use of multi-polarization and/or multi-temporal back-scattering coefficients measured at C-band.

This paper reports on the results of the application of SAR interferometry to the study of a landslide disaster which happened in the Sarno area (Italy) in 1998. A DEM generated by means of an ERS-1/2 tandem pair has been compared with a topographic one allowing the identification of the areas mostly affected by the landslide.

The potentiality of high resolution multi-spectral imagery of QuickBird satellite for monitoring urban areas and the sinergy with multitemporal SAR data is presented in this paper. The high resolution imagery can be applied for two purposes: for the analysis of the different spectral behaviour of several types of surface present in the area under observation and as ground-truth for the validation of procedures using only ERS-SAR data as input. In the first case, pixel-based classification procedures can be designed, trying to discriminate among various urban classes, including buildings, roads and vegetated areas. The decision-making process is performed by a neural network algorithm. The synergy with ERS-SAR involves the often not trivial co-registration process. Once this is completed, we can use multitemporal radar imagery for detecting major changes in the urban manufactures like news buildings and structures not visible in the older images, and validate the results with the help of the high resolution imagery. In this paper we report on some results obtained by applying the high resolution imagery of QuickBird for the analysis of the Tor Vergata University campus urban test area, located in Italy, South-East of Rome, whose extension is of about 600 ha. The results are divided in two sections. In the first one only the use of Quickbird data is considered, in the second one we report on the benefic effects carried on by the synergy with SAR imagery.

This paper reports on an investigation aimed at evaluating the performance of a neural-network based crop classification technique, which makes use of backscattering coefficients measured in different C-band synthetic aperture radar (SAR) configurations (multipolarization/multitemporal). To this end, C-band AirSAR and European Remote Sensing Satellite (ERS) data collected on the Flevoland site, extracted from the European RAdar-Optical Research Assemblage (ERA-ORA) library, have been used. The results obtained in classifying seven types of crops are discussed on the basis of the computed confusion matrices. The effect of increasing the number of polarizations and/or measurements dates are discussed and a scheme of interyear dynamic classification of five crop types is considered.

Considers the retrieval of cloud liquid from the brightness temperature measurements taken by a ground-based multifrequency microwave radiometer using a neural network-based inversion algorithm. Comparisons between the retrieved quantity and the atmospheric information provided by active instruments such as a 94-GHz radar and a lidar ceilometer, co-located on the RAL Chilbolton Observatory site (UK), are carried out.

The passive calibration of the radar altimeter consists in characterising the receiver by observing natural surfaces with known emission in the so-called noise-sensing mode. The paper focuses on the general approach undertaken to simulate the brightness temperature at the top of the atmosphere observed by the Envisat Radar Altimeter (RA-2). It is based on emissivity models for land and sea as well as atmospheric radiation models supported by a continuous flow of on-line data used as model inputs.

The capability of multifrequency polarimetric synthetic aperture radar (SAR) to discriminate among nine vegetation classes is shown using both experimental data and model simulations. The experimental data were collected by the multifrequency polarimetric AIRSAR at the Dutch Flevoland site and the Italian Montespertoli site. Simulations are carried out using an electromagnetic model, developed at Tor Vergata University, Rome, Italy, which computes microwave vegetation scattering. The classes have been defined on the basis of geometrical differences among vegetation species, leading to different polarimetric signatures. It is demonstrated that, for each class, there are some combinations of frequencies and polarizations producing a significant separability. On the basis of this background, a simple, hierarchical parallelepiped algorithm is proposed.

A new neural network algorithm for the inversion of radiometric data to retrieve atmospheric profiles of temperature and vapor has been developed. The potentiality of the neural networks has been exploited not only for inversion purposes but also for data feature extraction and dimensionality reduction. In its complete form, the algorithm uses a neural network architecture consisting of three stages: 1) the input stage reduces the dimension of the input vector; 2) the middle stage performs the mapping from the reduced input vector to the reduced output vector; 3) the third stage brings the output of the middle stage to the desired actual dimension. The effectiveness of the algorithm has been evaluated comparing its performance to that obtainable with more traditional linear techniques.

Polarimetric radar data collected by AIRSAR and SIR-C over agricultural fields, forests, and olive groves of the Italian Montespertoli site are analyzed. The objective is to investigate the radar capability in discriminating among various vegetation species and its sensitivity to agricultural and arboreous biomass. Results indicate that a combined use of P(0.45 GHz) and L- (1.2 GHz) bands allows one to discriminate between agricultural fields and other targets, while a combined use of L- and C- (5.3 GHz) bands allows the authors to discriminate within agricultural areas. To monitor biomass, P-band gives the best results for forests and olive groves, L-band appears to be good for crops with low plant density (m/sup -2/), while for crops with high plant density, both L- and C-bands are useful. The availability of crosspolarized data is important for both classification and biomass retrieval.

A comparative evaluation of the potential of active and passive microwave sensors in estimating vegetation biomass and soil moisture content is carried out. For this purpose, experimental data collected on an agricultural area by airborne scatterometers and radiometers during the AGRISCATT and AGRIRAD 1988 campaigns have been used. The results show that both microwave backscattering and emission are sensitive to vegetation biomass over a wide frequency range. Multifrequency observations seem to offer good probabilities for separating wide leaf from small leaf herbaceous crops, and for detecting different growth stages. Low frequency data (L band) at a steep incidence angle (10 degrees ) confirm that both the backscattering coefficient and the normalized temperature are correlated and sensitive to soil moisture content.>