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.

Super-resolution is aimed at enhancing image spatial resolution and it has been intensively explored for many years. The recent advancements, underpinned with deep learning, also include techniques developed specifically for hyper-spectral data. However, most of the emerging methods are validated in application-independent scenarios, which often rely on an unrealistic experimental setup—the reconstruction is performed from simulated low-resolution images (degraded from an original image) with the goal of inverting the degradation process and restoring the original image. This leads to over-optimistic assessment of super-resolution capabilities and limits their practical applications. In this paper, we demonstrate task-based validation for different types of hyperspectral PRISMA image super-resolution, including pan-sharpening, fusion of multispectral and hyperspectral data, as well as single-image super-resolution. The obtained results reported in the paper are encouraging and they help better understand the value of super-resolved PRISMA images.

There is an urgent need to assess the condition of transport network in many countries all over the world as in Italy and, in this context, the consideration of non-destructive techniques has particular importance. These techniques, such as space-born systems, laser scanners, ground-penetrating radar (GPR), and monitoring tests, provide valuable data but have limitations in assessing specific aspects of the infrastructure. This research aims to overcome these limitations by using a "data fusion" approach to achieve a comprehensive understanding of the asset’s condition. The ongoing project "EXTRA-TN" focuses on road infrastructures selected as case-studies, located in Salerno (Italy), utilizing the various aforementioned methods. This integrated approach aims to enhance infrastructure resilience, provide valuable information for maintenance scheduling, and monitor both external and internal factors affecting safety and functionality.

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.

In this paper, we present our study focused on applying a vision transformer-based pansharpening technique to enhance PRISMA satellite hyperspectral data. The PRISMA mission, launched by the Italian Space Agency, captures hyperspectral images comprising visible and near infra-red, as well as short-wave infra-red channels. By integrating the panchromatic image of high spatial resolution with the hyperspectral data of high spectral resolution, the pansharpening process consists in producing spatially-enhanced hyperspectral imagery. Our research involves modifying and adapting the state-of-the-art HyperTransformer architecture to effectively process real-life PRISMA data. The evaluation of our model’s performance utilizes PRISMA L2D data, encompassing simulated low-resolution data and real-life data. We employ quantitative metrics and visual examination to assess the results. We also highlight the importance of choosing right PRISMA data processing level for the pansharpening process. The proposed pansharpening model successfully enhances PRISMA data for practical applications, contributing to the advancement of Earth observation techniques.

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 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.

The aim of this paper is to introduce new algorithms for the oil spill detection taking fully advantage of the polarimetric and textural features contained in new generation SAR data such as those provided by Radarsat-2 and COSMO-SkyMed missions. The SAR information is exploited using a new statistical decomposition method based on AANN. Thanks to the AANN the original image is represented in terms of Nonlinear principal components (NLPC). The oil spill detection procedure is then directly applied to the new generated components.

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.

Dark-spot detection is a critical step in oil-spill detection. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar imagery is presented. A new approach from the combination of Weibull multiplicative model (WMM) and pulse-coupled neural network (PCNN) techniques is proposed to differentiate between the dark spots and the background. First, the filter created based on WMM is applied to each subimage. Second, the subimage is segmented by PCNN techniques. As the last step, a very simple filtering process is used to eliminate the false targets. The proposed approach was tested on 60 Envisat and ERS2 images which contained dark spots. The same parameters were used in all tests. For the overall data set, an average accuracy of 93.66% was obtained. The average computational time for dark-spot detection with a 512 × 512 image is about 7 s using IDL software, which is the fastest one in this field at present. Our experimental results demonstrate that the proposed approach is very fast, robust, and effective. The proposed approach can be applied on any kind of synthetic aperture radar imagery.

The mapping of the coastline is a well known and tested procedure exploiting the capabilities of optical satellite sensor. Nevertheless, it is affected by several inherent limits like weather condition and revisit time of the areas. The recent availability of very high-resolution X-Band SAR data acquired by a constellation of satellites with frequent revisit capabilities has brought a potential alternative, or support, for this kind of application. To this purpose a new automatic algorithm, based on Pulse Coupled Neural Networks, has been developed to process COSMO-SkyMed products taken with different polarization, geometric configuration and measurements mode. The results have been validated through a GPS survey, also respect to a traditional C-band technique applied on X-band, with the final intent of an assessment of the real impact of the proposed procedure in the coastal mapping application.