{"type": "FeatureCollection", "features": [{"id": "10.1109/jstars.2019.2958847", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:18:40Z", "type": "Journal Article", "created": "2020-01-22", "title": "Sentinel-1 InSAR Coherence for Land Cover Mapping: A Comparison of Multiple Feature-Based Classifiers", "description": "Open AccessThis article investigates and demonstrates the suitability of the Sentinel-1 interferometric coherence for land cover and vegetation mapping. In addition, this study analyzes the performance of this feature along with polarization and intensity products according to different classification strategies and algorithms. Seven different classification workflows were evaluated, covering pixel- and object-based analyses, unsupervised and supervised classification, different machine-learning classifiers, and the various effects of distinct input features in the SAR domain\u2014interferometric coherence, backscattered intensities, and polarization. All classifications followed the Corine land cover nomenclature. Three different study areas in Europe were selected during 2015 and 2016 campaigns to maximize diversity of land cover. Overall accuracies (OA), ranging from 70% to 90%, were achieved depending on the study area and methodology, considering between 9 and 15 classes. The best results were achieved in the rather flat area of Do\u00f1ana wetlands National Park in Spain (OA 90%), but even the challenging alpine terrain around the city of Merano in northern Italy (OA 77%) obtained promising results. The overall potential of Sentinel-1 interferometric coherence for land cover mapping was evaluated as very good. In all cases, coherence-based results provided higher accuracies than intensity-based strategies, considering 12 days of temporal sampling of the Sentinel-1 A stack. Both coherence and intensity prove to be complementary observables, increasing the overall accuracies in a combined strategy. The accuracy is expected to increase when Sentinel-1 A/B stacks, i.e., six-day sampling, are considered.", "keywords": ["Teledetecci\u00f3", "550", "Interferometric coherence", "Geophysics. 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Life on land", "[SPI.TRON] Engineering Sciences [physics]/Electronics", "SDG 11 - Sustainable Cities and Communities", "[SPI.TRON]Engineering Sciences [physics]/Electronics", "Ocean engineering", "Synthetic aperture radar (SAR)", "[SPI.ELEC]Engineering Sciences [physics]/Electromagnetism", "\u00c0rees tem\u00e0tiques de la UPC::Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3", ":Enginyeria de la telecomunicaci\u00f3::Radiocomunicaci\u00f3 i exploraci\u00f3 electromagn\u00e8tica::Teledetecci\u00f3 [\u00c0rees tem\u00e0tiques de la UPC]", "13. Climate action", "Teor\u00eda de la Se\u00f1al y Comunicaciones", "Sentinel-1", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "Land cover mapping", "Copernicus"]}, "links": [{"href": "https://doi.org/10.1109/jstars.2019.2958847"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/IEEE%20Journal%20of%20Selected%20Topics%20in%20Applied%20Earth%20Observations%20and%20Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.1109/jstars.2019.2958847", "name": "item", "description": "10.1109/jstars.2019.2958847", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.1109/jstars.2019.2958847"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "10.3390/rs8110938", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:21:18Z", "type": "Journal Article", "created": "2016-11-11", "title": "Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples", "description": "<p>This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth. Using the European Space Agency\uffe2\uff80\uff99s (ESA) Sen2Cor algorithm, the platform processes ESA\uffe2\uff80\uff99s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data. Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value).</p>", "keywords": ["550", "reflectance", "t\u00e9l\u00e9d\u00e9tection", "Science", "0211 other engineering and technologies", "02 engineering and technology", "7. Clean energy", "remote sensing", "Traitement du signal et de l'image", "atmospheric correction", "remote sensing;sentinel-2;atmospheric correction;Sen2Cor;LAI;broadband HDRF", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing", "9. Industry and infrastructure", "sentinel-2", "Q", "Signal and Image processing", "04 agricultural and veterinary sciences", "broadband HDRF", "620", "LAI", "atmosph\u00e8re", "Sen2Cor", "13. Climate action", "0401 agriculture", " forestry", " and fisheries", "Sentinel-2", "[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing", "donn\u00e9e satellitaire"]}, "links": [{"href": "http://www.mdpi.com/2072-4292/8/11/938/pdf"}, {"href": "https://doi.org/10.3390/rs8110938"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Remote%20Sensing", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.3390/rs8110938", "name": "item", "description": "10.3390/rs8110938", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.3390/rs8110938"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2016-11-11T00:00:00Z"}}, {"id": "10.5220/0009169301030110", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:22:06Z", "type": "Journal Article", "created": "2020-03-19", "title": "Two-step Multi-spectral Registration Via Key-point Detector and Gradient Similarity: Application to Agronomic Scenes for Proxy-sensing", "description": "The potential of multi-spectral images is growing rapidly in precision agriculture, and is currently based on the use of multi-sensor cameras. However, their development usually concerns aerial applications and their parameters are optimized for high altitudes acquisition by drone (UAV \u2248 50 meters) to ensure surface coverage and reduce technical problems. With the recent emergence of terrestrial robots (UGV), their use is diverted for nearby agronomic applications. Making it possible to explore new agronomic applications, maximizing specific traits extraction (spectral index, shape, texture \u2026) which requires high spatial resolution. The problem with these cameras is that all sensors are not aligned and the manufacturers\u2019 methods are not suitable for close-field acquisition, resulting in offsets between spectral images and degrading the quality of extractable informations. We therefore need a solution to accurately align images in such condition. In this study we propose a two-steps method applied to the six-bands Airphen multi-sensor camera with (i) affine correction using pre-calibrated matrix at different heights, the closest transformation can be selected via internal GPS and (ii) perspective correction to refine the previous one, using key-points matching between enhanced gradients of each spectral bands. Nine types of key-point detection algorithms (ORB, GFTT, AGAST, FAST, AKAZE, KAZE, BRISK, SURF, MSER) with three different modalities of parameters were evaluated on their speed and performances, we also defined the best reference spectra on each of them. The results show that GFTT is the most suitable methods for key-point extraction using our enhanced gradients, and the best spectral reference was identified to be the band centered on 570 nm for this one. Without any treatment the initial error is about 62 px, with our method, the remaining residual error is less than 1 px, where the manufacturer\u2019s involves distortions and loss of information with an estimated residual error of approximately 12 px", "keywords": ["03 medical and health sciences", "0302 clinical medicine", "Registration", "Registration", " Multi-spectral imagery", " Precision farming", " Feature descriptor", "0202 electrical engineering", " electronic engineering", " information engineering", "[SDV.SA.STA] Life Sciences [q-bio]/Agricultural sciences/Sciences and technics of agriculture", "Precision farming", "Feature descriptor", "Multi-spectral imagery", "02 engineering and technology", "15. Life on land", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"]}, "links": [{"href": "https://doi.org/10.5220/0009169301030110"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Proceedings%20of%20the%2015th%20International%20Joint%20Conference%20on%20Computer%20Vision%2C%20Imaging%20and%20Computer%20Graphics%20Theory%20and%20Applications", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5220/0009169301030110", "name": "item", "description": "10.5220/0009169301030110", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5220/0009169301030110"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}, {"id": "7d17628cc22c5a84c405669037b92bc8", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-29T16:27:51Z", "type": "Report", "title": "Characterization of fibronectin networks using graph-based representations of the fibers from 2D confocal images", "description": "Open AccessA major constituent of the Extracellular Matrix is a large protein called the Fibronectin (FN). Cellular FN is organized in fibrillar networks and can be assembled differently in the presence of two Extra Domains, EDA and EDB. Our objective was to develop numerical quantitative biomarkers to characterize the geometrical organization of the four FN variants (that differ by the inclusion/exclusion of EDA/EDB) from 2D confocal microscopy images, and to compare sane and cancerous tissues. First, we showed through two classification pipelines, based on curvelet features and deep learning framework, that the FN variants can be distinguished with a similar performance to that of a human annotator. We constructed a graph-based representation of the fibers, which were detected using Gabor filters. Graphspecific attributes were employed to classify the variants, proving that the graph representation embeds relevant information from the confocal images. Furthermore, we identified various techniques capable to differentiate the graphs, allowing us to compare the FN variants quantitatively and qualitatively. Performance analysis using toy graphs showed that the methods, which are based on graph matching and optimal transport, can meaningfully compare graphs. Using the graph-matching framework, we proposed different methodologies for defining the prototype graph, representative of a certain FN class. Additionally, the graph matching served as a tool to compute parameter deformation maps between the variants. These deformation maps were analyzed in a statistical framework showing whether or not the variation of the parameters can be explained by the variance within the same class.", "keywords": ["Appariement de graphes", "Traitement d\u2019images", "Extracellular matrix", "Statistical parametric maps", "Cartes statistiques des parametres", "Image processing", "Matrice extracellulaire", "Machine learning", "Fibronectine", "Apprentissage machine", "Fibronectin", "Graph-matching", "Cancer", "[SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing"], "contacts": [{"organization": "Grapa, Anca-Ioana", "roles": ["creator"]}]}, "links": [{"href": "https://doi.org/7d17628cc22c5a84c405669037b92bc8"}, {"rel": "self", "type": "application/geo+json", "title": "7d17628cc22c5a84c405669037b92bc8", "name": "item", "description": "7d17628cc22c5a84c405669037b92bc8", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/7d17628cc22c5a84c405669037b92bc8"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2020-01-01T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BSPI.SIGNAL%5D+Engineering+Sciences+%5Bphysics%5D%2FSignal+and+Image+processing&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BSPI.SIGNAL%5D+Engineering+Sciences+%5Bphysics%5D%2FSignal+and+Image+processing&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BSPI.SIGNAL%5D+Engineering+Sciences+%5Bphysics%5D%2FSignal+and+Image+processing&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=%5BSPI.SIGNAL%5D+Engineering+Sciences+%5Bphysics%5D%2FSignal+and+Image+processing&offset=4", "hreflang": "en-US"}], "numberMatched": 4, "numberReturned": 4, "distributedFeatures": [], "timeStamp": "2026-05-30T11:09:41.672679Z"}