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Classification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)
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[article]
Titre : Classification of hyperspectral and LiDAR data using coupled CNNs Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Zhu Li, Auteur ; Pedram Ghamisi, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4939 - 4950 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] données hétérogènes
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion de données
[Termes IGN] Houston (Texas)
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du sol
[Termes IGN] Perceptron multicouche
[Termes IGN] précision de la classification
[Termes IGN] semis de points
[Termes IGN] Trente
[Termes IGN] utilisation du solRésumé : (auteur) In this article, we propose an efficient and effective framework to fuse hyperspectral and light detection and ranging (LiDAR) data using two coupled convolutional neural networks (CNNs). One CNN is designed to learn spectral–spatial features from hyperspectral data, and the other one is used to capture the elevation information from LiDAR data. Both of them consist of three convolutional layers, and the last two convolutional layers are coupled together via a parameter-sharing strategy. In the fusion phase, feature-level and decision-level fusion methods are simultaneously used to integrate these heterogeneous features sufficiently. For the feature-level fusion, three different fusion strategies are evaluated, including the concatenation strategy, the maximization strategy, and the summation strategy. For the decision-level fusion, a weighted summation strategy is adopted, where the weights are determined by the classification accuracy of each output. The proposed model is evaluated on an urban data set acquired over Houston, USA, and a rural one captured over Trento, Italy. On the Houston data, our model can achieve a new record overall accuracy (OA) of 96.03%. On the Trento data, it achieves an OA of 99.12%. These results sufficiently certify the effectiveness of our proposed model. Numéro de notice : A2020-391 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2969024 Date de publication en ligne : 06/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2969024 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95374
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4939 - 4950[article]Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data / Sugandh Chauhan in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)
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Titre : Discriminant analysis for lodging severity classification in wheat using RADARSAT-2 and Sentinel-1 data Type de document : Article/Communication Auteurs : Sugandh Chauhan, Auteur ; Roshanak Darvishzadeh, Auteur ; Mirco Boschetti, Auteur ; Andrew Nelson, Auteur Année de publication : 2020 Article en page(s) : pp 138 - 151 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] agrégation de données
[Termes IGN] analyse diachronique
[Termes IGN] analyse discriminante
[Termes IGN] blé (céréale)
[Termes IGN] courbure
[Termes IGN] gestion prévisionnelle
[Termes IGN] image Radarsat
[Termes IGN] image Sentinel-SAR
[Termes IGN] Italie
[Termes IGN] matrice de confusion
[Termes IGN] méthode des moindres carrés
[Termes IGN] rendement agricole
[Termes IGN] surveillance agricoleRésumé : (auteur) Crop lodging - the bending of crop stems from their upright position or the failure of root-soil anchorage systems - is a major yield-reducing factor in wheat and causes deterioration of grain quality. The severity of lodging can be measured by a lodging score (LS)- an index calculated from the crop angle of inclination (CAI) and crop lodged area (LA). LS is difficult and time consuming to measure manually meaning that information on lodging occurrence and severity is limited and sparse. Remote sensing-based estimates of LS can provide more timely, synoptic and reliable information on crop lodging across vast areas. This information could improve estimates of crop yield losses, inform insurance loss adjusters and influence management decisions for subsequent seasons. This research - conducted in the 600 ha wheat sown area in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy - evaluated the performance of RADARSAT-2 and Sentinel-1 data to discriminate and classify lodging severity based on field measured LS. We measured temporal crop status characteristics related to lodging (e.g. lodged area, CAI, crop height) and collected relevant meteorological data (wind speed and rainfall) throughout May-June 2018. These field measurements were used to distinguish healthy (He) wheat from lodged wheat with different degrees of lodging severity (moderate, severe and very severe). We acquired multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 and Sentinel-1 (40°) images and derived multiple metrics from them to discriminate and classify lodging severity. As a part of our data exploration, we performed a correlation analysis between the image-based metrics and LS. Next, a multi-temporal discriminant analysis approach, including a partial least squares (PLS-DA) method, was developed to classify lodging severities. We used the area under the curve-receiver operating characteristics (AUC-ROC) and confusion matrices to evaluate the accuracy of the PLS-DA classification models. Results show that (1) volume scattering components were highly correlated with LS at low incidence angles while double and surface scattering was more prevalent at high incidence angles; (2) lodging severity was best classified using low incidence angle R-FQ8 data (overall accuracy 72%) and (3) the Sentinel-1 data-based classification model was able to correctly identify 60% of the lodging severity cases in the study site. The results from this first study on classifying lodging severity using satellite-based SAR platforms suggests that SAR-based metrics can capture a substantial proportion of the observed variation in lodging severity, which is important in the context of operational crop lodging assessment in particular, and sustainable agriculture in general. Numéro de notice : A2020-276 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.012 Date de publication en ligne : 29/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.012 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95087
in ISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 138 - 151[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020061 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020063 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020062 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Hydrogeology of the western Po plain (Piedmont, NW Italy) / Domenico Antonio De Luca in Journal of maps, vol 16 n° 2 ([01/06/2020])
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Titre : Hydrogeology of the western Po plain (Piedmont, NW Italy) Type de document : Article/Communication Auteurs : Domenico Antonio De Luca, Auteur ; Manuela Lasagna, Auteur ; laura Debernardi, Auteur Année de publication : 2020 Article en page(s) : pp 265 - 273 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Cartographie thématique
[Termes IGN] 1:300.000
[Termes IGN] aquifère
[Termes IGN] carte hydrogéologique
[Termes IGN] eau souterraine
[Termes IGN] gestion de l'eau
[Termes IGN] gestion des risques
[Termes IGN] lit majeur
[Termes IGN] Pô (plaine)
[Termes IGN] sédimentRésumé : (auteur) This paper describes the hydrogeological map of the western Po Plain, located in Piedmont (north-western Italy). Po plain represents a hydrogeological system of European relevance, and the Piedmont Plain is the most important groundwater reservoir of the Region. The 1:300,000 scale map was realised using previous and new data to update the knowledge of this area. The map provides information about the hydrogeological complexes and their type and degree of permeability, water table levels and depth, piezometric level fluctuation, lithostratigraphic cross-sections, thickness, and percentage of the permeable deposits between 0 and 50 m from the ground surface. All this information is essential to public administrations, stakeholders, researchers, and professionals for defining possible tools for groundwater protection and management and for planning new groundwater exploitation (i.e. municipal drinking water supplies). Numéro de notice : A2020-646 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/17445647.2020.1738280 Date de publication en ligne : 18/03/2020 En ligne : https://doi.org/10.1080/17445647.2020.1738280 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96073
in Journal of maps > vol 16 n° 2 [01/06/2020] . - pp 265 - 273[article]Incorporating landscape character in cork oak forest expansion in Sardinia: constraint or opportunity? / I.N. Vogiatzakis in Forests, vol 11 n° 5 (May 2020)
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Titre : Incorporating landscape character in cork oak forest expansion in Sardinia: constraint or opportunity? Type de document : Article/Communication Auteurs : I.N. Vogiatzakis, Auteur ; Geoffrey H. Griffiths, Auteur ; Maria Zomeni, Auteur Année de publication : 2020 Article en page(s) : 18 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] biodiversité végétale
[Termes IGN] changement d'utilisation du sol
[Termes IGN] habitat forestier
[Termes IGN] modélisation spatiale
[Termes IGN] occupation du sol
[Termes IGN] paysage
[Termes IGN] protection des forêts
[Termes IGN] Quercus suber
[Termes IGN] Sardaigne
[Termes IGN] site Natura 2000Résumé : (auteur) Cork oak (Quercus suber) is a declining woodland species across the island of Sardinia, despite its former economic importance for wine production and its significance for biodiversity. In particular, cork oak forests (COFs) on the island have seen a 29% decrease in the past 45 years. A spatial GIS model was developed to determine suitability for the expansion of cork oak forests on the island. The model uses a set of simple spatial decision rules based on principles of landscape ecology and expert opinion to assign a suitability score for pure cork oak forests to every land use parcel in Sardinia. These rules include the type of existing land parcel, its size, distance to existing cork oak forest, and the area of seminatural habitats in its neighborhood. This was coupled with a map of landscape types to assist with the development of policy for the protection of cork oak forests across Sardinia. The results show that there is an area of 116,785 ha potentially suitable for cork oak forest expansion in Sardinia, with the largest area of potential habitat on granitic mountains. There is a substantial overall agreement (Cohen’s kappa = 0.61) between the suitability map produced and the historical reference map. The model is flexible and can be rerun to reflect changes in policy relating to agri-environmental targets for habitats and species. Numéro de notice : A2020-653 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE Nature : Article DOI : 10.3390/f11050593 Date de publication en ligne : 24/05/2020 En ligne : https://doi.org/10.3390/f11050593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96113
in Forests > vol 11 n° 5 (May 2020) . - 18 p.[article]Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis / T. Poblete in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)
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Titre : Detection of Xylella fastidiosa infection symptoms with airborne multispectral and thermal imagery: Assessing bandset reduction performance from hyperspectral analysis Type de document : Article/Communication Auteurs : T. Poblete, Auteur ; C. Camino, Auteur ; P.S.A. Beck, Auteur Année de publication : 2020 Article en page(s) : pp 27 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] chlorophylle
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espèce végétale
[Termes IGN] fluorescence
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] image satellite
[Termes IGN] image thermique
[Termes IGN] Italie
[Termes IGN] maladie bactérienne
[Termes IGN] maladie phytosanitaire
[Termes IGN] Olea europaea
[Termes IGN] stress hydrique
[Termes IGN] surveillance de la végétation
[Termes IGN] télédétection aérienne
[Termes IGN] traitement d'imageRésumé : (auteur) Xylella fastidiosa (Xf) is a harmful plant pathogenic bacterium, able to infect over 500 plant species worldwide. Successful eradication and containment strategies for harmful pathogens require large-scale monitoring techniques for the detection of infected hosts, even when they do not display visual symptoms. Although a previous study using airborne hyperspectral and thermal imagery has shown promising results for the early detection of Xf-infected olive (Olea europaea) trees, further work is needed when adopting these techniques for large scale monitoring using multispectral cameras on board airborne platforms and satellites. We used hyperspectral and thermal imagery collected during a two-year airborne campaign in a Xf-infected area in southern Italy to assess the performance of spectrally constrained machine-learning algorithms for this task. The algorithms were used to assess multispectral bandsets, selected from the original hyperspectral imagery, that were compatible with large-scale monitoring from unmanned platforms and manned aircraft. In addition, the contribution of solar–induced chlorophyll fluorescence (SIF) and the temperature-based Crop Water Stress Index (CWSI) retrieved from hyperspectral and thermal imaging, respectively, were evaluated to quantify their relative importance in the algorithms used to detect Xf infection. The detection performance using support vector machine algorithms decreased from ∼80% (kappa, κ = 0.42) when using the original full hyperspectral dataset including SIF and CWSI to ∼74% (κ = 0.36) when the optimal set of six spectral bands most sensitive to Xf infection were used in addition to the CWSI thermal indicator. When neither SIF nor CWSI were used, the detection yielded less than 70% accuracy (decreasing κ to very low performance, 0.29), revealing that tree temperature was more important than chlorophyll fluorescence for the Xf detection. This work demonstrates that large-scale Xf monitoring can be supported using airborne platforms carrying multispectral and thermal cameras with a limited number of spectral bands (e.g., six to 12 bands with 10 nm bandwidths) as long as they are carefully selected by their sensitivity to the Xf symptoms. More precisely, the blue (bands between 400 and 450 nm to derive the NPQI index) and thermal (to derive CWSI from tree temperature) were the most critical spectral regions for their sensitivity to Xf symptoms in olive. Numéro de notice : A2020-120 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.02.010 Date de publication en ligne : 18/02/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.02.010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94745
in ISPRS Journal of photogrammetry and remote sensing > vol 162 (April 2020) . - pp 27 - 40[article]Effects of Quercus rubra L. on soil properties and humus forms in 50-year-old and 80-year-old forest stands of Lombardy plain / Chiara Ferré in Annals of Forest Science, Vol 77 n° 1 (March 2020)
PermalinkLarge-scale two-phase estimation of wood production by poplar plantations exploiting Sentinel-2 data as auxiliary information / Agnese Marcelli in Silva fennica, vol 54 n° 2 (March 2020)
PermalinkComplex deformation at shallow depth during the 30 October 2016 Mw6.5 Norcia earthquake: interferencebetween tectonic and gravity processes? / Arthur Delorme in Tectonics, vol 39 n° 2 (February 2020)
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PermalinkRadial interpolation of GPS and leveling data of ground deformation in a resurgent caldera: application to Campi Flegrei (Italy) / Andrea Bevilacqua in Journal of geodesy, vol 94 n°2 (February 2020)
PermalinkThe potentiality of Sentinel-2 to assess the effect of fire events on Mediterranean mountain vegetation / Walter de Simone in Plant sociology, vol 57 n° 1 ([01/02/2020])
PermalinkCombining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy / Zoë E. Wakeford in Photogrammetric record, vol 34 n° 168 (December 2019)
PermalinkInnovative techniques of photogrammetry for 3D modeling / Vicenzo Barrile in Applied geomatics, Vol 11 n° 4 (December 2019)
PermalinkComparison between convolutional neural networks and random forest for local climate zone classification in mega urban areas using Landsat images / Cheolhee Yoo in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)
PermalinkRéflexions d’une paysagiste sur la progression des boisements spontanés dans les Alpes et les Pyrénées / Françoise Copin in Revue forestière française, vol 71 n° 4-5 (2019)
PermalinkThe Parallel SBAS approach for Sentinel-1 interferometric wide swath deformation time-series generation: algorithm description and products quality assessment / Michele Manunta in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)
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