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Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images Type de document : Article/Communication Auteurs : Donato Amitrano, Auteur ; Raffaella Guida, Auteur ; Pasquale Lervolino, Auteur Année de publication : 2021 Article en page(s) : pp 5494 - 5514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse d'image orientée objet
[Termes IGN] biomasse forestière
[Termes IGN] canopée
[Termes IGN] changement d'occupation du sol
[Termes IGN] classification floue
[Termes IGN] classification non dirigée
[Termes IGN] déboisement
[Termes IGN] détection de changement
[Termes IGN] image multitemporelle
[Termes IGN] image radar moirée
[Termes IGN] image RVB
[Termes IGN] image Sentinel-SAR
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] segmentation d'image
[Termes IGN] seuillage d'image
[Termes IGN] texture d'imageRésumé : (auteur) Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. Numéro de notice : A2021-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029841 Date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97978
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5494 - 5514[article]Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data / Claire Teillet in Remote sensing, vol 13 n° 12 (June-2 2021)
[article]
Titre : Fast unsupervised multi-scale characterization of urban landscapes based on Earth observation data Type de document : Article/Communication Auteurs : Claire Teillet, Auteur ; Benjamin Pillot, Auteur ; Thibault Catry, Auteur ; Laurent Demagistri, Auteur ; Dominique Lyszczarz, Auteur ; Marc Lang, Auteur ; Pierre Couteron, Auteur ; Nicolas Barbier, Auteur ; Arsène Adou Kouassi, Auteur ; Quentin Gunther , Auteur ; Nadine Dessay, Auteur Année de publication : 2021 Projets : GeoSud / , TOSCA / Article en page(s) : n° 2398 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Brasilia
[Termes IGN] caractérisation
[Termes IGN] Côte d'Ivoire
[Termes IGN] empreinte
[Termes IGN] image Pléiades-HR
[Termes IGN] image Sentinel-MSI
[Termes IGN] paysage urbain
[Termes IGN] texture d'image
[Termes IGN] zone urbaineRésumé : (auteur) Most remote sensing studies of urban areas focus on a single scale, using supervised methodologies and very few analyses focus on the “neighborhood” scale. The lack of multi-scale analysis, together with the scarcity of training and validation datasets in many countries lead us to propose a single fast unsupervised method for the characterization of urban areas. With the FOTOTEX algorithm, this paper introduces a texture-based method to characterize urban areas at three nested scales: macro-scale (urban footprint), meso-scale (“neighbourhoods”) and micro-scale (objects). FOTOTEX combines a Fast Fourier Transform and a Principal Component Analysis to convert texture into frequency signal. Several parameters were tested over Sentinel-2 and Pleiades imagery on Bouake and Brasilia. Results showed that a single Sentinel-2 image better assesses the urban footprint than the global products. Pleiades images allowed discriminating neighbourhoods and urban objects using texture, which is correlated with metrics such as building density, built-up and vegetation proportions. The best configurations for each scale of analysis were determined and recommendations provided to users. The open FOTOTEX algorithm demonstrated a strong potential to characterize the three nested scales of urban areas, especially when training and validation data are scarce, and computing resources limited. Numéro de notice : A2021-505 Affiliation des auteurs : ENSG+Ext (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13122398 Date de publication en ligne : 19/06/2021 En ligne : https://doi.org/10.3390/rs13122398 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98125
in Remote sensing > vol 13 n° 12 (June-2 2021) . - n° 2398[article]Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey / Alkan Günlü in Geocarto international, vol 36 n° 8 ([01/05/2021])
[article]
Titre : Estimation of some stand parameters from textural features from WorldView-2 satellite image using the artificial neural network and multiple regression methods: a case study from Turkey Type de document : Article/Communication Auteurs : Alkan Günlü, Auteur ; İlker Ercanlı, Auteur ; Muammer Şenyurt, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 918 - 935 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] biomasse aérienne
[Termes IGN] classification par Perceptron multicouche
[Termes IGN] échantillonnage
[Termes IGN] fonction de base radiale
[Termes IGN] gestion forestière
[Termes IGN] image proche infrarouge
[Termes IGN] image Worldview
[Termes IGN] matrice de co-occurrence
[Termes IGN] peuplement forestier
[Termes IGN] Pinus nigra
[Termes IGN] régression multiple
[Termes IGN] réseau neuronal artificiel
[Termes IGN] texture d'image
[Termes IGN] TurquieRésumé : (auteur) The aim of this research is to assess some stand parameters such as stand volume (SV), basal area (BA), number of trees (NT) and aboveground biomass (AGB) of pure Crimean pine forest stands in Turkey by using ground measurements and remote sensing techniques. For this purpose, 86 sample plots were collected from pure Crimean pine stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. The stand parameters of each sample area were estimated using the data obtained from the sample plots. Subsequently, we calculated the values of contrast (CON), correlation (COR), dissimilarity (DIS), entropy (ENT), homogeneity (HOM), mean (M), second moment (SM) and variance (VAR) from WorldView-2 imagery using a grey-level co-occurrence matrix method. Eight textural features and twelve different window sizes ranging from 3 × 3 to 25 × 25 were generated from blue, green, red and near-infrared bands of the WorldView-2 satellite image. For predicting the relationships between WorldView-2 textural features and stand parameters of each sample plot, regression models were developed by using multiple linear regression (MLR) analysis. Additionally, artificial neural networks (ANNs) based on the multilayer perceptron (MLP) and the radial basis function (RBF) architectures were trained by comparing various numbers of neurons and activation functions in their network types. The results showed that the MLR models had low the coefficient of determination (R2) values (0.32 for SV, 0.35 for BA, 0.33 for NT and 0.34 for AGB), and the most of the ANNs models (MLP and RBF) were better than the regression models for estimating stand parameters. The ANNs model containing MLP and RBF for SV (R2 = 0.40; R2 = 0.56), for BA (R2 = 0.34; R2 = 0.51), for NT (R2 = 0.34; R2 = 0.37) and for AGB (R2 = 0.34, R2 = 0.57) were found the best results, respectively. Our results revealed that the ANNs models developed with WorldView-2 satellite image were beneficial to estimate stand parameters better than the MLR model in pure Crimean pine stands. Numéro de notice : A2021-484 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1629644 Date de publication en ligne : 25/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1629644 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97443
in Geocarto international > vol 36 n° 8 [01/05/2021] . - pp 918 - 935[article]Structure-aware completion of photogrammetric meshes in urban road environment / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)
[article]
Titre : Structure-aware completion of photogrammetric meshes in urban road environment Type de document : Article/Communication Auteurs : Qing Zhu, Auteur ; Qisen Shang, Auteur ; Han Hu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 56 - 70 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection d'objet
[Termes IGN] détection de partie cachée
[Termes IGN] espace urbain
[Termes IGN] image aérienne oblique
[Termes IGN] maillage
[Termes IGN] modélisation 3D
[Termes IGN] reconstruction de route
[Termes IGN] réseau routier
[Termes IGN] texture d'image
[Termes IGN] véhicule automobileRésumé : (auteur) Photogrammetric mesh models obtained from aerial oblique images have been widely used for urban reconstruction. However, photogrammetric meshes suffer from severe texture problems, particularly in typical road areas, owing to occlusion. This paper proposes a structure-aware completion approach to improve mesh quality by seamlessly removing undesired vehicles. Specifically, a discontinuous texture atlas is first integrated into a continuous screen space by rendering trough a graphics pipeline. The rendering also records the necessary mapping for deintegration to the original texture atlas after editing. Vehicle regions are masked by a standard object detection approach, namely, Faster RCNN. Subsequently, the masked regions are completed, guided by the linear structures and regularities in the road region; this is implemented based on PatchMatch. Finally, the completed rendered image is deintegrated to the original texture atlas, and the triangles for the vehicles are also flattened so that improved meshes can be obtained. Experimental evaluation and analysis are conducted on three datasets, which were captured with different sensors and ground sample distances. The results demonstrate that the proposed method can produce quite realistic meshes after removing the vehicles. The structure-aware completion approach for road regions outperforms popular image completion methods, and an ablation study further confirms the effectiveness of the linear guidance. It should be noted that the proposed method can also handle tiled mesh models for large-scale scenes. Code and datasets are available at the project website. Numéro de notice : A2021-263 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.010 Date de publication en ligne : 11/03/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.010 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97312
in ISPRS Journal of photogrammetry and remote sensing > vol 175 (May 2021) . - pp 56 - 70[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021051 SL Revue Centre de documentation Revues en salle Disponible 081-2021052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt 081-2021053 DEP-RECP Revue Saint-Mandé Dépôt en unité Exclu du prêt Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
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Titre : Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data Type de document : Article/Communication Auteurs : Xiao-Ming Li, Auteur ; Yan Sun, Auteur ; Qiang Zhang, Auteur Année de publication : 2021 Article en page(s) : pp 3040 - 3053 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Arctique, océan
[Termes IGN] classification non dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] entropie
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] glace de mer
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] polarisation croisée
[Termes IGN] rétrodiffusion
[Termes IGN] texture d'imageRésumé : (auteur) In this article, we focus on developing a novel method to extract sea ice cover (i.e., discrimination/classification of sea ice and open water) using Sentinel-1 (S1) cross-polarization [vertical–horizontal (VH) or horizontal–vertical (HV)] data in extra-wide (EW) swath mode based on the support vector machine (SVM) method. The classification basis includes the S1 radar backscatter and texture features, which are calculated from S1 data using the gray level co-occurrence matrix (GLCM). Different from previous methods where appropriate samples are manually selected to train the SVM to classify sea ice and open water, we proposed a method of unsupervised generation of the training samples based on two GLCM texture features, i.e., entropy and homogeneity, that have contrasting characteristics on sea ice and open water. We eliminate the most uncertainty of selecting training samples in machine learning and achieve automatic classification of sea ice and open water by using S1 EW data. The comparisons based on a few cases show good agreements between the synthetic aperture radar (SAR)-derived sea ice cover using the proposed method and visual inspections, of which the accuracy reaches approximately 90%–95%. Besides this, compared with the analyzed sea ice cover data Ice Mapping System (IMS) based on 728 S1 EW images, the accuracy of the extracted sea ice cover by using S1 data is more than 80%. Numéro de notice : A2021-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007789 Date de publication en ligne : 20/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007789 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97392
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3040 - 3053[article]Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)PermalinkSAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)PermalinkAmélioration et adaptation du protocole de mesure d’empreintes d’abrasion par photogrammétrie / Hiba Sayeh (2021)PermalinkContributions to graph-based hierarchical analysis for images and 3D point clouds / Leonardo Gigli (2021)PermalinkPermalinkGeometric and semantic joint approach for the reconstruction of digital models of buildings / Pierre-Alain Langlois (2021)PermalinkPermalinkUnderstanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection / Chandi Witharana in ISPRS Journal of photogrammetry and remote sensing, vol 170 (December 2020)PermalinkMapping tree species deciduousness of tropical dry forests combining reflectance, spectral unmixing, and texture data from high-resolution imagery / Astrid Helena Huechacona-Ruiz in Forests, vol 11 n°11 (November 2020)PermalinkTextural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])Permalink