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Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])  

Public [article]  inGeocarto international > vol 35 n° 10 [01/08/2020] . - pp 1088 - 1108 Titre : Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets Type de document : Article/Communication Auteurs : Lamin R. Mansaray, Auteur ; Fumin Wang, Auteur ; Jingfeng Huang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1088 - 1108 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] rizière [Termes descripteurs IGN] surface cultivée Résumé : (auteur) SVM and RF are widely used in rice mapping. However, their performance with single and different combinations of satellite datasets is rarely reported. Hence we report their rice mapping accuracies for two seasons using Sentinel-1A, Landsat-8 and Sentinel-2A images. The VH and VV polarizations of Sentinel-1A, and two spectral indices (SIs) of Landsat-8 and Sentine1-2A were used to obtain seven datasets (VH, VV, SI, VHVV, VHSI, VVSI and VHVVSI), and on which SVM and RF were applied and accuracies were assessed. VHSI showed the highest overall accuracy for both algorithms in both years. SVM with VHSI had a slightly higher accuracy (90.8%) than RF with VHSI (89.2%) in 2015 while in 2016 RF with VHSI showed a slightly higher accuracy (95.2%) than SVM with VHSI (93.4%). Although they produced equivalent accuracies within years, RF is more sensitive to additional data, given a 6.0% increase from 2015 to 2016 with VHSI. Numéro de notice : A2020-443 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1568586 date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1568586 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95501 [article]

Structure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 166 (August 2020) . - pp 140 - 152 Titre : Structure from motion for complex image sets Type de document : Article/Communication Auteurs : Mario Michelini, Auteur ; Helmut Mayer, Auteur Année de publication : 2020 Article en page(s) : pp 140 - 152 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] chambre de prise de vue numérique [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] étalonnage d'instrument [Termes descripteurs IGN] fusion de données multisource [Termes descripteurs IGN] itération [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] orientation [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] structure-from-motion Résumé : (auteur) This paper presents an approach for Structure from Motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines. Wide baselines occur for instance when terrestrial images and images from small Unmanned Aerial Systems (UAS) are combined. The resulting large (geometric/radiometric) distortions between images make image matching difficult possibly leading to an incomplete result. Weak baselines mean an insufficient distance between cameras compared to the distance of the observed scene and give rise to critical camera configurations. Inappropriate handling of such configurations may lead to various problems in triangulation-based SfM up to total failure. The focus of our approach lies on a complete linking of images even in case of wide or weak baselines. We do not rely on any additional information such as camera configurations, Global Positioning System (GPS) or an Inertial Navigation System (INS). As basis for generating suitable triplets to link the images, an iterative graph-based method is employed formulating image linking as the search for a terminal Steiner minimum tree in the line graph. SIFT (Lowe, 2004) descriptors are embedded into Hamming space for fast image similarity ranking. This is employed to limit the number of pairs to be geometrically verified by a computationally and more complex wide baseline matching method (Mayer et al., 2012). Critical camera configurations which are not suitable for geometric verification are detected by means of classification (Michelini and Mayer, 2019). Additionally, we propose a graph-based approach for the optimization of the hierarchical merging of triplets to efficiently generate larger image subsets. By this means, a complete, 3D reconstruction of the scene is obtained. Experiments demonstrate that the approach is able to produce reliable orientation for large image sets comprising wide as well as weak baseline configurations. Numéro de notice : A2020-355 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.05.020 date de publication en ligne : 12/06/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.05.020 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95242 [article]

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Transferring deep learning models for cloud detection between Landsat-8 and Proba-V / Gonzalo Mateo-García in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 160 (February 2020) . - pp 1 - 17 Titre : Transferring deep learning models for cloud detection between Landsat-8 and Proba-V Type de document : Article/Communication Auteurs : Gonzalo Mateo-García, Auteur ; Valero Laparra, Auteur ; Dan López-Puigdollers, Auteur ; Luis Gómez-Chova, Auteur Année de publication : 2020 Article en page(s) : pp 1 - 17 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage par transformation [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] conversion de données [Termes descripteurs IGN] détection des nuages [Termes descripteurs IGN] échantillonnage de données [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image PROBA [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] masque [Termes descripteurs IGN] réseau neuronal convolutif [Termes descripteurs IGN] seuillage de points Résumé : (Auteur) Accurate cloud detection algorithms are mandatory to analyze the large streams of data coming from the different optical Earth observation satellites. Deep learning (DL) based cloud detection schemes provide very accurate cloud detection models. However, training these models for a given sensor requires large datasets of manually labeled samples, which are very costly or even impossible to create when the satellite has not been launched yet. In this work, we present an approach that exploits manually labeled datasets from one satellite to train deep learning models for cloud detection that can be applied (or transferred) to other satellites. We take into account the physical properties of the acquired signals and propose a simple transfer learning approach using Landsat-8 and Proba-V sensors, whose images have different but similar spatial and spectral characteristics. Three types of experiments are conducted to demonstrate that transfer learning can work in both directions: (a) from Landsat-8 to Proba-V, where we show that models trained only with Landsat-8 data produce cloud masks 5 points more accurate than the current operational Proba-V cloud masking method, (b) from Proba-V to Landsat-8, where models that use only Proba-V data for training have an accuracy similar to the operational FMask in the publicly available Biome dataset (87.79–89.77% vs 88.48%), and (c) jointly from Proba-V and Landsat-8 to Proba-V, where we demonstrate that using jointly both data sources the accuracy increases 1–10 points when few Proba-V labeled images are available. These results highlight that, taking advantage of existing publicly available cloud masking labeled datasets, we can create accurate deep learning based cloud detection models for new satellites, but without the burden of collecting and labeling a large dataset of images. Numéro de notice : A2020-043 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.024 date de publication en ligne : 10/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.024 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94522 [article]

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A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images / Xiaohui Ding in International Journal of Remote Sensing IJRS, vol 41 n° 3 (15 - 22 janvier 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n° 3 (15 - 22 janvier 2020) . - pp 1093 - 1117 Titre : A restrictive polymorphic ant colony algorithm for the optimal band selection of hyperspectral remote sensing images Type de document : Article/Communication Auteurs : Xiaohui Ding, Auteur ; Shuqing Zhang, Auteur ; Huapeng Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1093 - 1117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] dimensionnalité [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] optimisation par colonie de fourmis [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] test de performance Résumé : (auteur) With hundreds of spectral bands, the rise of the issue of dimensionality in the classification of hyperspectral images is usually inevitable. In this paper, a restrictive polymorphic ant colony algorithm (RPACA) based band selection algorithm (RPACA-BS) was proposed to reduce the dimensionality of hyperspectral images. In the proposed algorithm, both local and global searches were conducted considering band similarity. Moreover, the problem of falling into local optima, due to the selection of similar band subsets although travelling different paths, was solved by varying the pheromone matrix between ants moving in opposite directions. The performance of the proposed RPACA-BS algorithm was evaluated using three public datasets (the Indian Pines, Pavia University and Botswana datasets) based on average overall classification accuracy (OA) and CPU processing time. The experimental results showed that average OA of RPACA-BS was up to 89.80%, 94.96% and 92.17% for the Indian Pines, Pavia University and Botswana dataset, respectively, which was higher than that of the benchmarks, including the ant colony algorithm-based band selection algorithm (ACA-BS), polymorphic ant colony algorithm-based band selection algorithm (PACA-BS) and other band selection methods (e.g. the ant lion optimizer-based band selection algorithm). Meanwhile, the time consumed by RPACA-BS and PACA-BS were slightly lower than that of ACA-BS but obviously lower than that of other benchmarks. The proposed RPACA-BS method is thus able to effectively enhance the search abilities and efficiencies of the ACA-BS and PACA-BS algorithms to handle the complex band selection issue for hyperspectral remotely sensed images. Numéro de notice : A2020-214 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1655810 date de publication en ligne : 20/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1655810 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94899 [article]

Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery / H. Tombul in Journal of geodetic science, vol 10 n° 1 (janvier 2020)  

Public [article]  inJournal of geodetic science > vol 10 n° 1 (janvier 2020) . - pp 14 - 22 Titre : Classification of poplar trees with object-based ensemble learning algorithms using Sentinel-2A imagery Type de document : Article/Communication Auteurs : H. Tombul, Auteur ; Ismail Colkesen, Auteur ; Taskin Kavzoglu, Auteur Année de publication : 2020 Article en page(s) : pp 14 - 22 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme d'apprentissage [Termes descripteurs IGN] analyse canonique [Termes descripteurs IGN] analyse comparative [Termes descripteurs IGN] bande spectrale [Termes descripteurs IGN] boosting adapté [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] carte thématique [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] jeu de données [Termes descripteurs IGN] populus (genre) [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] Rotation Forest classification [Termes descripteurs IGN] segmentation multirésolution [Termes descripteurs IGN] Turquie Résumé : (auteur) The poplar species in the forest ecosystems are one of the most valuable and beneficial species for the society and environment. Conventional methods require high cost, time and labor need, and the results obtained vary and are insu˚cient in terms of achieved accuracy level. Determination of poplar cultivated fields and mapping of their spatial sites play a vital role for decision-makers and planners to enhance the economic and ecological value of poplar trees. The study aims to map Poplar (P. deltoides) cultivated areas in Akyazi district of Sakarya, Turkey province using various combinations of the Sentinel-2A image bands. For this purpose, object-based classification based on multi-resolution segmentation algorithm was utilized to produce image objects and ensemble learning algorithms, namely, Adaboost (AdaB), Random Forest (RF), Rotation Forest (RotFor) and Canonical correlation forest (CCF) were applied to produce thematic maps. In order to analyze the effects of the spectral bands of the Sentinel-2A image on the object-based classification performance, three datasets consisting of different spectral band combinations (i.e. four 10 m bands, six 20 m bands and ten 10m pan-sharpened bands) were used. The results showed that the RotFor and CCF classifiers produced superior classification performances compared to the AdaB and RF classifiers for the band combinations regarded in this study. Moreover, it was found that determination of poplar tree class level accuracy reached to ~94% in terms of F-score. It was also observed that the inclusion of the six spectral bands at 20 m resolution resulted in a noteworthy increase in classification accuracy (up to 6%) compared to single 10m band combination. Numéro de notice : A2020-420 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jogs-2020-0003 date de publication en ligne : 04/05/2020 En ligne : https://doi.org/10.1515/jogs-2020-0003 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95477 [article]

WFS 3.0 dans les starting blocks / Anonyme in Géomatique expert, n° 128 (juin - juillet 2019)

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Interpreting effects of multiple, large-scale disturbances using national forest inventory data: A case study of standing dead trees in east Texas, USA / Christopher B. Edgar in Forest ecology and management, vol 437 (1 April 2019)  

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Un bilan des modalités d’évaluation de l’état de conservation des habitats forestiers dans 399 sites Natura 2000 / Damien Marage in Revue forestière française [en ligne], Vol 71 n° 2 (2019)  

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Challenging deep image descriptors for retrieval in heterogeneous iconographic collections / Dimitri Gominski (2019)  

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Foundations of deep convolutional models through kernel methods / Alberto Bietti (2019)  

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Tile & merge: Distributed Delaunay triangulations for cloud computing / Laurent Caraffa (2019)  

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Zoome encore un peu … Une interface de saisie de données géographiques qui permet d’être au bon niveau de détail / Guillaume Touya (2019)  

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Historical collaborative geocoding / Rémi Cura in ISPRS International journal of geo-information, vol 7 n° 7 (July 2018)    

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Band subset selection for anomaly detection in hyperspectral imagery / Lin Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 9 (September 2017)  

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Enseignements du test utilisateur d'une géovisualisation dynamique : des améliorations possibles pour les expériences en cartographie / Cécile Saint-Marc in Cartes & Géomatique, n° 233 (septembre - novembre 2017) 

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