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Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree / Shuang Wang in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8583 - 8597 Titre : Semi-supervised PolSAR image classification based on improved tri-training with a minimum spanning tree Type de document : Article/Communication Auteurs : Shuang Wang, Auteur ; Yanhe Guo, Auteur ; Wenqiang Hua, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8583 - 8597 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] échantillon [Termes descripteurs IGN] étiquette [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] voisinage (topologie) Résumé : (auteur) n this article, the terrain classifications of polarimetric synthetic aperture radar (PolSAR) images are studied. A novel semi-supervised method based on improved Tri-training combined with a neighborhood minimum spanning tree (NMST) is proposed. Several strategies are included in the method: 1) a high-dimensional vector of polarimetric features that are obtained from the coherency matrix and diverse target decompositions is constructed; 2) this vector is divided into three subvectors and each subvector consists of one-third of the polarimetric features, randomly selected. The three subvectors are used to separately train the three different base classifiers in the Tri-training algorithm to increase the diversity of classification; and 3) a help-training sample selection with the improved NMST that uses both the coherency matrix and the spatial information is adopted to select highly reliable unlabeled samples to increase the training sets. Thus, the proposed method can effectively take advantage of unlabeled samples to improve the classification. Experimental results show that with a small number of labeled samples, the proposed method achieves a much better performance than existing classification methods. Numéro de notice : A2020-743 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2988982 date de publication en ligne : 14/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2988982 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96374 [article]

A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification / Jing Lv in Geoinformatica [en ligne], vol 24 n° 4 (October 2020)  

Public [article]  inGeoinformatica [en ligne] > vol 24 n° 4 (October 2020) . - pp 827 - 848 Titre : A novel spectral–spatial based adaptive minimum spanning forest for hyperspectral image classification Type de document : Article/Communication Auteurs : Jing Lv, Auteur ; Huimin Zhang, Auteur ; Ming Yang, Auteur ; Wanqi Yang, Auteur Année de publication : 2020 Article en page(s) : pp 827 - 848 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] segmentation d'image Résumé : (Auteur) The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps. Numéro de notice : A2020-496 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10707-020-00403-0 date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1007/s10707-020-00403-0 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96117 [article]

Chloroplast haplotypes of Northern red oak (Quercus rubra L.) stands in Germany suggest their origin from Northeastern Canada / Jeremias Götz in Forests, vol 11 n° 9 (September 2020)  

Public [article]  inForests > vol 11 n° 9 (September 2020) . - n° 1025 Titre : Chloroplast haplotypes of Northern red oak (Quercus rubra L.) stands in Germany suggest their origin from Northeastern Canada Type de document : Article/Communication Auteurs : Jeremias Götz, Auteur ; Konstantin V. Krutovsky, Auteur ; Ludger Leinemann, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 1025 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Allemagne [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] Canada [Termes descripteurs IGN] croissance végétale [Termes descripteurs IGN] gestion forestière durable [Termes descripteurs IGN] quercus rubra [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Northern red oak (Quercus rubra L.) is one of the most important foreign tree species in Germany and considered as a major candidate for prospective sustainable forestry in the face of climate change. Therefore, Q. rubra was subject of many previous studies on its growth traits and attempts to infer the origin of various populations of this species using nuclear and chloroplast DNA markers. However, the exact geographic origin of German red oak stands has still not been identified. Its native range widely extends over North America, and the species can tolerate a broad range of environmental conditions. We genotyped individual trees in 85 populations distributed in Germany and North America using five chloroplast microsatellite and three novel chloroplast CAPS markers, resulting in the identification of 29 haplotypes. The new marker set enabled the identification of several new red oak haplotypes with restricted geographic origin. Some very rare haplotypes helped us narrow down the origin of Q. rubra stands in Germany, especially some stands from North Rhine-Westphalia, to the northern part of the species’ natural distribution area including the Peninsula of Nova Scotia, where the most similar haplotype composition was observed, compared to distinct German stands. Numéro de notice : A2020-751 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f11091025 date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.3390/f11091025 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96427 [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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On the spatial distribution of buildings for map generalization / Zhiwei Wei in Cartography and Geographic Information Science, Vol 45 n° 6 (November 2018)  

Public [article]  inCartography and Geographic Information Science > Vol 45 n° 6 (November 2018) . - pp 539 - 555 Titre : On the spatial distribution of buildings for map generalization Type de document : Article/Communication Auteurs : Zhiwei Wei, Auteur ; Qingsheng Guo, Auteur ; Lin Wang, Auteur ; Fen Yan, Auteur Année de publication : 2018 Article en page(s) : pp 539 - 555 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse en composantes principales [Termes descripteurs IGN] arbre aléatoire minimum [Termes descripteurs IGN] bati [Termes descripteurs IGN] distribution spatiale [Termes descripteurs IGN] données localisées des bénévoles [Termes descripteurs IGN] généralisation cartographique automatisée [Termes descripteurs IGN] OpenStreetMap [Vedettes matières IGN] Généralisation Résumé : (Auteur) Information on spatial distribution of buildings must be explored as part of the process of map generalization. A new approach is proposed in this article, which combines building classification and clustering to enable the detection of class differences within a pattern, as well as patterns within a class. To do this, an analysis of existing parameters describing building characteristics is performed via principal component analysis (PCA), and four major parameters (i.e. convex hull area, IPQ compactness, number of edges, and smallest minimum bounding rectangle orientation) are selected for further classification based on similarities between building characteristics. A building clustering method based on minimum spanning tree (MST) considering rivers and roads is then applied. Theory and experiments show that use of a relative neighbor graph (RNG) is more effective in detecting linear building patterns than either a nearest neighbor graph (NNG), an MST, or a Gabriel graph (GssG). Building classification and clustering are therefore conducted separately using experimental data extracted from OpenStreetMap (OSM), and linear patterns are then recognized within resultant clusters. Experimental results show that the approach proposed in this article is both reasonable and efficient for mining information on the spatial distribution of buildings for map generalization. Numéro de notice : A2018-480 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/15230406.2018.1433068 date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/15230406.2018.1433068 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91258 [article]

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Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)  

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Built-up area analysis / Nicolas Regnauld (1995)

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