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Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification / Ming Cong in Geocarto international, vol 36 n° 18 ([01/10/2021])
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[article]
Titre : Unsupervised self-adaptive deep learning classification network based on the optic nerve microsaccade mechanism for unmanned aerial vehicle remote sensing image classification Type de document : Article/Communication Auteurs : Ming Cong, Auteur ; Zhiye Wang, Auteur ; Yiting Tao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2065 - 2084 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] chromatopsie
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] compréhension de l'image
[Termes IGN] échantillonnage d'image
[Termes IGN] filtrage numérique d'image
[Termes IGN] image captée par drone
[Termes IGN] vision
[Termes IGN] vision par ordinateurRésumé : (auteur) Unmanned aerial vehicle remote sensing images need to be precisely and efficiently classified. However, complex ground scenes produced by ultra-high ground resolution, data uniqueness caused by multi-perspective observations, and need for manual labelling make it difficult for current popular deep learning networks to obtain reliable references from heterogeneous samples. To address these problems, this paper proposes an optic nerve microsaccade (ONMS) classification network, developed based on multiple dilated convolution. ONMS first applies a Laplacian of Gaussian filter to find typical features of ground objects and establishes class labels using adaptive clustering. Then, using an image pyramid, multi-scale image data are mapped to the class labels adaptively to generate homologous reliable samples. Finally, an end-to-end multi-scale neural network is applied for classification. Experimental results show that ONMS significantly reduces sample labelling costs while retaining high cognitive performance, classification accuracy, and noise resistance—indicating that it has significant application advantages. Numéro de notice : A2021-707 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/10106049.2019.1687593 Date de publication en ligne : 07/11/2019 En ligne : https://doi.org/10.1080/10106049.2019.1687593 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98602
in Geocarto international > vol 36 n° 18 [01/10/2021] . - pp 2065 - 2084[article]Spatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland / Eva Bianchi in Forest ecology and management, vol 494 (August-15 2021)
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[article]
Titre : Spatial patterns of living and dead small trees in subalpine Norway spruce forest reserves in Switzerland Type de document : Article/Communication Auteurs : Eva Bianchi, Auteur ; Harald Bugmann, Auteur ; Martina Lena Hobi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 119315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de groupement
[Termes IGN] distance
[Termes IGN] espace topologique
[Termes IGN] fonction K de Ripley
[Termes IGN] forêt alpestre
[Termes IGN] forêt subalpine
[Termes IGN] mortalité
[Termes IGN] Picea abies
[Termes IGN] régénération (sylviculture)
[Termes IGN] réserve forestière
[Termes IGN] Suisse
[Termes IGN] topographie locale
[Termes IGN] voisinage (relation topologique)
[Vedettes matières IGN] Ecologie forestièreRésumé : (auteur) Spatial patterns can reveal a lot about ecological processes, but our knowledge of the spatial ecology of tree regeneration at a fine scale is quite limited. Therefore, we studied the spatial patterns of living and dead small trees in two subalpine Norway spruce forest reserves in Switzerland (Scatlè and Bödmerenwald) using three types of analyses. First, we investigated the distances of small trees to the nearest large neighboring tree and, by using maximum distances as indicator, inferred the size of forest gaps, detecting mainly forest gaps of small size, although with two exceptions that were driven by large-scale disturbances. Second, we accounted for spatial inhomogeneity in the pattern of small and large trees (i.e., variations in local tree densities) by including environmental covariates in point pattern models. Latitude (within the forest reserve), elevation and aspect contributed significantly to explaining the density of living and dead small trees, and partly of living and dead large trees. Yet, the influence of these environmental covariates varied between the two reserves due to their different topography and peculiar site conditions. Third, we analyzed neighborhood interactions between small and large trees based on the vicinity and size of trees. In both forest reserves, small living trees were randomly dispersed around large dead trees over a broad range of distances and, at certain distances in one reserve, even dispersed away from them. Small living trees further showed clustering around large living trees at short distances and dispersion at large distances. Small dead trees featured mainly a random pattern, although with a tendency to cluster around large neighbors at short distances, irrespective whether these were living or dead. Yet, the weakening of clustering with increasing distances indicates that the influence of large trees on small trees varies with spatial scale and thus that these neighborhood interactions are scale-dependent. Overall, our study contributes to a better understanding of the spatial ecology of mortality in small trees and ultimately of tree regeneration processes and stand dynamics in mountain forests. Numéro de notice : A2021-583 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.foreco.2021.119315 Date de publication en ligne : 11/05/2021 En ligne : https://doi.org/10.1016/j.foreco.2021.119315 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98198
in Forest ecology and management > vol 494 (August-15 2021) . - n° 119315[article]Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification / Divyesh Varade in Geocarto international, vol 36 n° 15 ([15/08/2021])
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[article]
Titre : Unsupervised band selection of hyperspectral data based on mutual information derived from weighted cluster entropy for snow classification Type de document : Article/Communication Auteurs : Divyesh Varade, Auteur ; Ajay K. Maurya, Auteur ; Onkar Dikshit, Auteur Année de publication : 2021 Article en page(s) : pp 1709 - 1731 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] bande spectrale
[Termes IGN] classification floue
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par nuées dynamiques
[Termes IGN] distribution spatiale
[Termes IGN] entropie
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] Inde
[Termes IGN] manteau neigeux
[Termes IGN] neige
[Termes IGN] réflectance spectraleRésumé : (auteur) Information on the spatial and temporal extent of snow cover distribution is a significant input in hydrological processes and climate models. Although hyperspectral remote sensing provides significant opportunities in the assessment of land cover, the applications of such data are limited in the snow-covered alpine regions. A major issue with hyperspectral data is the larger dimensionality. Feature selection methods are often used to derive the most informative subset of bands from the hyperspectral data. In this study, a band selection technique is proposed which utilizes the mutual information (MI) between hyperspectral bands and a reference band. The first principal component of the hyperspectral data is selected as the reference band. Two variants of this approach are proposed involving preclustering of bands using: (1) the k-means and (2) the fuzzy k-means algorithms. The MI is derived from weighted entropy of the hyperspectral band and the reference band. The weights are computed from the cluster distance ratio and the cluster membership function for the k-means and fuzzy k-means algorithm, respectively. The selected bands were classified using random forest classifier. The proposed methods are evaluated with four datasets, two Hyperion datasets corresponding to the geographical locations of Dhundi and Solang in India, corresponding to snow covered terrain and two benchmark AVIRIS datasets of Indian Pines and Salinas. The average classification accuracy (0.995 and 0.721 for Dhundi and Solang datasets, respectively) for the proposed approach were observed to be better as compared with those from other state of the art techniques. Numéro de notice : A2021-568 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1665717 Date de publication en ligne : 18/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1665717 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98183
in Geocarto international > vol 36 n° 15 [15/08/2021] . - pp 1709 - 1731[article]Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)
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Titre : Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Weiguo Zhang, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7038 - 7050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] bois
[Termes IGN] densité du feuillage
[Termes IGN] données lidar
[Termes IGN] données TLS (télémétrie)
[Termes IGN] feuille (végétation)
[Termes IGN] réflectance spectrale
[Termes IGN] semis de pointsRésumé : (auteur) Terrestrial laser scanning (TLS) is a highly effective and noninvasive technology for retrieving the structural and biophysical attributes of trees using 3-D high-accuracy and high-density point clouds. The separation of leaf and wood points in TLS data is a prerequisite for the accurate and reliable derivation of these attributes. In this study, a new method is proposed to separate the leaf and wood points of individual trees by combining the TLS radiometric (intensity) and geometric (density) data. The leaf points are separated from the wood ones through three steps. First, the corrected intensity data are used to separate a part of the leaf points preliminarily given the differences in reflectance characteristics. Second, the density data are adopted for the further separation of another part of the leaf points because the density of the remaining leaf points is smaller than that of the wood points. Finally, a connectivity clustering algorithm is conducted to form several clusters with different sizes (points) and the remaining leaf points are separated in accordance with the cluster sizes. Eight different trees are selected to evaluate the performance of the proposed method. The averaged overall accuracy and kappa coefficient of the eight trees are approximately 95% and 0.81, respectively. The results suggest that the combination of TLS intensity and density data can perform a superior separation of leaf and wood points in terms of efficiency and accuracy, and the proposed separation method can be accurately and robustly used for various trees with different species, sizes, and structures. Numéro de notice : A2021-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032167 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98295
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 7038 - 7050[article]Towards efficient indoor/outdoor registration using planar polygons / Rahima Djahel in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2021 (July 2021)
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[article]
Titre : Towards efficient indoor/outdoor registration using planar polygons Type de document : Article/Communication Auteurs : Rahima Djahel, Auteur ; Bruno Vallet , Auteur ; Pascal Monasse, Auteur
Année de publication : 2021 Projets : BIOM / Vallet, Bruno Article en page(s) : pp 51 - 58 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de groupement
[Termes IGN] appariement de primitives
[Termes IGN] bati
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de points
[Termes IGN] géométrie euclidienne
[Termes IGN] polygone
[Termes IGN] scène intérieure
[Termes IGN] scène urbaine
[Termes IGN] superposition de donnéesRésumé : (auteur) The registration of indoor and outdoor scans with a precision reaching the level of geometric noise represents a major challenge for Indoor/Outdoor building modeling. The basic idea of the contribution presented in this paper consists in extracting planar polygons from indoor and outdoor LiDAR scans, and then matching them. In order to cope with the very small overlap between indoor and outdoor scans of the same building, we propose to start by extracting points lying in the buildings’ interior from the outdoor scans as points where the laser ray crosses detected façades. Since, within a building environment, most of the objects are bounded by a planar surface, we propose a new registration algorithm that matches planar polygons by clustering polygons according to their normal direction, then by their offset in the normal direction. We use this clustering to find possible polygon correspondences (hypotheses) and estimate the optimal transformation for each hypothesis. Finally, a quality criteria is computed for each hypothesis in order to select the best one. To demonstrate the accuracy of our algorithm, we tested it on real data with a static indoor acquisition and a dynamic (Mobile Laser Scanning) outdoor acquisition. Numéro de notice : A2021-490 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2021-51-2021 Date de publication en ligne : 17/06/2021 En ligne : http://dx.doi.org/10.5194/isprs-annals-V-2-2021-51-2021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97955
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2021 (July 2021) . - pp 51 - 58[article]Individual tree identification using a new cluster-based approach with discrete-return airborne LiDAR data / Haijian Liu in Remote sensing of environment, vol 258 (June 2021)
PermalinkProvisioning forest and conservation science with high-resolution maps of potential distribution of major European tree species under climate change / Debojyoti Chakraborty in Annals of Forest Science, vol 78 n° 2 (June 2021)
PermalinkA BiLSTM-CNN model for predicting users’ next locations based on geotagged social media / Yi Bao in International journal of geographical information science IJGIS, vol 35 n° 4 (April 2021)
PermalinkGeovisualization of COVID-19: State of the art and opportunities / Yu Lan in Cartographica, vol 56 n° 1 (Spring 2021)
PermalinkHyperspectral image denoising via clustering-based latent variable in variational Bayesian framework / Peyman Azimpour in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)
PermalinkUtilizing urban geospatial data to understand heritage attractiveness in Amsterdam / Sevim Sezi Karayazi in ISPRS International journal of geo-information, vol 10 n° 4 (April 2021)
PermalinkRecognition of varying size scene images using semantic analysis of deep activation maps / Shikha Gupta in Machine Vision and Applications, vol 32 n° 2 (March 2021)
PermalinkA heuristic approach to the generalization of complex building groups in urban villages / Wenhao Yu in Geocarto international, vol 36 n° 2 ([01/02/2021])
PermalinkIdentifying urban growth patterns through land-use/land-cover spatio-temporal metrics: Simulation and analysis / Marta Sapena Moll in International journal of geographical information science IJGIS, vol 35 n° 2 (February 2021)
PermalinkTopoclimatic zoning of continental Chile / Donna Cortez in Journal of maps, vol 17 n° 2 (February 2021)
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