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Unsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
[article]
Titre : Unsupervised deep joint segmentation of multitemporal high-resolution images Type de document : Article/Communication Auteurs : Sudipan Saha, Auteur ; Lichao Mou, Auteur ; Chunping Qiu, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8780 - 8792 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse d'image orientée objet
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de données
[Termes IGN] image à haute résolution
[Termes IGN] image à très haute résolution
[Termes IGN] image multitemporelle
[Termes IGN] itération
[Termes IGN] segmentation sémantiqueRésumé : (auteur) High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth’s surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento. Numéro de notice : A2020-744 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2990640 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2990640 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96375
in IEEE Transactions on geoscience and remote sensing > Vol 58 n° 12 (December 2020) . - pp 8780 - 8792[article]Acquisition of weak GPS signals using wavelet-based de-noising methods / Mohaddeseh Sharie in Survey review, vol 52 n° 375 (November 2020)
[article]
Titre : Acquisition of weak GPS signals using wavelet-based de-noising methods Type de document : Article/Communication Auteurs : Mohaddeseh Sharie, Auteur ; Mohammad-Reza Mosavi, Auteur ; Narjes Rahemi, Auteur Année de publication : 2020 Article en page(s) : pp 497 - 513 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] atténuation du signal
[Termes IGN] filtrage du bruit
[Termes IGN] rapport signal sur bruit
[Termes IGN] récepteur GPS
[Termes IGN] seuillage
[Termes IGN] signal GPS
[Termes IGN] sous ensemble flou
[Termes IGN] transformation en ondelettesRésumé : (auteur) Various factors cause GPS signals to be weakened in their path from satellites to receiver. This phenomenon leads to some problems in the normal stages of the positioning process, including acquisition stage. The wavelet transform is one of the tools that have been employed in several methods to improve receiver’s sensitivity in confronting weak GPS signals. Choosing an appropriate threshold plays a key role in the performance of this de-noising method. This study presents two methods for selecting an appropriate threshold. In the first method, the threshold is determined based on the statistical information extracted from the signal. On the other hand, the threshold in the second method is determined by type II fuzzy sets. The results show that employment of these two methods increases SNR gain as well as the range of acquirable SNR which makes signals acquisition with minimum post-correlation SNR of −6.5dB in a GPS receiver possible. Numéro de notice : A2020-687 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1648718 Date de publication en ligne : 01/08/2020 En ligne : https://doi.org/10.1080/00396265.2019.1648718 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96220
in Survey review > vol 52 n° 375 (November 2020) . - pp 497 - 513[article]Active and incremental learning for semantic ALS point cloud segmentation / Yaping Lin in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)
[article]
Titre : Active and incremental learning for semantic ALS point cloud segmentation Type de document : Article/Communication Auteurs : Yaping Lin, Auteur ; M. George Vosselman, Auteur ; Yanpeng Cao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 73 - 92 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] entropie
[Termes IGN] incertitude des données
[Termes IGN] itération
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (auteur) Supervised training of a deep neural network for semantic segmentation of point clouds requires a large amount of labelled data. Nowadays, it is easy to acquire a huge number of points with high density in large-scale areas using current LiDAR and photogrammetric techniques. However it is extremely time-consuming to manually label point clouds for model training. In this paper, we propose an active and incremental learning strategy to iteratively query informative point cloud data for manual annotation and the model is continuously trained to adapt to the newly labelled samples in each iteration. We evaluate the data informativeness step by step and effectively and incrementally enrich the model knowledge. The data informativeness is estimated by two data dependent uncertainty metrics (point entropy and segment entropy) and one model dependent metric (mutual information). The proposed methods are tested on two datasets. The results indicate the proposed uncertainty metrics can enrich current model knowledge by selecting informative samples, such as considering points with difficult class labels and choosing target objects with various geometries in the labelled training pool. Compared to random selection, our metrics provide valuable information to significantly reduce the labelled training samples. In contrast with training from scratch, the incremental fine-tuning strategy significantly save the training time. Numéro de notice : A2020-638 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.09.003 Date de publication en ligne : 14/09/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.09.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96061
in ISPRS Journal of photogrammetry and remote sensing > vol 169 (November 2020) . - pp 73 - 92[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020111 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020113 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020112 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Evaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria / Johannes Scholz in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)
[article]
Titre : Evaluating geo-tagged Twitter data to analyze tourist flows in Styria, Austria Type de document : Article/Communication Auteurs : Johannes Scholz, Auteur ; Janja Jeznik, Auteur Année de publication : 2020 Article en page(s) : n° 681 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] Autriche
[Termes IGN] coefficient de corrélation
[Termes IGN] contenu généré par les utilisateurs
[Termes IGN] distribution spatiale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] segmentation sémantique
[Termes IGN] tourisme
[Termes IGN] TwitterRésumé : (auteur) The research focuses on detecting tourist flows in the Province of Styria in Austria based on crowdsourced data. Twitter data were collected in the time range from 2008 until August 2018. Extracted tweets were submitted to an extensive filtering process within non-relational database MongoDB. Hotspot Analysis and Kernel Density Estimation methods were applied, to investigate spatial distribution of tourism relevant tweets under temporal variations. Furthermore, employing the VADER method an integrated semantic analysis provides sentiments of extracted tweets. Spatial analyses showed that detected Hotspots correspond to typical Styrian touristic areas. Apart from mainly successful sentiment analysis, it pointed out also a problematic aspect of working with multilingual data. For evaluation purposes, the official tourism data from the Province of Styria and federal Statistical Office of Austria played a role of ground truth data. An evaluation with Pearson’s correlation coefficient was employed, which proves a statistically significant correlation between Twitter data and reference data. In particular, the paper shows that crowdsourced data on a regional level can serve as accurate indicator for the behaviour and movement of users. Numéro de notice : A2020-731 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi9110681 Date de publication en ligne : 15/11/2020 En ligne : https://doi.org/10.3390/ijgi9110681 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96344
in ISPRS International journal of geo-information > vol 9 n° 11 (November 2020) . - n° 681[article]High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network / Fengpeng Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : High-resolution remote sensing image scene classification via key filter bank based on convolutional neural network Type de document : Article/Communication Auteurs : Fengpeng Li, Auteur ; Ruyi Feng, Auteur ; Wei Han, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 8077 - 8092 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] image à haute résolution
[Termes IGN] jeu de données
[Termes IGN] segmentation sémantique
[Termes IGN] test statistiqueRésumé : (auteur) High-resolution remote sensing (HRRS) image scene classification has attracted an enormous amount of attention due to its wide application in a range of tasks. Due to the rapid development of deep learning (DL), models based on convolutional neural network (CNN) have made competitive achievements on HRRS image scene classification because of the excellent representation capacity of DL. The scene labels of HRRS images extremely depend on the combination of global information and information from key regions or locations. However, most existing models based on CNN tend only to represent the global features of images or overstate local information capturing from key regions or locations, which may confuse different categories. To address this issue, a key region or location capturing method called key filter bank (KFB) is proposed in this article, and KFB can retain global information at the same time. This method can combine with different CNN models to improve the performance of HRRS imagery scene classification. Moreover, for the convenience of practical tasks, an end-to-end model called KFBNet where KFB combined with DenseNet-121 is proposed to compare the performance with existing models. This model is evaluated on public benchmark data sets, and the proposed model makes better performance on benchmarks than the state-of-the-art methods. Numéro de notice : A2020-683 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2987060 Date de publication en ligne : 23/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2987060 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96208
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8077 - 8092[article]Indoor point cloud segmentation using iterative Gaussian mapping and improved model fitting / Bufan Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkRiver ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)PermalinkStreets of London: Using Flickr and OpenStreetMap to build an interactive image of the city / Azam Raha Bahrehdar in Computers, Environment and Urban Systems, vol 84 (November 2020)PermalinkChoosing an appropriate training set size when using existing data to train neural networks for land cover segmentation / Huan Ning in Annals of GIS, vol 26 n° 4 (October 2020)PermalinkExploring multiscale object-based convolutional neural network (multi-OCNN) for remote sensing image classification at high spatial resolution / Vitor Martins in ISPRS Journal of photogrammetry and remote sensing, vol 168 (October 2020)PermalinkHierarchical instance recognition of individual roadside trees in environmentally complex urban areas from UAV laser scanning point clouds / Yongjun Wang in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)PermalinkHomogeneous tree height derivation from tree crown delineation using Seeded Region Growing (SRG) segmentation / Muhamad Farid Ramli in Geo-spatial Information Science, vol 23 n° 3 (September 2020)PermalinkA novel deep learning instance segmentation model for automated marine oil spill detection / Shamsudeen Temitope Yekeen in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkRelevé 3D et classification de nuages de points de patrimoine bâti / Arnadi Murtiyoso in XYZ, n° 164 (septembre 2020)PermalinkSemi-automated framework for generating cycling lane centerlines on roads with roadside barriers from noisy MLS data / Yang Ma in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)Permalink