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Understanding 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)
[article]
Titre : Understanding the synergies of deep learning and data fusion of multispectral and panchromatic high resolution commercial satellite imagery for automated ice-wedge polygon detection Type de document : Article/Communication Auteurs : Chandi Witharana, Auteur ; Md Abul Ehsan Bhuiyan, Auteur ; Anna K. Liljedahl, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 174-191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] apprentissage profond
[Termes IGN] Arctique
[Termes IGN] artefact
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] fusion d'images
[Termes IGN] glace
[Termes IGN] image à haute résolution
[Termes IGN] pergélisol
[Termes IGN] texture d'imageRésumé : (Auteur) The utility of sheer volumes of very high spatial resolution (VHSR) commercial imagery in mapping the Arctic region is new and actively evolving. Commercial satellite sensors typically record image data in low-resolution multispectral (MS) and high-resolution panchromatic (PAN) mode. Spatial resolution is needed to accurately describe feature shapes and textural patterns, such as ice-wedge polygons (IWPs) that are rapidly transforming surface features due to degrading permafrost, while spectral resolution allows capturing of land-use and land-cover types. Data fusion, the process of combining PAN and MS images with complementary characteristics often serves as an integral component of remote sensing mapping workflows. The fusion process generates spectral and spatial artifacts that may affect the classification accuracies of subsequent automated image analysis algorithms, such as deep learning (DL) convolutional neural nets (CNN). We employed a detailed multidimensional assessment to understand the performances of an array of eight application-oriented data fusion algorithms when applied to VHSR image scenes for DLCNN-based mapping of ice-wedge polygons. Our findings revealed the scene dependency of data fusion algorithms and emphasized the need for careful selection of the proper algorithm. Results suggested that the fusion algorithms that preserve spatial character of original PAN imagery favor the DLCNN model performances. The choice of fusion approach needs to be considered of equal importance to the required training dataset for successful applications using DLCNN on VHRS imagery in order to enable an accurate mapping effort of permafrost thaw across the Arctic region. Numéro de notice : A2020-705 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.10.010 Date de publication en ligne : 01/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.10.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96232
in ISPRS Journal of photogrammetry and remote sensing > vol 170 (December 2020) . - pp 174-191[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020121 RAB Revue Centre de documentation En réserve L003 Disponible 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]Bayesian-deep-learning estimation of earthquake location from single-station observations / S. Mostafa Mousavi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : Bayesian-deep-learning estimation of earthquake location from single-station observations Type de document : Article/Communication Auteurs : S. Mostafa Mousavi, Auteur ; Gregory C. Beroza, Auteur Année de publication : 2020 Article en page(s) : pp 8211 - 8224 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal
[Termes IGN] apprentissage profond
[Termes IGN] classification bayesienne
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection du signal
[Termes IGN] épicentre
[Termes IGN] estimation bayesienne
[Termes IGN] onde sismique
[Termes IGN] régression
[Termes IGN] séisme
[Termes IGN] station d'observation
[Termes IGN] surveillance géologique
[Termes IGN] temps de propagationRésumé : (auteur) We present a deep-learning method for a single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multitask temporal convolutional neural network to learn epicentral distance and P travel time from 1-min seismograms. The network estimates epicentral distance and P travel time with mean errors of 0.23 km and 0.03 s and standard deviations of 5.42 km and 0.66 s, respectively, along with their epistemic and aleatory uncertainties. We design a separate multi-input network using standard convolutional layers to estimate the back-azimuth angle and its epistemic uncertainty. This network estimates the direction from which seismic waves arrive at the station with a mean error of 1°. Using this information, we estimate the epicenter, origin time, and depth along with their confidence intervals. We use a global data set of earthquake signals recorded within 1° (~112 km) from the event to build the model and demonstrate its performance. Our model can predict epicenter, origin time, and depth with mean errors of 7.3 km, 0.4 s, and 6.7 km, respectively, at different locations around the world. Our approach can be used for fast earthquake source characterization with a limited number of observations and also for estimating the location of earthquakes that are sparsely recorded—either because they are small or because stations are widely separated. Numéro de notice : A2020-684 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2988770 Date de publication en ligne : 06/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2988770 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96209
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 8211 - 8224[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]River ice segmentation with deep learning / Abhineet Singh in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)
[article]
Titre : River ice segmentation with deep learning Type de document : Article/Communication Auteurs : Abhineet Singh, Auteur ; Hayden Kalke, Auteur ; Mark Loewen, Auteur Année de publication : 2020 Article en page(s) : pp 7570 - 7579 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] Canada
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] glace
[Termes IGN] image captée par drone
[Termes IGN] rivière
[Termes IGN] segmentation d'image
[Termes IGN] segmentation sémantiqueRésumé : (auteur) This article deals with the problem of computing surface concentrations for two types of river ice from digital images acquired during freeze-up. It presents the results of attempting to solve this problem using several state-of-the-art semantic segmentation methods based on deep convolutional neural networks (CNNs). This task presents two main challenges—very limited availability of labeled training data and presence of noisy labels due to the great difficulty of visually distinguishing between the two types of ice, even for human experts. The results are used to analyze the extent to which some of the best deep learning methods currently in existence can handle these challenges. The code and data used in the experiments are made publicly available to facilitate further work in this domain. Numéro de notice : A2020-674 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2981082 Date de publication en ligne : 13/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2981082 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96165
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7570 - 7579[article]3D hand mesh reconstruction from a monocular RGB image / Hao Peng in The Visual Computer, vol 36 n° 10 - 12 (October 2020)PermalinkApplication of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 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)PermalinkA graph convolutional network model for evaluating potential congestion spots based on local urban built environments / Kun Qin in Transactions in GIS, Vol 24 n° 5 (October 2020)PermalinkA low-cost integrated MEMS-based INS/GPS vehicle navigation system with challenging conditions based on an optimized IT2FNN in occluded environments / Elahe S. Abdolkarimi in GPS solutions, Vol 24 n° 4 (October 2020)PermalinkCrater detection and registration of planetary images through marked point processes, multiscale decomposition, and region-based analysis / David Solarna in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkCSVM architectures for pixel-wise object detection in high-resolution remote sensing images / Youyou Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)PermalinkMultiscale supervised kernel dictionary learning for SAR target recognition / Lei Tao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (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)PermalinkA novel deep network and aggregation model for saliency detection / Ye Liang in The Visual Computer, vol 36 n° 9 (September 2020)PermalinkRecognition of building group patterns using graph convolutional network / Rong Zhao in Cartography and Geographic Information Science, Vol 47 n° 5 (September 2020)PermalinkUsing OpenStreetMap data and machine learning to generate socio-economic indicators / Daniel Feldmeyer in ISPRS International journal of geo-information, vol 9 n° 9 (September 2020)PermalinkVehicle detection of multi-source remote sensing data using active fine-tuning network / Xin Wu in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkX-ModalNet: A semi-supervised deep cross-modal network for classification of remote sensing data / Danfeng Hong in ISPRS Journal of photogrammetry and remote sensing, vol 167 (September 2020)PermalinkCNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)PermalinkLanduse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])PermalinkClassification of hyperspectral and LiDAR data using coupled CNNs / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkClassification of sea ice types in Sentinel-1 SAR data using convolutional neural networks / Hugo Boulze in Remote sensing, vol 12 n° 13 (July-1 2020)PermalinkEvaluating techniques for mapping island vegetation from unmanned aerial vehicle (UAV) images: Pixel classification, visual interpretation and machine learning approaches / S.M. 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Arundel in Transactions in GIS, Vol 24 n° 3 (June 2020)PermalinkA hybrid deep learning–based model for automatic car extraction from high-resolution airborne imagery / Mehdi Khoshboresh Masouleh in Applied geomatics, vol 12 n° 2 (June 2020)PermalinkAssessing alternative methods for unsupervised segmentation of urban vegetation in very high-resolution multispectral aerial imagery / Allison Lassiter in Plos one, vol 15 n° 5 (May 2020)PermalinkAutomatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks / Mahmoud Saeedimoghaddam in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)PermalinkA convolutional neural network with mapping layers for hyperspectral image classification / Rui Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)PermalinkDeep learning for enrichment of vector spatial databases: Application to highway interchange / Guillaume Touya in ACM 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2020)PermalinkDirectionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkGeocoding of trees from street addresses and street-level images / Daniel Laumer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkMultichannel Pulse-Coupled Neural Network-Based Hyperspectral Image Visualization / Puhong Duan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkOnline flu epidemiological deep modeling on disease contact network / Liang Zhao in Geoinformatica, vol 24 n° 2 (April 2020)PermalinkA Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)PermalinkStreet-Frontage-Net: urban image classification using deep convolutional neural networks / Stephen Law in International journal of 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