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DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn / Roberto Interdonato in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
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Titre : DuPLO: A DUal view Point deep Learning architecture for time series classificatiOn Type de document : Article/Communication Auteurs : Roberto Interdonato, Auteur ; Dino Ienco, Auteur ; Raffaele Gaetano, Auteur ; Kenji Ose, Auteur Année de publication : 2019 Article en page(s) : pp 91 - 104 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image Sentinel-MSI
[Termes IGN] occupation du sol
[Termes IGN] réseau neuronal convolutif
[Termes IGN] série temporelleRésumé : (Auteur) Nowadays, modern Earth Observation systems continuously generate huge amounts of data. A notable example is represented by the Sentinel-2 mission, which provides images at high spatial resolution (up to 10 m) with high temporal revisit period (every 5 days), which can be organized in Satellite Image Time Series (SITS). While the use of SITS has been proved to be beneficial in the context of Land Use/Land Cover (LULC) map generation, unfortunately, most of machine learning approaches commonly leveraged in remote sensing field fail to take advantage of spatio-temporal dependencies present in such data. Recently, new generation deep learning methods allowed to significantly advance research in this field. These approaches have generally focused on a single type of neural network, i.e., Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), which model different but complementary information: spatial autocorrelation (CNNs) and temporal dependencies (RNNs). In this work, we propose the first deep learning architecture for the analysis of SITS data, namely DuPLO (DUal view Point deep Learning architecture for time series classificatiOn), that combines Convolutional and Recurrent neural networks to exploit their complementarity. Our hypothesis is that, since CNNs and RNNs capture different aspects of the data, a combination of both models would produce a more diverse and complete representation of the information for the underlying land cover classification task. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Gard site in Mainland France and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal. Numéro de notice : A2019-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.011 Date de publication en ligne : 24/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92441
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 91 - 104[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Hyperspectral image classification with squeeze multibias network / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
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Titre : Hyperspectral image classification with squeeze multibias network Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Guangyun Liu, Auteur ; Shutao Li, Auteur ; Pedram Ghamisi, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2019 Article en page(s) : pp 1291 - 1301 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] erreur systématique
[Termes IGN] image hyperspectraleRésumé : (Auteur) A convolutional neural network (CNN) has recently demonstrated its outstanding capability for the classification of hyperspectral images (HSIs). Typical CNN-based methods usually adopt image patches as inputs to the network. However, a fixed-size image patch in HSI with complex spatial contexts may contain multiple ground objects of different classes, which will deteriorate the classification performance of the CNN. In addition, traditional convolutional layers adopted in the CNN have a huge amount of parameters needed to be tuned, which will cause high computational cost. To address the above-mentioned issues, a novel squeeze multibias network (SMBN) is proposed for HSI classification. Specifically, the proposed SMBN first introduces the multibias module (MBM), which incorporates multibias into the rectified linear unit layers. The MBM can decouple the feature maps of input patches into multiple response maps (corresponding to different ground objects) and adaptively select the meaningful maps for classification. Furthermore, the proposed SMBN replaces the traditional convolutional layer with a squeeze convolution module, which can greatly reduce the number of parameters in the network, thus saving the running time, while still maintaining high classification accuracy. Experimental results on three real HSIs demonstrate the superiority of the proposed SMBN method over several state-of-the-art classification approaches. Numéro de notice : A2019-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2865953 Date de publication en ligne : 13/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2865953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92453
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1291 - 1301[article]Land cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms / Dimitri Bulatov in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)
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Titre : Land cover classification in combined elevation and optical images supported by OSM data, mixed-level features, and non-local optimization algorithms Type de document : Article/Communication Auteurs : Dimitri Bulatov, Auteur ; Gisela Häufel, Auteur ; Lucas Lucks, Auteur ; Melanie Pohl, Auteur Année de publication : 2019 Article en page(s) : pp 179 - 195 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données localisées des bénévoles
[Termes IGN] extraction automatique
[Termes IGN] milieu urbain
[Termes IGN] OpenStreetMap
[Termes IGN] orthoimageRésumé : (Auteur) Land cover classification from airborne data is considered a challenging task in Remote Sensing. Even in the case of available elevation data, shadows and strong intra-class variations of appearances are abundant in urban terrain. In this paper, we propose an approach for supervised land cover classification that has three main contributions. Firstly, for the cumbersome task of training data sampling we propose an algorithm which combines the freely available OpenStreetMap data with the actual sensor data and requires only a minimum of user interaction. The key idea of this algorithm is to rasterize the vector data using a fast segmentation result. Secondly, pixel-wise classification may take long and be quite sensitive to the resolution and quality of input data. Therefore, superpixel decomposition of images, supported by a general framework on operations with superpixels, guarantees fast grouping of pixel-wise features and their assignment to one of four important classes (building, tree, grass and road). Particularly for extraction of street canyons lying in the shadowy regions, high-level features based on stripes are introduced. Finally, the output of a probabilistic learning algorithm can be postprocessed by a non-local optimization module operating on Markov Random Fields, thus allowing to correct noisy results using a smoothness prior. Extensive tests on three datasets of quite different nature have been performed with two probabilistic learners: The well-known Random Forest and by far less known Import Vector Machine are explored. Thus, this work provides insights about promising feature sets for both classifiers. The quantitative results for the ISPRS benchmark dataset Vaihingen are promising, achieving up to 94.5% and 87.1% accuracy on superpixel and on pixel level, respectively, despite the fact that only around 10% of available labeled data were used. At the same time, the results for two additional datasets, validated with the autonomously acquired training data, yielded a significantly lower number of misclassified superpixels. This confirms that the proposed algorithm on training data extraction works quite well in reducing errors of second kind. However, it tends to extract predominantly huge and easy-to-classify areas, while in complicated, ambiguous regions, first type errors often occur. For this and other algorithm shortcomings, directions of future research are outlined. Numéro de notice : A2019-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.3.179 Date de publication en ligne : 01/03/2019 En ligne : https://doi.org/10.14358/PERS.85.3.179 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92476
in Photogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 3 (March 2019) . - pp 179 - 195[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2019031 SL Revue Centre de documentation Revues en salle Disponible A novel sharpening approach for superresolving multiresolution optical images / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 57 n° 3 (March 2019)
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Titre : A novel sharpening approach for superresolving multiresolution optical images Type de document : Article/Communication Auteurs : Claudia Paris, Auteur ; José Bioucas-Dias, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2019 Article en page(s) : pp 1545 - 1560 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] filtrage du bruit
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] problème inverseRésumé : (Auteur) This paper aims to provide a compact superresolution formulation specific for multispectral (MS) multiresolution optical data, i.e., images characterized by different scales across different spectral bands. The proposed method, named multiresolution sharpening approach (MuSA), relies on the solution of an optimization problem tailored to the properties of those images. The superresolution problem is formulated as the minimization of an objective function containing a data-fitting term that models the blurs and downsamplings of the different bands and a patch-based regularizer that promotes image self-similarity guided by the geometric details provided by the high-resolution bands. By exploiting the approximately low-rank property of the MS data, the ill-posedness of the inverse problem in hand is strongly reduced, thus sharply improving its conditioning. The state-of-the-art color block-matching and 3D filtering (C-BM3D) image denoiser is used as a patch-based regularizer by leveraging the “plug-and-play” framework: the denoiser is plugged into the iterations of the alternating direction method of multipliers. The main novelties of the proposed method are: 1) the introduction of an observation model tailored to the specific properties of (MS) multiresolution images and 2) the exploitation of the high-spatial-resolution bands to guide the grouping step in the color block-matching and 3D filtering (C-BM3D) denoiser, which constitutes a form of regularization learned from the high-resolution channels. The results obtained on the real and synthetic Sentinel 2 data sets give an evidence of the effectiveness of the proposed approach. Numéro de notice : A2019-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2867284 Date de publication en ligne : 26/09/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2867284 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92458
in IEEE Transactions on geoscience and remote sensing > vol 57 n° 3 (March 2019) . - pp 1545 - 1560[article]Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
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Titre : Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis Type de document : Article/Communication Auteurs : Matheus Pinheiro Ferreira, Auteur ; Fabien Hubert Wagner, Auteur ; Luiz E.O.C. Aragão, Auteur Année de publication : 2019 Article en page(s) : pp 119 - 131 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse texturale
[Termes IGN] arbre (flore)
[Termes IGN] Brésil
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] espèce végétale
[Termes IGN] forêt tropicale
[Termes IGN] houppier
[Termes IGN] image à très haute résolution
[Termes IGN] image infrarouge
[Termes IGN] image Worldview
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] matrice de co-occurrence
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] variation saisonnièreRésumé : (Auteur) Tropical forest conservation and management can significantly benefit from information about the spatial distribution of tree species. Very-high resolution (VHR) spaceborne platforms have been hailed as a promising technology for mapping tree species over broad spatial extents. WorldView-3, the most advanced VHR sensor, provides spectral data in 16 bands covering the visible to near-infrared (VNIR, 400–1040 nm) and shortwave-infrared (SWIR, 1210–2365 nm) wavelength ranges. It also collects images at unprecedented levels of details using a panchromatic band with 0.3-m of spatial resolution. However, the potential of WorldView-3 at its full spectral and spatial resolution for tropical tree species classification remains unknown. In this study, we performed a comprehensive assessment of WorldView-3 images acquired in the dry and wet seasons for tree species discrimination in tropical semi-deciduous forests. Classification experiments were performed using VNIR individually and combined with SWIR channels. To take advantage of the sub-metric resolution of the panchromatic band for classification, we applied an individual tree crown (ITC)-based approach that employed pan-sharpened VNIR bands and gray level co-occurrence matrix texture features. We determined whether the combination of images from the two annual seasons improves the classification accuracy. Finally, we investigated which plant traits influenced species detection. The new SWIR sensing capabilities of WorldView-3 increased the average producer’s accuracy up to 7.8%, by enabling the detection of non-photosynthetic vegetation within ITCs. The combination of VNIR bands from the two annual seasons did not improve the classification results when compared to the results obtained using images from each season individually. The use of VNIR bands at their original 1.2-m spatial resolution yielded average producer’s accuracies of 43.1 ± 3.1% and 38.8 ± 3% in the wet and dry seasons, respectively. The ITC-based approach improved the accuracy to 70 ± 8% in the wet and 68.4 ± 7.4% in the dry season. Texture analysis of the panchromatic band enabled the detection of species-specific differences in crown structure, which improved species detection. The use of texture analysis, pan-sharpening, and ITC delineation is a potential approach to perform tree species classification in tropical forests with WorldView-3 satellite images. Numéro de notice : A2019-117 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE/FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.01.019 Date de publication en ligne : 28/01/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.01.019 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92444
in ISPRS Journal of photogrammetry and remote sensing > vol 149 (March 2019) . - pp 119 - 131[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2019031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Complete 3D scene parsing from an RGBD image / Chuhang Zou in International journal of computer vision, vol 127 n° 2 (February 2019)
PermalinkLearning spectral-spatial-temporal features via a recurrent convolutional neural network for change detection in multispectral imagery / Lichao Mou in IEEE Transactions on geoscience and remote sensing, vol 57 n° 2 (February 2019)
PermalinkTree cover mapping using hybrid fuzzy C-means method and multispectral satellite images / Linda Gulbe in Baltic forestry, vol 25 n° 1 ([01/02/2019])
PermalinkAnalyse d’images par méthode de Deep Learning appliquée au contexte routier en conditions météorologiques dégradées / Khouloud Dahmane (2019)
PermalinkPermalinkPermalinkForest inventory sensitivity to UAS-based image processing algorithms / Bonifasius Maturbongs in Annals of forest research, vol 62 n° 1 (January - June 2019)
PermalinkGeographic Information Systems in Geospatial Intelligence, ch. 5. Spectral optimization of airborne multispectral camera for land cover classification: automatic feature selection and spectral band clustering / Arnaud Le Bris (2019)
PermalinkPermalinkPermalinkPermalinkMultimodal scene understanding: algorithms, applications and deep learning, ch. 11. Decision fusion of remote-sensing data for land cover classification / Arnaud Le Bris (2019)
PermalinkPermalinkSegmentation d'image par intégration itérative de connaissances / Mahaman Sani Chaibou Salaou (2019)
PermalinkSemantic aware quality evaluation of 3D building models : Modeling and simulation / Oussama Ennafii (2019)
PermalinkSensitivity of urban material classification to spatial and spectral configurations from visible to short-wave infrared / Arnaud Le Bris (2019)
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PermalinkSpectral unmixing with perturbed endmembers / Reza Arablouei in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)
PermalinkTime-space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series / Vivien Sainte Fare Garnot (2019)
PermalinkTraitement d'images multispectrales et spatialisation des données pour la caractérisation de la matière organique des phases solides naturelles / Kevin Jacq (2019)
PermalinkUrban morpho-types classification from SPOT-6/7 imagery and Sentinel-2 time series / Arnaud Le Bris (2019)
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