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Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning / Mingge Yu in Remote sensing, vol 14 n° 10 (May-2 2022)
[article]
Titre : Research on automatic identification method of terraces on the Loess plateau based on deep transfer learning Type de document : Article/Communication Auteurs : Mingge Yu, Auteur ; Xiaoping Rui, Auteur ; Weiyi Xie, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2446 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] détection automatique
[Termes IGN] échantillonnage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à haute résolution
[Termes IGN] image panchromatique
[Termes IGN] image Worldview
[Termes IGN] modèle de simulation
[Termes IGN] surface cultivée
[Termes IGN] terrasseRésumé : (auteur) Rapid, accurate extraction of terraces from high-resolution images is of great significance for promoting the application of remote-sensing information in soil and water conservation planning and monitoring. To solve the problem of how deep learning requires a large number of labeled samples to achieve good accuracy, this article proposes an automatic identification method for terraces that can obtain high precision through small sample datasets. Firstly, a terrace identification source model adapted to multiple data sources is trained based on the WorldView-1 dataset. The model can be migrated to other types of images for terracing extraction as a pre-trained model. Secondly, to solve the small sample problem, a deep transfer learning method for accurate pixel-level extraction of high-resolution remote-sensing image terraces is proposed. Finally, to solve the problem of insufficient boundary information and splicing traces during prediction, a strategy of ignoring edges is proposed, and a prediction model is constructed to further improve the accuracy of terrace identification. In this paper, three regions outside the sample area are randomly selected, and the OA, F1 score, and MIoU averages reach 93.12%, 91.40%, and 89.90%, respectively. The experimental results show that this method, based on deep transfer learning, can accurately extract terraced field surfaces and segment terraced field boundaries. Numéro de notice : A2022-402 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14102446 Date de publication en ligne : 19/05/2022 En ligne : https://doi.org/10.3390/rs14102446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100705
in Remote sensing > vol 14 n° 10 (May-2 2022) . - n° 2446[article]Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation / Toshihiro Sakamoto in Photogrammetric Engineering & Remote Sensing, PERS, vol 88 n° 5 (May 2022)
[article]
Titre : Alternative procedure to improve the positioning accuracy of orthomosaic images acquired with Agisoft Metashape and DJI P4 multispectral for crop growth observation Type de document : Article/Communication Auteurs : Toshihiro Sakamoto, Auteur ; Daisuke Ogawa, Auteur ; Satoko Hiura, Auteur ; Nobusuke Iwasaki, Auteur Année de publication : 2022 Article en page(s) : pp 323 - 332 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] bande spectrale
[Termes IGN] blé (céréale)
[Termes IGN] chlorophylle
[Termes IGN] image à haute résolution
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] orthophotoplan numérique
[Termes IGN] point d'appui
[Termes IGN] précision du positionnement
[Termes IGN] rizière
[Termes IGN] structure-from-motionRésumé : (Auteur) Vegetation indices (VIs), such as the green chlorophyll index and normalized difference vegetation index, are calculated from visible and near-infrared band images for plant diagnosis in crop breeding and field management. The DJI P4 Multispectral drone combined with the Agisoft Metashape Structure from Motion/Multi View Stereo software is some of the most cost-effective equipment for creating high-resolution orthomosaic VI images. However, the manufacturer's procedure results in remarkable location estimation inaccuracy (average error: 3.27–3.45 cm) and alignment errors between spectral bands (average error: 2.80–2.84 cm). We developed alternative processing procedures to overcome these issues, and we achieved a higher positioning accuracy (average error: 1.32–1.38 cm) and better alignment accuracy between spectral bands (average error: 0.26–0.32 cm). The proposed procedure enables precise VI analysis, especially when using the green chlorophyll index for corn, and may help accelerate the application of remote sensing techniques to agriculture. Numéro de notice : A2022-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.21-00064R2 Date de publication en ligne : 01/05/2022 En ligne : https://doi.org/10.14358/PERS.21-00064R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101379
in Photogrammetric Engineering & Remote Sensing, PERS > vol 88 n° 5 (May 2022) . - pp 323 - 332[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2022052 SL Revue Centre de documentation Revues en salle Disponible 105-2022051 SL Revue Centre de documentation Revues en salle Disponible ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network / Qinjun Qiu in Transactions in GIS, vol 26 n° 3 (May 2022)
[article]
Titre : ChineseTR: A weakly supervised toponym recognition architecture based on automatic training data generator and deep neural network Type de document : Article/Communication Auteurs : Qinjun Qiu, Auteur ; Zhong Xie, Auteur ; Shu Wang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 1256 - 1279 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] apprentissage profond
[Termes IGN] Chine
[Termes IGN] classification par réseau neuronal récurrent
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] données issues des réseaux sociaux
[Termes IGN] échantillonnage de données
[Termes IGN] OpenStreetMap
[Termes IGN] reconnaissance automatique
[Termes IGN] répertoire toponymique
[Termes IGN] site wiki
[Termes IGN] toponymeRésumé : (auteur) Toponym recognition is used to extract toponyms from natural language texts, which is a fundamental task of ubiquitous geographic information applications. Existing toponym recognition methods with state-of-the-art performance mainly leverage supervised learning (i.e., deep-learning-based approaches) with parameters learned from massive, labeled datasets that must be annotated manually. This is a great inconvenience when model training needs to fit different domain texts, especially those of social media messaging. To address this issue, this article proposes a weakly supervised Chinese toponym recognition (ChineseTR) architecture that leverages a training dataset creator that generates training datasets automatically based on word collections and associated word frequencies from various texts and an extension recognizer that employs a basic bidirectional recurrent neural network based on particular features designed for toponym recognition. The results show that the proposed ChineseTR achieves a 0.76 F1 score in a corpus with a 0.718 out-of-vocabulary rate and a 0.903 in-vocabulary rate. All comparative experiments demonstrate that ChineseTR is an effective and scalable architecture that recognizes toponyms. Numéro de notice : A2022-462 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1111/tgis.12902 Date de publication en ligne : 02/02/2022 En ligne : https://doi.org/10.1111/tgis.12902 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100796
in Transactions in GIS > vol 26 n° 3 (May 2022) . - pp 1256 - 1279[article]Conformal cylindrical properties of Adriatic Sea basin renderings on portolan charts / Tome Marelić in Cartographic journal (the), vol 59 n° 2 (May 2022)
[article]
Titre : Conformal cylindrical properties of Adriatic Sea basin renderings on portolan charts Type de document : Article/Communication Auteurs : Tome Marelić, Auteur Année de publication : 2022 Article en page(s) : pp 83 - 101 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Projections
[Termes IGN] Adriatique, mer
[Termes IGN] modèle géométrique
[Termes IGN] portulan
[Termes IGN] projection conforme cylindrique
[Termes IGN] projection Universal Transverse Mercator
[Termes IGN] transformation de HelmertRésumé : (auteur) The geometry of Adriatic Sea basin renderings on 12 portolan charts made from the late thirteenth to the late sixteenth century was compared to a modern map by applying Helmert transformation in order to preserve their initial geometry and by using the same 40 identical points per chart. In the first stage of the research, the geometry of the portolan charts was compared to six selected map projections in order to determine the geometrical ‘best fit'. In the second stage, networks of lines plotted on charts were divided regionally and functionally and compared to the ‘best fit’ map projection to determine their geometric patterns in more detail. The research results showed that Adriatic Sea basin renderings on portolan charts were probably the result of a deliberately applied map projection which was geometrically most similar to the modern conformal cylindrical (Mercator) projection. Numéro de notice : A2022-862 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/POSITIONNEMENT Nature : Article DOI : 10.1080/00087041.2021.1956263 Date de publication en ligne : 11/03/2022 En ligne : https://doi.org/10.1080/00087041.2021.1956263 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102278
in Cartographic journal (the) > vol 59 n° 2 (May 2022) . - pp 83 - 101[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 030-2022021 RAB Revue Centre de documentation En réserve L003 Disponible Fusion of optical, radar and waveform LiDAR observations for land cover classification / Huiran Jin in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)
[article]
Titre : Fusion of optical, radar and waveform LiDAR observations for land cover classification Type de document : Article/Communication Auteurs : Huiran Jin, Auteur ; Giorgos Mountrakis, Auteur Année de publication : 2022 Article en page(s) : pp 171 - 190 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse comparative
[Termes IGN] carte de la végétation
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image ALOS-PALSAR
[Termes IGN] image Landsat-TM
[Termes IGN] image multitemporelle
[Termes IGN] occupation du solRésumé : (Auteur) Land cover is an integral component for characterizing anthropogenic activity and promoting sustainable land use. Mapping distribution and coverage of land cover at broad spatiotemporal scales largely relies on classification of remotely sensed data. Although recently multi-source data fusion has been playing an increasingly active role in land cover classification, our intensive review of current studies shows that the integration of optical, synthetic aperture radar (SAR) and light detection and ranging (LiDAR) observations has not been thoroughly evaluated. In this research, we bridged this gap by i) summarizing related fusion studies and assessing their reported accuracy improvements, and ii) conducting our own case study where for the first time fusion of optical, radar and waveform LiDAR observations and the associated improvements in classification accuracy are assessed using data collected by spaceborne or appropriately simulated platforms in the LiDAR case. Multitemporal Landsat-5/Thematic Mapper (TM) and Advanced Land Observing Satellite-1/ Phased Array type L-band SAR (ALOS-1/PALSAR) imagery acquired in the Central New York (CNY) region close to the collection of airborne waveform LVIS (Land, Vegetation, and Ice Sensor) data were examined. Classification was conducted using a random forest algorithm and different feature sets in terms of sensor and seasonality as input variables. Results indicate that the combined spectral, scattering and vertical structural information provided the maximum discriminative capability among different land cover types, giving rise to the highest overall accuracy of 83% (2–19% and 9–35% superior to the two-sensor and single-sensor scenarios with overall accuracies of 64–81% and 48–74%, respectively). Greater improvement was achieved when combining multitemporal Landsat images with LVIS-derived canopy height metrics as opposed to PALSAR features, suggesting that LVIS contributed more useful thematic information complementary to spectral data and beneficial to the classification task, especially for vegetation classes. With the Global Ecosystem Dynamics Investigation (GEDI), a recently launched LiDAR instrument of similar properties to the LVIS sensor now operating onboard the International Space Station (ISS), it is our hope that this research will act as a literature summary and offer guidelines for further applications of multi-date and multi-type remotely sensed data fusion for improved land cover classification. Numéro de notice : A2022-228 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.03.010 Date de publication en ligne : 17/03/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.03.010 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100214
in ISPRS Journal of photogrammetry and remote sensing > vol 187 (May 2022) . - pp 171 - 190[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2022051 SL Revue Centre de documentation Revues en salle Disponible 081-2022053 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2022052 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Lines of power: The eighteenth-century struggle over the Norwegian–Swedish border in Central Scandinavia / Anne Christine Lien in Cartographic journal (the), vol 59 n° 2 (May 2022)PermalinkMapping and prediction of soil organic carbon by an advanced geostatistical technique using remote sensing and terrain data / Santanu Malik in Geocarto international, vol 37 n° 8 ([01/05/2022])PermalinkMapscapes: Applying anachronic techniques in contemporary maps as a design strategy for new ways of seeing / José Miguel Carvalho Cardoso in Cartographic journal (the), vol 59 n° 2 (May 2022)PermalinkMulti-modal temporal attention models for crop mapping from satellite time series / Vivien Sainte Fare Garnot in ISPRS Journal of photogrammetry and remote sensing, vol 187 (May 2022)PermalinkProduction of optimum forest roads and comparison of these routes with current forest roads: a case study in Maçka, Turkey / Faruk Yildirim in Geocarto international, vol 37 n° 8 ([01/05/2022])PermalinkA review of maps in PhDs: Is your map worth a thousand words? / Serena Coetzee in Cartographic journal (the), vol 59 n° 2 (May 2022)PermalinkL’usage des cartes en temps de guerre / Olivier Razemon in Géomètre, n° 2202 (mai 2022)PermalinkCrop type identification and spatial mapping using Sentinel-2 satellite data with focus on field-level information / Murali Krishna Gumma in Geocarto international, vol 37 n° 7 ([15/04/2022])PermalinkOptimal resolution of soil properties maps varies according to their geographical extent and location / Christian Piedallu in Geoderma, vol 412 (15 April 2022)PermalinkAccuracy issues for spatial update of digital cadastral maps / David Pullar in ISPRS International journal of geo-information, vol 11 n° 4 (April 2022)Permalink