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Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion / Olivier Stocker in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
[article]
Titre : Can SPOT-6/7 CNN semantic segmentation improve Sentinel-2 based land cover products? sensor assessment and fusion Type de document : Article/Communication Auteurs : Olivier Stocker, Auteur ; Arnaud Le Bris , Auteur Année de publication : 2020 Projets : MAESTRIA / Mallet, Clément Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Projets : TOSCA Parcelle / Le Bris, Arnaud Article en page(s) : pp 557 - 564 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] fusion d'images
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 7
[Termes IGN] occupation du sol
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels. Numéro de notice : A2020-504 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-557-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-557-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95644
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 557 - 564[article]CNN 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)
[article]
Titre : CNN semantic segmentation to retrieve past land cover out of historical orthoimages and DSM: first experiments Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Année de publication : 2020 Projets : HIATUS / Giordano, Sébastien Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 1013 - 1019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] base de données historiques
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image aérienne
[Termes IGN] modèle numérique de surface
[Termes IGN] occupation du sol
[Termes IGN] orthoimage
[Termes IGN] segmentation sémantiqueRésumé : (auteur) Images from archival aerial photogrammetric surveys are a unique and relatively unexplored means to chronicle 3D land-cover changes occurred since the mid 20th century. They provide a relatively dense temporal sampling of the territories with a very high spatial resolution. Thus, they offer time series data which can answer a large variety of long-term environmental monitoring studies. Besides, they are generally stereoscopic surveys, making it possible to derive 3D information (Digital Surface Models). In recent years, they have often been digitized, making them more suitable to be considered in automatic analyses processes. Some photogrammetric softwares make it possible to retrieve their geometry (pose and camera calibration) and to generate corresponding DSM and orthophotomosaic. Thus, archival aerial photogrammetric surveys appear as being a powerful remote sensing data source to study land use/cover evolution over the last century. However, several difficulties have to be faced to be able to use them in automatic analysis processes. Indeed, surveys available on a study area can exhibit very different characteristics: survey pattern, focal, spatial resolution, modality (panchromatic, colour, infrared…). Planimetric and altimetric accuracies of derived products strongly depend on these characteristics. Thus, analysis processes have to cope with these uncertainties. Another important gap states in the lack of training data. Deep learning methods and especially Convolutional Neural Networks (CNN) are at present the most efficient semantic segmentation methods as long as a sufficient training dataset is available. However, temporal gaps can be very important between existing available databases and archival data. In this study, two custom variants of simple yet effective U-net - Deconv-Net inspired DL architectures are developed to process ortho-image and DSM based information. They are then trained out of a groundtruth derived out of a recent database to process archival datasets. Numéro de notice : A2020-469 Affiliation des auteurs : UGE-LASTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-1013-2020 Date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-1013-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95637
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 1013 - 1019[article]Comparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India / Tarun Kumar in Geocarto international, vol 35 n° 11 ([01/08/2020])
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Titre : Comparative study of different models for soil erosion and sediment yield in Pairi watershed, Chhattisgarh, India Type de document : Article/Communication Auteurs : Tarun Kumar, Auteur ; D. C. Jhariya, Auteur ; H.K. Pandey, Auteur Année de publication : 2020 Article en page(s) : pp 1245 - 1266 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse comparative
[Termes IGN] bassin hydrographique
[Termes IGN] érosion côtière
[Termes IGN] Inde
[Termes IGN] modèle RUSLE
[Termes IGN] occupation du sol
[Termes IGN] précipitation
[Termes IGN] sédiment
[Termes IGN] système d'information géographique
[Termes IGN] texture du sol
[Termes IGN] topographie
[Termes IGN] utilisation du solRésumé : (auteur) A Comparative Study of different models for assessment of soil erosion (Universal Soil Loss Equation and Revised Universal Soil Loss Equation) and sediment yield (Modify Universal Soil Loss Equation and Sediment delivery ratio) carried out in Pairi Watershed, Chhattisgarh, India. The main objective is to estimate the spatial pattern of annual soil erosion rate and sediment yield using Remote Sensing and GIS technique which are promising and cost-effective tools for mapping critical areas. To calculate the soil erosion and sediment yield different spatial data sets were utilized such as rainfall, soil texture, topography, land use land cover etc. Obtained result of soil erosion and sediment yield was validated using field check using Baronda CWC Observatory data. It is found that, RUSLE model for soil erosion and MUSLE model for sediment yield has been found to be most reliable for the study area. Numéro de notice : A2020-470 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1576779 Date de publication en ligne : 26/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1576779 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95567
in Geocarto international > vol 35 n° 11 [01/08/2020] . - pp 1245 - 1266[article]Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])
[article]
Titre : Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method Type de document : Article/Communication Auteurs : Vijendra Singh Bramhe, Auteur ; Sanjay Kumar Ghosh, Auteur ; Pradeep Kumar Garg, Auteur Année de publication : 2020 Article en page(s) : pp 1067 - 1087 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse spectrale
[Termes IGN] analyse texturale
[Termes IGN] bati
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image Landsat-OLI
[Termes IGN] image multibande
[Termes IGN] matrice de co-occurrence
[Termes IGN] niveau de gris (image)
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] réseau neuronal artificiel
[Termes IGN] texture d'imageRésumé : (auteur) Information of built-up area is essential for various applications, such as sustainable development or urban planning. Built-up area extraction using optical data is challenging due to spectral confusion between built-up and other classes (bare land or river sand, etc.). Here an automated approach has been proposed to generate built-up maps using spectral-textural features and feature selection techniques. Eight Grey-Level Co-Occurrence Matrix based texture features are extracted using Landsat-8 Operational Land Imager bands and combined with multispectral data. The most informative features are selected from combined spectral-textural dataset using feature selection techniques. Further, Support Vector Machine (SVM) classifiers are trained on labelled samples using optimal features and results are compared with Back Propagation-Neural Network (BP-NN) and k-Nearest Neighbour (k-NN). The results show that inclusion of textural features and applying feature selection methods increases the highest overall accuracy of Linear-SVM, RBF-SVM, BP-NN, and k-NN by 9.20%, 9.09%, 8.42%, and 7.39%, respectively. Numéro de notice : A2020-425 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1566406 Date de publication en ligne : 18/03/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566406 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95489
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1067 - 1087[article]Extraction of urban built-up areas from nighttime lights using artificial neural network / Tingting Xu in Geocarto international, vol 35 n° 10 ([01/08/2020])
[article]
Titre : Extraction of urban built-up areas from nighttime lights using artificial neural network Type de document : Article/Communication Auteurs : Tingting Xu, Auteur ; Giovanni Coco, Auteur ; Jay Gao, Auteur Année de publication : 2020 Article en page(s) : pp 1049 - 1066 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aménagement du territoire
[Termes IGN] bati
[Termes IGN] cartographie urbaine
[Termes IGN] classification dirigée
[Termes IGN] développement durable
[Termes IGN] échantillonnage
[Termes IGN] éclairage public
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] Normalized Difference Vegetation Index
[Termes IGN] rayonnement lumineux
[Termes IGN] réseau neuronal artificiel
[Termes IGN] seuillage
[Termes IGN] température au sol
[Termes IGN] zone urbaineRésumé : (auteur) The spatial distribution of urban areas at the national and regional scales is critical for urban planners and governments to design sustainable and environment-friendly future development plans. The nighttime lights (NTL) data provide an effective way to monitor the urban at different scales however is usually achieved by using empirical threshold-based algorithms. This study proposed a novel Artificial Neural Network (ANN) approach, using moderate resolution imageries as NTL, MODIS NDVI and land surface temperature data, to map urban areas. Both random and maximum dissimilarity distance algorithm sampling methods were considered and compared. The validation of the urban areas extracted from MDA-based ANN against the 2011 US national land cover data showed a reasonable quality (overall accuracy = 97.84; Kappa = 0.74) and achieved more accurate result than the threshold method. This study demonstrates that ANN can provide an effective, rapid, and accurate alternative in extracting urban built-up areas from NTL. Numéro de notice : A2020-424 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1559887 Date de publication en ligne : 21/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1559887 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95488
in Geocarto international > vol 35 n° 10 [01/08/2020] . - pp 1049 - 1066[article]Landuse 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)PermalinkDense stereo matching strategy for oblique images that considers the plane directions in urban areas / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkMoGUS, un outil de modélisation et d'analyse comparative des trames urbaines / Dominique Badariotti in Revue internationale de géomatique, vol 30 n° 3-4 (juillet - décembre 2020)PermalinkPath length correction for improving leaf area index measurements over sloping terrains: A deep analysis through computer simulation / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)PermalinkRegionalization of flood magnitudes using the ecological attributes of watersheds / Bahman Jabbarian Amiri in Geocarto international, vol 35 n° 9 ([01/07/2020])PermalinkRoles of horizontal and vertical tree canopy structure in mitigating daytime and nighttime urban heat island effects / Jike Chen in International journal of applied Earth observation and geoinformation, vol 89 (July 2020)PermalinkAn integrated approach for detection and prediction of greening situation in a typical desert area in China and its human and climatic factors analysis / Lei Zhou in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkA change of theme: the role of generalization in thematic mapping / Paulo Raposo in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)PermalinkDeveloping shopping and dining walking indices using POIs and remote sensing data / Yingbin Deng in ISPRS International journal of geo-information, vol 9 n° 6 (June 2020)Permalink