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Investigating operational country-level crop monitoring with Sentinel~1 and~2 imagery / Nicolas David in Remote sensing letters, vol 12 n° 10 (October 2021)  

Public [article]  inRemote sensing letters > vol 12 n° 10 (October 2021) . - pp 970 - 982 Titre : Investigating operational country-level crop monitoring with Sentinel~1 and~2 imagery Type de document : Article/Communication Auteurs : Nicolas David , Auteur ; Sébastien Giordano , Auteur ; Clément Mallet , Auteur Année de publication : 2021 Projets : MAESTRIA / Mallet, Clément Article en page(s) : pp 970 - 982 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] chaîne de traitement [Termes IGN] France (administrative) [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] politique agricole commune [Termes IGN] surveillance agricole Résumé : (auteur) In this paper, we propose an operational solution for the yearly classification of crop parcels at national scale (namely France) for Land Parcel Identification System updating, under the Common Agricultural Policy (CAP) umbrella. Our pipeline is based on the ι2 open-source framework and fed with both time series of Sentinel-1 radar and Sentinel-2 optical images, with complementary contributions. Three conceivable scenarios are investigated with two sets of nomenclatures (17 and 43 classes): early, on-line, and late classifications. Experiments performed on 2017 show very satisfactory results (82–97%), locally almost on-par with state-of-the-art deep-based methods. We can conclude our framework offers a strong basis for country-scale operational deployment for 2020+CAP. Numéro de notice : A2021-600 Affiliation des auteurs : UGE-LaSTIG (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/2150704X.2021.1950940 En ligne : https://doi.org/10.1080/2150704X.2021.1950940 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98222 [article]

A deep translation (GAN) based change detection network for optical and SAR remote sensing images / Xinghua Li in ISPRS Journal of photogrammetry and remote sensing, vol 179 (September 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 179 (September 2021) . - pp 14 - 34 Titre : A deep translation (GAN) based change detection network for optical and SAR remote sensing images Type de document : Article/Communication Auteurs : Xinghua Li, Auteur ; Zhengshun Du, Auteur ; Yanyuan Huang, Auteur ; Zhenyu Tan, Auteur Année de publication : 2021 Article en page(s) : pp 14 - 34 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] détection de changement [Termes IGN] image à très haute résolution [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] méthode robuste [Termes IGN] polarisation [Termes IGN] réseau antagoniste génératif [Termes IGN] réseau neuronal profond [Termes IGN] zone d'intérêt Résumé : (Editeur) With the development of space-based imaging technology, a larger and larger number of images with different modalities and resolutions are available. The optical images reflect the abundant spectral information and geometric shape of ground objects, whose qualities are degraded easily in poor atmospheric conditions. Although synthetic aperture radar (SAR) images cannot provide the spectral features of the region of interest (ROI), they can capture all-weather and all-time polarization information. In nature, optical and SAR images encapsulate lots of complementary information, which is of great significance for change detection (CD) in poor weather situations. However, due to the difference in imaging mechanisms of optical and SAR images, it is difficult to conduct their CD directly using the traditional difference or ratio algorithms. Most recent CD methods bring image translation to reduce their difference, but the results are obtained by ordinary algebraic methods and threshold segmentation with limited accuracy. Towards this end, this work proposes a deep translation based change detection network (DTCDN) for optical and SAR images. The deep translation firstly maps images from one domain (e.g., optical) to another domain (e.g., SAR) through a cyclic structure into the same feature space. With the similar characteristics after deep translation, they become comparable. Different from most previous researches, the translation results are imported to a supervised CD network that utilizes deep context features to separate the unchanged pixels and changed pixels. In the experiments, the proposed DTCDN was tested on four representative data sets from Gloucester, California, and Shuguang village. Compared with state-of-the-art methods, the effectiveness and robustness of the proposed method were confirmed. Numéro de notice : A2021-574 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.07.007 date de publication en ligne : 23/07/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.07.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98174 [article]

Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning / Qiutong Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 405 - 412 Titre : Resolution enhancement for large-scale land cover mapping via weakly supervised deep learning Type de document : Article/Communication Auteurs : Qiutong Yu, Auteur ; Wei Liu, Auteur ; Wesley Nunes Gonçalves, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 405 - 412 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] apprentissage profond [Termes IGN] apprentissage semi-dirigé [Termes IGN] carte d'occupation du sol [Termes IGN] changement d'occupation du sol [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données d'apprentissage [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image multibande [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] image Terra-MODIS [Termes IGN] série temporelle Résumé : (Auteur) Multispectral satellite imagery is the primary data source for monitoring land cover change and characterizing land cover globally. However, the consistency of land cover monitoring is limited by the spatial and temporal resolutions of the acquired satellite images. The public availability of daily high-resolution images is still scarce. This paper aims to fill this gap by proposing a novel spatiotemporal fusion method to enhance daily low spatial resolution land cover mapping using a weakly supervised deep convolutional neural network. We merge Sentinel images and moderate resolution imaging spectroradiometer (MODIS )-derived thematic land cover maps under the application background of massive remote sensing data and the large spatial resolution gaps between MODIS data and Sentinel images. The neural network training was conducted on the public data set SEN12MS, while the validation and testing used ground truth data from the 2020 IEEE Geoscience and Remote Sensing Society data fusion contest. The proposed data fusion method shows that the synthesized land cover map has significantly higher spatial resolution than the corresponding MODIS-derived land cover map. The ensemble approach can be implemented for generating high-resolution time series of satellite images by fusing fine images from Sentinel-1 and -2 and daily coarse images from MODIS. Numéro de notice : A2021-373 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.6.405 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.6.405 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97825 [article]

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Lifting scheme-based sparse density feature extraction for remote sensing target detection / Ling Tian in Remote sensing, vol 13 n° 9 (May 2021)  

Public [article]  inRemote sensing > vol 13 n° 9 (May 2021) . - n° 1862 Titre : Lifting scheme-based sparse density feature extraction for remote sensing target detection Type de document : Article/Communication Auteurs : Ling Tian, Auteur ; Yu Cao, Auteur ; Zishan Shi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 1862 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de cible [Termes IGN] données clairsemées [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] image optique [Termes IGN] image radar moirée [Termes IGN] transformation en ondelettes Résumé : (auteur) The design of backbones is of great significance for enhancing the location and classification precision in the remote sensing target detection task. Recently, various approaches have been proposed on altering the feature extraction density in the backbones to enlarge the receptive field, make features prominent, and reduce computational complexity, such as dilated convolution and deformable convolution. Among them, one of the most widely used methods is strided convolution, but it loses the information about adjacent feature points which leads to the omission of some useful features and the decrease of detection precision. This paper proposes a novel sparse density feature extraction method based on the relationship between the lifting scheme and convolution, which improves the detection precision while keeping the computational complexity almost the same as the strided convolution. Experimental results on remote sensing target detection indicate that our proposed method improves both detection performance and network efficiency. Numéro de notice : A2021-405 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13091862 date de publication en ligne : 10/05/2021 En ligne : https://doi.org/10.3390/rs13091862 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97720 [article]

Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data / Vijay Pratap Yadav in Geocarto international, vol 36 n° 7 ([01/04/2021])  

Public [article]  inGeocarto international > vol 36 n° 7 [01/04/2021] . - pp 791 - 802 Titre : Leaf area index estimation of wheat crop using modified water cloud model from the time-series SAR and optical satellite data Type de document : Article/Communication Auteurs : Vijay Pratap Yadav, Auteur ; Rajendra Prasad, Auteur ; Ruchi Bala, Auteur Année de publication : 2021 Article en page(s) : pp 791 - 802 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] blé (céréale) [Termes IGN] image radar moirée [Termes IGN] image Sentinel-MSI [Termes IGN] image Sentinel-SAR [Termes IGN] Inde [Termes IGN] Leaf Area Index [Termes IGN] polarisation [Termes IGN] rendement agricole [Termes IGN] série temporelle Résumé : (Auteur) The time-series synthetic aperture radar (SAR) and optical satellite data were used for the leaf area index (LAI) estimation of wheat crop using modified water cloud model (MWCM) in Varanasi district, India. In this study, MWCM was developed by including scale invariant vegetation fraction (fveg) in the old WCM for the estimation of LAI. The non-linear least square optimization technique was applied to determine the optimum model parameters for the retrieval of LAI which was further validated with the observed LAI. The estimated values of LAI by MWCM at VV polarization shows good correspondence (R2 = 0.901 and RMSE = 0.456 m2/m2) with the observed LAI values than at VH polarization (R2 = 0.742 and RMSE = 0.521 m2/m2).The MWCM shows great potential for the LAI estimation of wheat crop by incorporating optical data (i.e. Sentinel-2) in terms of fveg with SAR data (i.e. Sentinel-1A). Numéro de notice : A2021-294 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1624984 date de publication en ligne : 10/06/2019 En ligne : https://doi.org/10.1080/10106049.2019.1624984 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97352 [article]

Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Extraction of impervious surface using Sentinel-1A time-series coherence images with the aid of a Sentinel-2A image / Wenfu Wu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 3 (March 2021)  

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Deep learning for wildfire progression monitoring using SAR and optical satellite image time series / Puzhao Zhang (2021)  

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Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping / Mthembeni Mngadi in Geocarto international, vol 36 n° 1 ([01/01/2021])  

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Real-time multimodal semantic scene understanding for autonomous UGV navigation / Yifei Zhang (2021)  

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Cartographie des cultures dans le périmètre du Loukkos (Maroc) : apport de la télédétection radar et optique / Siham Acharki in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)  

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Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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A deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

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A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery / Niangang Jiao in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

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Textural classification of remotely sensed images using multiresolution techniques / Rizwan Ahmed Ansari in Geocarto international, vol 35 n° 14 ([15/10/2020])  

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