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Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters / Ting Mao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 11 (November 2019) . - pp 8732 - 8744 Titre : Unsupervised classification of multispectral images embedded with a segmentation of panchromatic images using localized clusters Type de document : Article/Communication Auteurs : Ting Mao, Auteur ; Wei Huang, Auteur Année de publication : 2019 Article en page(s) : pp 8732 - 8744 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation d'image [Termes descripteurs IGN] segmentation multirésolution [Termes descripteurs IGN] superpixel Résumé : (auteur) There are many approaches to fuse panchromatic (PAN) and multispectral (MS) images for classification, mainly including sharpening-then-classification methods, classification-then-sharpening methods, and segmentation-then-classification methods. The generalized Chinese restaurant franchise (gCRF) is a segmentation-then-classification-like method to fuse very high resolution (VHR) PAN and MS images for classification, which has the limitation the same as that of the general segmentation-then-classification methods that segmentation errors will affect the subsequent classification. The problems of gCRF are that during the segmentation step, the spatial coherence in the image plane is deficient and the global clusters without spatial position information are used for segmentation, which may lead to undersegmented and disconnected regions in the segmentation results and decrease classification accuracy. In this paper, we propose an improved model, which overcomes the problems of the gCRF during the segmentation step, to increase the classification accuracy by the following two ways: 1) building the spatial coherence in the image plane by introducing neighborhood information of superpixels to construct the subimages and 2) using localized clusters with spatial location information instead of global clusters to measure the similarity between superpixels and segments. The experimental results show that the problems of undersegmentation and disconnected segments are both alleviated, resulting in better classification results in terms of the visual and quantitative aspects. Numéro de notice : A2019-597 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2922672 date de publication en ligne : 17/07/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2922672 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94589 [article]

Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data / Mateo Gašparović in Photogrammetric record, vol 34 n° 167 (September 2019)  

Public [article]  inPhotogrammetric record > vol 34 n° 167 (September 2019) . - pp 266 - 281 Titre : Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data Type de document : Article/Communication Auteurs : Mateo Gašparović, Auteur ; Dino Dobrinić, Auteur ; Damir Medak, Auteur Année de publication : 2019 Article en page(s) : pp 266 - 281 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image ALOS [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orientation du capteur [Termes descripteurs IGN] orthorectification [Termes descripteurs IGN] point d'appui [Termes descripteurs IGN] précision géométrique (imagerie) Résumé : (auteur) This paper provides geometric accuracy improvements for very highresolution WorldView-2 (WV2) Ortho Ready Standard Level-2A (ORS2A) satelliteimages in the panchromatic (PAN) band. The entire process can be divided intotwo steps: (1) sensor orientation, which was conducted on 40 ground controlpoints (GCPs) to determine which sensor model was best depending on variousnumbers of GCPs; and (2) orthorectification, whose main objective was tocompare the accuracy of the process when using different freely available globaldigital elevation models (DEMs). The research showed that a sensor model basedon rational polynomial coefficients (RPCs) with a shift or zero-order biascorrection attained the best geopositioning accuracy during the orientation stepwith the use of seven GCPs. The highest accuracy was achieved for imagesorthorectified with the ALOS World 3D-30 m DEM (AW3D30). Numéro de notice : A2019-432 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12292 date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1111/phor.12292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93759 [article]

Pan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 165 - 183 Titre : Pan-sharpening via deep metric learning Type de document : Article/Communication Auteurs : Yinghui Xing, Auteur ; Min Wang, Auteur ; Shuyuan Yang, Auteur ; Licheng Jiao, Auteur Année de publication : 2018 Article en page(s) : pp 165 - 183 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Neighbors Embedding based pansharpening methods have received increasing interests in recent years. However, image patches do not strictly follow the similar structure in the shallow MultiSpectral (MS) and PANchromatic (PAN) image spaces, consequently leading to a bias to the pansharpening. In this paper, a new deep metric learning method is proposed to learn a refined geometric multi-manifold neighbor embedding, by exploring the hierarchical features of patches via multiple nonlinear deep neural networks. First of all, down-sampled PAN images from different satellites are divided into a large number of training image patches and are then grouped coarsely according to their shallow geometric structures. Afterwards, several Stacked Sparse AutoEncoders (SSAE) with similar structures are separately constructed and trained by these grouped patches. In the fusion, image patches of the source PAN image pass through the networks to extract features for formulating a deep distance metric and thus deriving their geometric labels. Then, patches with the same geometric labels are grouped to form geometric manifolds. Finally, the assumption that MS patches and PAN patches form the same geometric manifolds in two distinct spaces, is cast on geometric groups to formulate geometric multi-manifold embedding for estimating high resolution MS image patches. Some experiments are taken on datasets acquired by different satellites. The experimental results demonstrate that our proposed method can obtain better fusion results than its counterparts in terms of visual results and quantitative evaluations. Numéro de notice : A2018-493 Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.016 date de publication en ligne : 17/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91236 [article]

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Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest / Qingxia Zhao in Forests, vol 9 n° 10 (October 2018)  

Public [article]  inForests > vol 9 n° 10 (October 2018) Titre : Estimating forest canopy cover in black locust (Robinia pseudoacacia L.) plantations on the loess plateau using random forest Type de document : Article/Communication Auteurs : Qingxia Zhao, Auteur ; Non-répertorié, Auteur ; Jun Zhao, Auteur ; Jingjing Zhou, Auteur ; et al., Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] canopée [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] détection d'arbres [Termes descripteurs IGN] Enhanced vegetation index [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] loess [Termes descripteurs IGN] matrice de co-occurence [Termes descripteurs IGN] plantation forestière [Termes descripteurs IGN] régression [Termes descripteurs IGN] Robinia pseudoacacia [Termes descripteurs IGN] Soil Adjusted Vegetation Index Résumé : (Auteur) The forest canopy is the medium for energy and mass exchange between forest ecosystems and the atmosphere. Remote sensing techniques are more efficient and appropriate for estimating forest canopy cover (CC) than traditional methods, especially at large scales. In this study, we evaluated the CC of black locust plantations on the Loess Plateau using random forest (RF) regression models. The models were established using the relationships between digital hemispherical photograph (DHP) field data and variables that were calculated from satellite images. Three types of variables were calculated from the satellite data: spectral variables calculated from a multispectral image, textural variables calculated from a panchromatic image (Tpan) with a 15 × 15 window size, and textural variables calculated from spectral variables (TB+VIs) with a 9 × 9 window size. We compared different mtry and ntree values to find the most suitable parameters for the RF models. The results indicated that the RF model of spectral variables explained 57% (root mean square error (RMSE) = 0.06) of the variability in the field CC data. The soil-adjusted vegetation index (SAVI) and enhanced vegetation index (EVI) were more important than other spectral variables. The RF model of Tpan obtained higher accuracy (R2 = 0.69, RMSE = 0.05) than the spectral variables, and the grey level co-occurrence matrix-based texture measure—Correlation (COR) was the most important variable for Tpan. The most accurate model was obtained from the TB+VIs (R2 = 0.79, RMSE = 0.05), which combined spectral and textural information, thus providing a significant improvement in estimating CC. This model provided an effective approach for detecting the CC of black locust plantations on the Loess Plateau. Numéro de notice : A2018-477 Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9100623 date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.3390/f9100623 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91178 [article]

Littoral, "Ricochet" ausculte / Marielle Mayo in Géomètre, n° 2155 (février 2018)

Public [article]  inGéomètre > n° 2155 (février 2018) . - pp 48 - 51 Titre : Littoral, "Ricochet" ausculte Type de document : Article/Communication Auteurs : Marielle Mayo, Auteur Année de publication : 2018 Article en page(s) : pp 48 - 51 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] acquisition de données [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] drone [Termes descripteurs IGN] éboulement [Termes descripteurs IGN] érosion côtière [Termes descripteurs IGN] falaise [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] modélisation 3D [Termes descripteurs IGN] Normandie (région 2016) [Termes descripteurs IGN] récepteur GPS [Termes descripteurs IGN] restitution [Termes descripteurs IGN] trait de côte Résumé : (Auteur) En Normandie, des chercheurs et des élus travaillent main dans la main pour améliorer la connaissance des risques littoraux et intégrer les changements côtiers dans les projets d'aménagement. Numéro de notice : A2018-028 Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89189 [article]

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Intersensor statistical matching for pansharpening : theoretical issues and practical solutions / Luciano Alparone in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

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Pan-sharpening of Landsat-8 images and its application in calculating vegetation greenness and canopy water contents / Khan Rubayet Rahaman in ISPRS International journal of geo-information, vol 6 n° 6 (June 2017)  

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Evaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)  

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Habitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery / Guy W. Jeter Jr in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)  

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Supervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)  

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Exploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)  

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A critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)

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Correction of distorsions in YG-12 high-resolution panchromatic images / Yonghua Jiang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 1 (January 2015)  

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Pléiades satellites image quality commissioning / Laurent Lebègue in Revue Française de Photogrammétrie et de Télédétection, n° 209 (Janvier 2015)

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Quantification et cartographie de la structure forestière à partir de la texture des images Pléiades / Benoit Beguet in Revue Française de Photogrammétrie et de Télédétection, n° 208 (Octobre 2014)

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