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Improved geometric modeling of 1960s KH-5 ARGON satellite images for regional Antarctica applications / Wenkai Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 7 (July 2017) . - pp 477 - 491 Titre : Improved geometric modeling of 1960s KH-5 ARGON satellite images for regional Antarctica applications Type de document : Article/Communication Auteurs : Wenkai Ye, Auteur ; Gang Qiao, Auteur ; Fansi Kong, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 477 - 491 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] Antarctique [Termes IGN] changement temporel [Termes IGN] image à basse résolution [Termes IGN] image KH-5 [Termes IGN] modélisation géométrique de prise de vue [Termes IGN] numérisation de photographie [Termes IGN] point d'appui Résumé : (auteur) Long-term observations of the Antarctic ice sheet will contribute to a quantitative evaluation and precise prediction of the sea level change induced by global changes in climate. This paper proposes an improved rigorous geometric modeling method for the declassified KH-5 ARGON satellite images collected in Antarctica in 1960s. The scanned film images are preprocessed beforehand to enhance the quality for further analysis. Systematic errors such as lens distortion and atmospheric refraction are also considered and corrected. A scheme is proposed to measure the ground control points for the historical images based on modern image mosaic and DEM products. The bundle adjustment results of four blocks in regions in East Antarctica present a geometric positioning accuracy of less than one nominal pixel resolution (140 m) in both horizontal and vertical directions, outperforming the published results. A regional DEM of the ice sheet that represents the topography in 1963 is then generated from the stereo ARGON images for the first time, the evaluation of which shows its consistency with the modern product but with great value for studying the recent change history of the ice sheet. Numéro de notice : A2017-432 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.7.477 En ligne : https://doi.org/10.14358/PERS.83.7.477 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86336 [article]

Extrapolated georeferencing of high-resolution satellite imagery based on the strip constraint / Jinshan Cao in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 7 (July 2017) . - pp 493 - 499 Titre : Extrapolated georeferencing of high-resolution satellite imagery based on the strip constraint Type de document : Article/Communication Auteurs : Jinshan Cao, Auteur ; Xiuxiao Yuan, Auteur ; Jianya Gong, Auteur ; Miaozhong Xu, Auteur Année de publication : 2017 Article en page(s) : pp 493 - 499 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] compensation [Termes IGN] géoréférencement indirect [Termes IGN] image à haute résolution [Termes IGN] image satellite [Termes IGN] image ZiYuan-3 [Termes IGN] modèle relationnel [Termes IGN] point d'appui Résumé : (auteur) Ground control points (GCPs) are necessary in order to achieve precise georeferencing of high-resolution satellite (HRS) imagery. However, measuring GCPs is costly, laborious, and time consuming. In some remote areas, we cannot even obtain well-defined GCPs. In this study, a strip constraint model is established. Based on the bias-compensated rational function model and the strip constraint model, a feasible extrapolated georeferencing approach for HRS imagery is presented. The presented approach remains effective even when the intermediate images in the strip are unavailable. Experimental results of the two ZiYuan-3 (ZY-3) nadir datasets show that the direct georeferencing accuracy of the ZY-3 nadir images reaches only 9 to 12 pixels. With four GCPs in the first image, the georeferencing accuracy of the other images in the strip is improved to better than 2 pixels through extrapolated georeferencing. Numéro de notice : A2017-433 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.7.493 En ligne : https://doi.org/10.14358/PERS.83.7.493 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86337 [article]

Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 7 (July 2017) . - pp 501 - 507 Titre : Northern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle Type de document : Article/Communication Auteurs : Steven E. Franklin, Auteur ; Oumer S. Ahmed, Auteur ; Griffin Williams, Auteur Année de publication : 2017 Article en page(s) : pp 501 - 507 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image orientée objet [Termes IGN] Canada [Termes IGN] classification automatique [Termes IGN] drone [Termes IGN] espèce végétale [Termes IGN] image aérienne [Termes IGN] image multibande [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Ontario (Canada) [Termes IGN] Pinophyta [Termes IGN] semis de points Résumé : (auteur) Object-based image analysis and machine learning classification procedures, after field calibration and photogrammetric processing of consumer-grade unmanned aerial vehicle (UAV) digital camera data, were implemented to classify tree species in a conifer forest in the Great Lakes/St Lawrence Lowlands Ecoregion, Ontario, Canada. A red-green-blue (RGB) digital camera yielded approximately 72 percent classification accuracy for three commercial tree species and one conifer shrub. Accuracy improved approximately 15 percent, to 87 percent overall, with higher radiometric quality data acquired separately using a digital camera that included near infrared observations (at a lower spatial resolution). Interpretation of the point cloud, spectral, texture and object (tree crown) classification Variable Importance (VI) selected by a machine learning algorithm suggested a good correspondence with the traditional aerial photointerpretation cues used in the development of well-established large-scale photography northern conifer elimination keys, which use three-dimensional crown shape, spectral response (tone), texture derivatives to quantify branching characteristics, and crown size, development and outline features. These results suggest that commonly available consumer-grade UAV-based digital cameras can be used with object-based image analysis to obtain acceptable conifer species classification accuracy to support operational forest inventory applications. Numéro de notice : A2017-434 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.83.7.501 En ligne : https://doi.org/10.14358/PERS.83.7.501 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86338 [article]