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GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)
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Titre : GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening Type de document : Article/Communication Auteurs : Hao Zhang, Auteur ; Jiayi Ma, Auteur Année de publication : 2021 Article en page(s) : pp 223 - 239 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] apprentissage profond
[Termes descripteurs IGN] classification dirigée
[Termes descripteurs IGN] fusion d'images
[Termes descripteurs IGN] gradient
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] image panchromatique
[Termes descripteurs IGN] Normalized Difference Vegetation Index
[Termes descripteurs IGN] pansharpening (fusion d'images)
[Termes descripteurs IGN] régressionRésumé : (auteur) Pansharpening aims to fuse low-resolution multi-spectral image and high-resolution panchromatic (PAN) image to produce a high-resolution multi-spectral (HRMS) image. In this paper, a new residual learning network based on gradient transformation prior, termed as GTP-PNet, is proposed to generate the high-quality HRMS image with accurate spectral distribution as well as reasonable spatial structure. Different from previous deep models that only rely on supervision of the HRMS reference image, we introduce the gradient transformation prior to the deep model, so as to improve the solution accuracy. Our model consists of two networks, namely gradient transformation network (TNet) and pansharpening network (PNet). TNet is committed to seeking the nonlinear mapping between gradients of PAN and HRMS images, which is essentially a spatial relationship regression of imaging bands in different ranges. PNet is the residual learning network used to generate the HRMS image, which is not only supervised by the HRMS reference image, but also constrained by the trained TNet. As a result, the HRMS image generated by PNet not only approximates the HRMS reference image in the spectral distribution, but also conforms to the gradient transformation prior in the spatial structure. Experimental results demonstrate the significant superiority of our method over the current state-of-the-arts in terms of both subjective visual effect and quantitative metrics. We also apply our method to produce the HR normalized difference vegetation index in remote sensing, which can achieve the best performance. Moreover, our method is much competitive compared with the state-of-the-art alternatives in running efficiency. Numéro de notice : A2021-089 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.014 date de publication en ligne : 11/01/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.12.014 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96859
in ISPRS Journal of photogrammetry and remote sensing > Vol 172 (February 2021) . - pp 223 - 239[article]A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery / Farzaneh Dadrass Javan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)
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Titre : A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery Type de document : Article/Communication Auteurs : Farzaneh Dadrass Javan, Auteur ; Farhad Samadzadegan, Auteur ; Soroosh Mehravar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 101 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] affinage d'image
[Termes descripteurs IGN] analyse de variance
[Termes descripteurs IGN] fusion d'images
[Termes descripteurs IGN] image Kompsat
[Termes descripteurs IGN] image à haute résolution
[Termes descripteurs IGN] image Geoeye
[Termes descripteurs IGN] image Ikonos
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] image panchromatique
[Termes descripteurs IGN] image Pléiades-HR
[Termes descripteurs IGN] image Quickbird
[Termes descripteurs IGN] image Worldview
[Termes descripteurs IGN] netteté
[Termes descripteurs IGN] pansharpening (fusion d'images)
[Termes descripteurs IGN] pouvoir de résolution spectraleRésumé : (auteur) Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), and Hybrid and were tested on a collection of 21 case studies. These include images from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with the aerial platform, and Deimos-2. Neural network-based methods were excluded due to their substantial computational requirements for operational mapping purposes. The methods were evaluated based on four Spectral and three Spatial quality metrics. An Analysis Of Variance (ANOVA) was used to statistically compare the pan-sharpening categories. Results indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based methods had the highest spatial quality and CS-based methods had the lowest results both spectrally and spatially. The revisited version of the Additive Wavelet Luminance Proportional Pan-sharpening method had the highest spectral quality, whereas Generalized IHS with Best Trade-off Parameter with Additive Weights showed the highest spatial quality. CS-based methods generally had the fastest run-time, whereas the majority of methods belonging to MRA and VO categories had relatively long run times. Numéro de notice : A2021-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.001 date de publication en ligne : 21/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96418
in ISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 101 - 117[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021011 SL Revue Centre de documentation Revues en salle Disponible 081-2021013 DEP-RECP Revue MATIS Dépôt en unité Exclu du prêt 081-2021012 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt fusionImage: An R package for pan‐sharpening images in open source software / Fulgencio Cánovas‐García in Transactions in GIS, Vol 24 n° 5 (October 2020)
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Titre : fusionImage: An R package for pan‐sharpening images in open source software Type de document : Article/Communication Auteurs : Fulgencio Cánovas‐García, Auteur ; Paúl Pesántez‐Cobos, Auteur ; Francisco Alonso‐Sarría, Auteur Année de publication : 2020 Article en page(s) : pp 1185-1207 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] algorithme de Gram-Schmidt
[Termes descripteurs IGN] analyse en composantes principales
[Termes descripteurs IGN] filtre passe-haut
[Termes descripteurs IGN] logiciel libre
[Termes descripteurs IGN] pansharpening (fusion d'images)
[Termes descripteurs IGN] pouvoir de résolution géométrique
[Termes descripteurs IGN] R (langage)Résumé : (Auteur) The objective of this article is to evaluate the performance of three pan‐sharpening algorithms (high‐pass filter, principal component analysis and Gram–Schmidt) to increase the spatial resolution of five types of multispectral images and to evaluate the results in terms of color, coherence and spatial sharpness, both qualitatively and quantitatively. A secondary objective is to present an implementation of the aforementioned pan‐sharpening techniques within the open source software R. From a qualitative point of view, pan‐sharpening of images with a high spatial resolution ratio give better results than those whose spatial resolution ratio is 2. According to the quantitative evaluation, there is no pan‐sharpening methodology that obtains optimal results simultaneously for all types of images used. The results of the spectral and spatial ERGAS index vary for four out of the five types of images analyzed. The results show that none of the methods implemented in this work can be considered a priori better than the others. At the same time, this work indicates the importance of both qualitative and quantitative assessment. Numéro de notice : A2020-499 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12676 date de publication en ligne : 15/09/2020 En ligne : https://doi.org/10.1111/tgis.12676 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96206
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1185-1207[article]Comparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)
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Titre : Comparison of tree-based classification algorithms in mapping burned forest areas Type de document : Article/Communication Auteurs : Dilek Kucuk Matci, Auteur ; Resul Comert, Auteur ; Ugur Avdan, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] analyse comparative
[Termes descripteurs IGN] analyse d'image orientée objet
[Termes descripteurs IGN] bassin méditerranéen
[Termes descripteurs IGN] carte forestière
[Termes descripteurs IGN] classification dirigée
[Termes descripteurs IGN] classification par arbre de décision
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] image Landsat-8
[Termes descripteurs IGN] incendie de forêt
[Termes descripteurs IGN] matrice de confusion
[Termes descripteurs IGN] pansharpening (fusion d'images)
[Termes descripteurs IGN] Rotation Forest classification
[Termes descripteurs IGN] Turquie
[Termes descripteurs IGN] zone sinistréeRésumé : (auteur) In this study, we compared the performance of tree-based classification algorithms – Random Forest (RF), Rotation Forest (RotF), J48, The Alternating Decision Tree (ADTree), Forest by Penalising Attributes (Forest PA), Logical Analysis of Data Algorithm (LADTree) and Functional Trees (FT) – for mapping burned forest areas within the Mediterranean region in Turkey. Object-based image analysis (OBIA) was performed to pan-sharpened the Landsat 8 images. Four different burned areas, namely Kumluca, Adrasan, Anamur, and Alanya, were used as study areas. Kumluca, Anamur, and Alanya regions were used as training areas, and Adrasan region was used as the test area. Obtained results were evaluated with confusion matrix and statistically significant analysis. According to the results, FT and RotF produced more accurate results than other algorithms. Also, the results obtained with these algorithms are statistically significant. Numéro de notice : A2020-626 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2020.03.348-360 date de publication en ligne : 23/08/2020 En ligne : https://doi.org/10.15292/geodetski-vestnik.2020.03.348-360 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96087
in Geodetski vestnik > vol 64 n° 3 (September - November 2020) . - 13 p.[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 139-2020031 SL Revue Centre de documentation Revues en salle Disponible Pansharpening: context-based generalized Laplacian pyramids by robust regression / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 9 (September 2020)
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Titre : Pansharpening: context-based generalized Laplacian pyramids by robust regression Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Stefano Marano, Auteur ; Jocelyn Chanussot, Auteur Année de publication : 2020 Article en page(s) : pp 6152 - 6167 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes descripteurs IGN] analyse multirésolution
[Termes descripteurs IGN] fonction de transfert de modulation
[Termes descripteurs IGN] fusion d'images
[Termes descripteurs IGN] image multibande
[Termes descripteurs IGN] image panchromatique
[Termes descripteurs IGN] lissage de données
[Termes descripteurs IGN] Normalized Difference Vegetation Index
[Termes descripteurs IGN] pansharpening (fusion d'images)
[Termes descripteurs IGN] régression
[Termes descripteurs IGN] transformation en ondelettesRésumé : (auteur) Pansharpening refers to the combination of panchromatic (PAN) and multispectral (MS) images, designed to obtain a fused product retaining the fine spatial resolution of the former and the high spectral content of the latter. One of the most popular and successful approaches to pansharpening is the method known as context-based generalized Laplacian pyramid, which requires as a key ingredient for the estimation of the so-called injection coefficients. In this article, we propose the adoption of robust techniques for the estimation of the injection coefficients and detection strategies to select the clusters for which robust regression is needed, providing a suitable balancing between fusion performance and computational burden. Experimental results conducted on five real data sets acquired by the sensors QuickBird, WorldView-3, and WorldView-4, show the superiority of the proposed method with respect to current state-of-the-art pansharpening techniques. Numéro de notice : A2020-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2974806 date de publication en ligne : 04/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2974806 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95706
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 9 (September 2020) . - pp 6152 - 6167[article]Tree species classification in tropical forests using visible to shortwave infrared WorldView-3 images and texture analysis / Matheus Pinheiro Ferreira in ISPRS Journal of photogrammetry and remote sensing, vol 149 (March 2019)
PermalinkSynergetic efficiency of Lidar and WorldView-2 for 3D urban cartography in Northeast Mexico / Fabiola D. Yepez-Rincon in Geocarto international, vol 34 n° 2 ([01/02/2019])
PermalinkSuper-resolution of Sentinel-2 images : Learning a globally applicable deep neural network / Charis Lanaras in ISPRS Journal of photogrammetry and remote sensing, vol 146 (December 2018)
PermalinkPan-sharpening via deep metric learning / Yinghui Xing in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)
PermalinkSensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)
PermalinkIntersensor 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)
PermalinkDescribing contrast across scales / Sohaib Ali Syed in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)
PermalinkPan-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)
PermalinkEvaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)
PermalinkPan-sharpening quality investigation of PLÉIADES-1A images / Mustafa Ozendi in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
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