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Unsupervised pansharpening based on self-attention mechanism / Ying Qu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3192 - 3208 Titre : Unsupervised pansharpening based on self-attention mechanism Type de document : Article/Communication Auteurs : Ying Qu, Auteur ; Razieh Kaviani Baghbaderani, Auteur ; Hairong Qi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3192 - 3208 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] pouvoir de résolution géométrique [Termes descripteurs IGN] précision infrapixellaire [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Pansharpening is to fuse a multispectral image (MSI) of low-spatial-resolution (LR) but rich spectral characteristics with a panchromatic image (PAN) of high spatial resolution (HR) but poor spectral characteristics. Traditional methods usually inject the extracted high-frequency details from PAN into the upsampled MSI. Recent deep learning endeavors are mostly supervised assuming that the HR MSI is available, which is unrealistic especially for satellite images. Nonetheless, these methods could not fully exploit the rich spectral characteristics in the MSI. Due to the wide existence of mixed pixels in satellite images where each pixel tends to cover more than one constituent material, pansharpening at the subpixel level becomes essential. In this article, we propose an unsupervised pansharpening (UP) method in a deep-learning framework to address the abovementioned challenges based on the self-attention mechanism (SAM), referred to as UP-SAM. The contribution of this article is threefold. First, the SAM is proposed where the spatial varying detail extraction and injection functions are estimated according to the attention representations indicating spectral characteristics of the MSI with subpixel accuracy. Second, such attention representations are derived from mixed pixels with the proposed stacked attention network powered with a stick-breaking structure to meet the physical constraints of mixed pixel formulations. Third, the detail extraction and injection functions are spatial varying based on the attention representations, which largely improves the reconstruction accuracy. Extensive experimental results demonstrate that the proposed approach is able to reconstruct sharper MSI of different types, with more details and less spectral distortion compared with the state-of-the-art. Numéro de notice : A2021-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3009207 date de publication en ligne : 23/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3009207 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97394 [article]

Pan-sharpening via multiscale dynamic convolutional neural network / Jianwen Hu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2231 - 2244 Titre : Pan-sharpening via multiscale dynamic convolutional neural network Type de document : Article/Communication Auteurs : Jianwen Hu, Auteur ; Pei Hu, Auteur ; Xudong Kang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2231 - 2244 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] données multiéchelles [Termes descripteurs IGN] image Geoeye [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] reconstruction d'image Résumé : (Auteur) Pan-sharpening is an effective method to obtain high-resolution multispectral images by fusing panchromatic (PAN) images with fine spatial structure and low-resolution multispectral images with rich spectral information. In this article, a multiscale pan-sharpening method based on dynamic convolutional neural network is proposed. The filters in dynamic convolution are generated dynamically and locally by the filter generation network which is different from the standard convolution and strengthens the adaptivity of the network. The dynamic filters are adaptively changed according to the input images. The proposed multiscale dynamic convolutions extract detail feature of PAN image at different scales. Multiscale network structure is beneficial to obtain effective detail features. The weights obtained by the weight generation network are used to adjust the relationship among the detail features in each scale. The GeoEye-1, QuickBird, and WorldView-3 data are used to evaluate the performance of the proposed method. Compared with the widely used state-of-the-art pan-sharpening approaches, the experimental results demonstrate the superiority of the proposed method in terms of both objective quality indexes and visual performance. Numéro de notice : A2021-216 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3007884 date de publication en ligne : 16/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3007884 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97206 [article]

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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 223 - 239 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] régression Résumé : (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 [article]

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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 101 - 117 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 spectrale Résumé : (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 [article]

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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)  

Public [article]  inTransactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1185-1207 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 [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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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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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)  

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Synergetic 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])  

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Super-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)  

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

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Sensitivity analysis of pansharpening in hyperspectral change detection / Seyd Teymoor Seydi in Applied geomatics, vol 10 n° 1 (March 2018)  

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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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Describing contrast across scales / Sohaib Ali Syed in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 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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