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HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion / Kun Li in ISPRS Journal of photogrammetry and remote sensing, vol 188 (June 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 188 (June 2022) . - pp 30 - 44 Titre : HyperNet: A deep network for hyperspectral, multispectral, and panchromatic image fusion Type de document : Article/Communication Auteurs : Kun Li, Auteur ; Wei Zhang, Auteur ; Dian Yu, Auteur ; Xin Tian, Auteur Année de publication : 2022 Article en page(s) : pp 30 - 44 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image floue [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] réseau neuronal profond Résumé : (Auteur) Traditional approaches mainly fuse a hyperspectral image (HSI) with a high-resolution multispectral image (MSI) to improve the spatial resolution of the HSI. However, such improvement in the spatial resolution of HSIs is still limited because the spatial resolution of MSIs remains low. To further improve the spatial resolution of HSIs, we propose HyperNet, a deep network for the fusion of HSI, MSI, and panchromatic image (PAN), which effectively injects the spatial details of an MSI and a PAN into an HSI while preserving the spectral information of the HSI. Thus, we design HyperNet on the basis of a uniform fusion strategy to solve the problem of complex fusion of three types of sources (i.e., HSI, MSI, and PAN). In particular, the spatial details of the MSI and the PAN are extracted by multiple specially designed multiscale-attention-enhance blocks in which multi-scale convolution is used to adaptively extract features from different reception fields, and two attention mechanisms are adopted to enhance the representation capability of features along the spectral and spatial dimensions, respectively. Through the capability of feature reuse and interaction in a specially designed dense-detail-insertion block, the previously extracted features are subsequently injected into the HSI according to the unidirectional feature propagation among the layers of dense connection. Finally, we construct an efficient loss function by integrating the multi-scale structural similarity index with the norm, which drives HyperNet to generate high-quality results with a good balance between spatial and spectral qualities. Extensive experiments on simulated and real data sets qualitatively and quantitatively demonstrate the superiority of HyperNet over other state-of-the-art methods. Numéro de notice : A2022-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.04.001 Date de publication en ligne : 07/04/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.04.001 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100461 [article]

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PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data / Qi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 123 - 139 Titre : PolGAN: A deep-learning-based unsupervised forest height estimation based on the synergy of PolInSAR and LiDAR data Type de document : Article/Communication Auteurs : Qi Zhang, Auteur ; Linlin Ge, Auteur ; Scott Hensley, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 123 - 139 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes IGN] analyse discriminante [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] bande L [Termes IGN] données lidar [Termes IGN] forêt boréale [Termes IGN] forêt tropicale [Termes IGN] Global Ecosystem Dynamics Investigation lidar [Termes IGN] hauteur de la végétation [Termes IGN] hauteur des arbres [Termes IGN] image captée par drone [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] polarimétrie radar [Termes IGN] pouvoir de résolution géométrique [Termes IGN] réseau antagoniste génératif [Termes IGN] semis de points Résumé : (auteur) This paper describes a deep-learning-based unsupervised forest height estimation method based on the synergy of the high-resolution L-band repeat-pass Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) and low-resolution large-footprint full-waveform Light Detection and Ranging (LiDAR) data. Unlike traditional PolInSAR-based methods, the proposed method reformulates the forest height inversion as a pan-sharpening process between the low-resolution LiDAR height and the high-resolution PolSAR and PolInSAR features. A tailored Generative Adversarial Network (GAN) called PolGAN with one generator and dual (coherence and spatial) discriminators is proposed to this end, where a progressive pan-sharpening strategy underpins the generator to overcome the significant difference between spatial resolutions of LiDAR and SAR-related inputs. Forest height estimates with high spatial resolution and vertical accuracy are generated through a continuous generative and adversarial process. UAVSAR PolInSAR and LVIS LiDAR data collected over tropical and boreal forest sites are used for experiments. Ablation study is conducted over the boreal site evidencing the superiority of the progressive generator with dual discriminators employed in PolGAN (RMSE: 1.21 m) in comparison with the standard generator with dual discriminators (RMSE: 2.43 m) and the progressive generator with a single coherence (RMSE: 2.74 m) or spatial discriminator (RMSE: 5.87 m). Besides that, by reducing the dependency on theoretical models and utilizing the shape, texture, and spatial information embedded in the high-spatial-resolution features, the PolGAN method achieves an RMSE of 2.37 m over the tropical forest site, which is much more accurate than the traditional PolInSAR-based Kapok method (RMSE: 8.02 m). Numéro de notice : A2022-195 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.02.008 Date de publication en ligne : 17/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.02.008 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99962 [article]

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A novel unmixing-based hypersharpening method via convolutional neural network / Xiaochen Lu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 5503614 Titre : A novel unmixing-based hypersharpening method via convolutional neural network Type de document : Article/Communication Auteurs : Xiaochen Lu, Auteur ; Tong Li, Auteur ; Junping Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5503614 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] pansharpening (fusion d'images) [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale Résumé : (auteur) Hypersharpening (namely, hyperspectral (HS) and multispectral (MS) image fusion) aims at enhancing the spatial resolution of HS image via an auxiliary higher resolution MS image. Currently, numerous hypersharpening methods are proposed successively, among which the unmixing-based approaches have been widely researched and demonstrated their effectiveness in the spectral fidelity aspect. However, existing unmixing-based fusion methods substantially employ mathematical techniques to solve the spectral mixture model, without taking full advantage of the collaborative spatial–spectral information that is usually helpful for abundance estimation improvement. To overcome this drawback, in this article, a novel unmixing-based HS and MS image fusion method, via a convolutional neural network (CNN), is proposed to promote spectral fidelity. The main idea of this work is to use CNN to fully explore the spatial information and the spectral information of both HS and MS images simultaneously, thereby enhancing the accuracy of estimating the abundance maps. Experiments on four simulated and real remote sensing data sets demonstrate that the proposed method is beneficial to the spectral fidelity of the fused images compared with some state-of-the-art algorithms. Meanwhile, it is also easy to implement and has a certain advantage in running time. Numéro de notice : A2022-028 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3063105 Date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3063105 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99264 [article]

A novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery / Massimiliano Pepe in ISPRS International journal of geo-information, vol 10 n° 10 (October 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 10 (October 2021) . - n° 697 Titre : A novel method based on deep learning, GIS and geomatics software for building a 3D city model from VHR satellite stereo imagery Type de document : Article/Communication Auteurs : Massimiliano Pepe, Auteur ; Domenica Costantino, Auteur ; Vincenzo Saverio Alfio, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 697 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme de Gram-Schmidt [Termes IGN] apprentissage profond [Termes IGN] ArcGIS [Termes IGN] détection du bâti [Termes IGN] empreinte [Termes IGN] hauteur du bâti [Termes IGN] image à très haute résolution [Termes IGN] image Worldview [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] modèle numérique de surface [Termes IGN] Oman [Termes IGN] pansharpening (fusion d'images) [Termes IGN] reconnaissance automatique [Termes IGN] système d'information géographique Résumé : (auteur) The aim of the paper is to identify a suitable method for the construction of a 3D city model from stereo satellite imagery. In order to reach this goal, it is necessary to build a workflow consisting of three main steps: (1) Increasing the geometric resolution of the color images through the use of pan-sharpening techniques, (2) identification of the buildings’ footprint through deep-learning techniques and, finally, (3) building an algorithm in GIS (Geographic Information System) for the extraction of the elevation of buildings. The developed method was applied to stereo imagery acquired by WorldView-2 (WV-2), a commercial Earth-observation satellite. The comparison of the different pan-sharpening techniques showed that the Gram–Schmidt method provided better-quality color images than the other techniques examined; this result was deduced from both the visual analysis of the orthophotos and the analysis of quality indices (RMSE, RASE and ERGAS). Subsequently, a deep-learning technique was applied for pan sharpening an image in order to extract the footprint of buildings. Performance indices (precision, recall, overall accuracy and the F1measure) showed an elevated accuracy in automatic recognition of the buildings. Finally, starting from the Digital Surface Model (DSM) generated by satellite imagery, an algorithm built in the GIS environment allowed the extraction of the building height from the elevation model. In this way, it was possible to build a 3D city model where the buildings are represented as prismatic solids with flat roofs, in a fast and precise way. Numéro de notice : A2021-801 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10100697 Date de publication en ligne : 14/10/2021 En ligne : https://doi.org/10.3390/ijgi10100697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98853 [article]

Hyperspectral image fusion and multitemporal image fusion by joint sparsity / Han Pan in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 9 (September 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 9 (September 2021) . - pp 7887 - 7900 Titre : Hyperspectral image fusion and multitemporal image fusion by joint sparsity Type de document : Article/Communication Auteurs : Han Pan, Auteur ; Zhongliang Jing, Auteur ; Henry Leung, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7887 - 7900 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] correction d'image [Termes IGN] flou [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multitemporelle [Termes IGN] image panchromatique [Termes IGN] pansharpening (fusion d'images) [Termes IGN] représentation parcimonieuse Résumé : (auteur) Different image fusion systems have been developed to deal with the massive amounts of image data for different applications, such as remote sensing, computer vision, and environment monitoring. However, the generalizability and versatility of these fusion systems remain unknown. This article proposes an efficient regularization framework to achieve different kinds of fusion tasks accounting for the spatiospectral and spatiotemporal variabilities of the fusion process. A joint minimization functional is developed by taking an advantage of a composite regularizer for enforcing joint sparsity in the gradient domain and the frame domain. The proposed composite regularizer is composed of the Hessian Schatten-norm regularization and contourlet-based regularization terms. The resulting problems are solved by the alternating direction method of multipliers (ADMM). The effectiveness of the proposed method is validated in a variety of image fusion experiments: 1) hyperspectral (HS) and panchromatic image fusion; 2) HS and multispectral image fusion; 3) multitemporal image fusion (MIF); and 4) multi-image deblurring. Results show promising performance compared with state-of-the-art fusion methods. Numéro de notice : A2021-649 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3039046 Date de publication en ligne : 07/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3039046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98360 [article]

Detail injection-based deep convolutional neural networks for pansharpening / Liang-Jian Deng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

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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)  

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

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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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Amélioration des résolutions spatiale et spectrale d’images satellitaires par réseaux antagonistes / Anaïs Gastineau (2021)  

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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)  

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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)  

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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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Application of geographic Information system and remote sensing in multiple criteria analysis to identify priority areas for biodiversity conservation in Vietnam / Xuan Dinh Vu (2020)  

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