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An integrated approach to registration and fusion of hyperspectral and multispectral images / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3020 - 3033 Titre : An integrated approach to registration and fusion of hyperspectral and multispectral images Type de document : Article/Communication Auteurs : Yuan Zhou, Auteur ; Anand Rangarajan, Auteur ; Paul D. Gader, Auteur Année de publication : 2020 Article en page(s) : pp 3020 - 3033 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] algorithme de fusion [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] méthode des moindres carrés [Termes descripteurs IGN] points registration [Termes descripteurs IGN] tâche image d'un point Résumé : (auteur) Combining a hyperspectral (HS) image and a multispectral (MS) image—an example of image fusion—can result in a spatially and spectrally high-resolution image. Despite the plethora of fusion algorithms in remote sensing, a necessary prerequisite, namely registration, is mostly ignored. This limits their application to well-registered images from the same source. In this article, we propose and validate an integrated registration and fusion approach (code available at https://github.com/zhouyuanzxcv/Hyperspectral ). The registration algorithm minimizes a least-squares (LSQ) objective function with the point spread function (PSF) incorporated together with a nonrigid freeform transformation applied to the HS image and a rigid transformation applied to the MS image. It can handle images with significant scale differences and spatial distortion. The fusion algorithm takes the full high-resolution HS image as an unknown in the objective function. Assuming that the pixels lie on a low-dimensional manifold invariant to local linear transformations from spectral degradation, the fusion optimization problem leads to a closed-form solution. The method was validated on the Pavia University, Salton Sea, and the Mississippi Gulfport datasets. When the proposed registration algorithm is compared to its rigid variant and two mutual information-based methods, it has the best accuracy for both the nonrigid simulated dataset and the real dataset, with an average error less than 0.15 pixels for nonrigid distortion of maximum 1 HS pixel. When the fusion algorithm is compared with current state-of-the-art algorithms, it has the best performance on images with registration errors as well as on simulations that do not consider registration effects. Numéro de notice : A2020-231 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94969 [article]

A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions / Shahryar K. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2471 - 2480 Titre : A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions Type de document : Article/Communication Auteurs : Shahryar K. Ahmad, Auteur ; Faisal Hossain, Auteur ; Hisham Eldardiry, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2471 - 2480 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Bangladesh [Termes descripteurs IGN] climat tropical [Termes descripteurs IGN] eau de surface [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] image proche infrarouge [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] plan d'eau [Termes descripteurs IGN] radar à antenne synthétique [Termes descripteurs IGN] reconnaissance de surface [Termes descripteurs IGN] surveillance hydrologique [Termes descripteurs IGN] télédétection spatiale [Termes descripteurs IGN] zone humide Résumé : (auteur) Consistent estimation of water surface area from remote sensing remains challenging in regions such as South Asia with vegetation, mountainous topography, and persistent monsoonal cloud cover. High-resolution optical imagery, which is often used for global inundation mapping, is highly impacted by clouds, while synthetic aperture radar (SAR) imagery is not impacted by clouds and is affected by both topographic layover and vegetation. Here, we compare and contrast inundation extent measurements from visible (Landsat-8 and Sentinel-2) and SAR (Sentinel-1) imagery. Each data type (wavelength) has complementary strengths and weaknesses which were gauged separately over selected water bodies in Bangladesh. High-resolution cloud-free PlanetScope imagery at 3-m resolution was used as a reference to check the accuracy of each technique and data type. Next, the optical and radar images were fused for a rule-based water area classification algorithm to derive the optimal decision for the water mask. Results indicate that the fusion approach can improve the overall accuracy by up to 3.8%, 18.2%, and 8.3% during the wet season over using the individual products of Landsat8, Sentinel-1, and Sentinel-2, respectively, at three sites, while providing increased observational frequency. The fusion-derived products resulted in overall accuracy ranging from 85.8% to 98.7% and Kappa coefficient varying from 0.61 to 0.83. The proposed SAR-visible fusion technique has potential for improving satellite-based surface water monitoring and storage changes, especially for smaller water bodies in humid tropical climate of South Asia. Numéro de notice : A2020-198 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2950705 date de publication en ligne : 19/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2950705 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94868 [article]

Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 158 (December 2019) Titre : Combining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture Type de document : Article/Communication Auteurs : Dino Lenco, Auteur ; Roberto Interdonato, Auteur ; Raffaele Gaetano, Auteur ; Ho Tong Minh Dinh, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Burkina Faso [Termes descripteurs IGN] carte de la végétation [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] occupation du sol [Termes descripteurs IGN] Réunion, île de la [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] utilisation du sol Résumé : (auteur) The huge amount of data currently produced by modern Earth Observation (EO) missions has allowed for the design of advanced machine learning techniques able to support complex Land Use/Land Cover (LULC) mapping tasks. The Copernicus programme developed by the European Space Agency provides, with missions such as Sentinel-1 (S1) and Sentinel-2 (S2), radar and optical (multi-spectral) imagery, respectively, at 10 m spatial resolution with revisit time around 5 days. Such high temporal resolution allows to collect Satellite Image Time Series (SITS) that support a plethora of Earth surface monitoring tasks. How to effectively combine the complementary information provided by such sensors remains an open problem in the remote sensing field. In this work, we propose a deep learning architecture to combine information coming from S1 and S2 time series, namely TWINNS (TWIn Neural Networks for Sentinel data), able to discover spatial and temporal dependencies in both types of SITS. The proposed architecture is devised to boost the land cover classification task by leveraging two levels of complementarity, i.e., the interplay between radar and optical SITS as well as the synergy between spatial and temporal dependencies. Experiments carried out on two study sites characterized by different land cover characteristics (i.e., the Koumbia site in Burkina Faso and Reunion Island, a overseas department of France in the Indian Ocean), demonstrate the significance of our proposal. Numéro de notice : A2019-544 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.016 date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94186 [article]

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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]

Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 / Ali Mokhtari in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 154 (August 2019) . - pp 231 - 245 Titre : Calculating potential evapotranspiration and single crop coefficient based on energy balance equation using Landsat 8 and Sentinel-2 Type de document : Article/Communication Auteurs : Ali Mokhtari, Auteur ; Hamideh Noory, Auteur ; Farrokh Pourshakouri, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 231 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bilan énergétique [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] cultures [Termes descripteurs IGN] évapotranspiration [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] image MODIS [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] orge (céréale) [Termes descripteurs IGN] Téhéran Résumé : (Auteur) Evapotranspiration is considered to be an important component of allocating water to agricultural sector; therefore, the more accurate this parameter is, the more optimized the water use can be. This study was conducted in order to evaluate the Landsat 8 and Sentinel-2 data (A and B), both separately and combined, in potential evapotranspiration (ETp) and single crop coefficient (Kc) estimations. Field measurements such as crop height, leaf area index (LAI), land surface temperature (LST), air temperature above canopy (AT), and spectral data were exploited in the evaluating process throughout the entirety of 2017–18 growing season under winter wheat and barley cultivations in the Agricultural Research Farms of the University of Tehran. The novel method of Multi-Sensor Data Fusion using the Priestly-Taylor equation was taken into practice for satellite-based ETp (MSDF-ET) calculation from the combination of MODIS thermal and Landsat 8 and Sentinel-2 multispectral data. Thermal images were downscaled by the means of the TsHARP algorithm. Thus, prior to ETp calculation, the thermal sharpening algorithm calculated using different spectral indices (SI) was assessed. The SI included NDVI, SAVI, SR, NDWI, NDWIg, and LSWI. The subsequent results were representative of the LSWI qualification under both Landsat 8 and Sentinel-2 conditions against thermal and spectral measurements. Also the satellite-based ETp strongly correlated with the ETp derived from the field data illuminating the promising accuracy of the MSDF-ET method in both Landsat 8 and Sentinel-2 data. In the end, the time series of Kc obtained from the combination of satellites were fairly indicative of the real-world variations under different vegetation cover and crop growth stages. Overall, using Landsat 8 and Sentinel-2 products in integration with each other could significantly result in more reliable decisions in agricultural water resources management. Numéro de notice : A2019-270 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.06.011 date de publication en ligne : 24/06/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.011 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93088 [article]

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Combining spatiotemporal fusion and object-based image analysis for improving wetland mapping in complex and heterogeneous urban landscapes / Meng Zhang in Geocarto international, vol 34 n° 10 ([15/07/2019])  

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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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Evaluating SAR-optical sensor fusion for aboveground biomass estimation in a Brazilian tropical forest / Aline Bernarda Debastiani in Annals of forest research, vol 62 n° 1 (January - June 2019)  

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Exploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)  

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Learning to Understand Remote Sensing Images, 1. Volume 1 / Qi Wang (2019)  

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Learning to Understand Remote Sensing Images, 2. Volume 2 / Qi Wang (2019)  

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Scalable Evaluation of 3D City Models / Oussama Ennafii (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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