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Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 915 - 928 Titre : Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach Type de document : Article/Communication Auteurs : Frank S. Marzano, Auteur ; Michele Iacobelli, Auteur ; Massimo Orlandi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 915 - 928 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Adriatique, mer [Termes IGN] bathymétrie [Termes IGN] chlorophylle [Termes IGN] correction atmosphérique [Termes IGN] couleur de l'océan [Termes IGN] eaux côtières [Termes IGN] image Sentinel-MSI [Termes IGN] incertitude spectrale [Termes IGN] matière organique [Termes IGN] Méditerranée, mer [Termes IGN] réseau neuronal artificiel Résumé : (auteur) Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination R2 up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg m−3 , using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with R2=0.52 and RMSE = 1.95 g/m 3 using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores. Numéro de notice : A2021-110 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2980941 Date de publication en ligne : 09/12/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2980941 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96912 [article]

Optimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale / Linling Tang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 962 - 978 Titre : Optimization of multi-ecosystem model ensembles to simulate vegetation growth at the global scale Type de document : Article/Communication Auteurs : Linling Tang, Auteur ; Qian Lei, Auteur ; Weizhe Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 962 - 978 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] croissance végétale [Termes IGN] écosystème [Termes IGN] estimation bayesienne [Termes IGN] Leaf Area Index [Termes IGN] modèle de simulation [Termes IGN] optimisation (mathématiques) [Termes IGN] optimisation par essaim de particules Résumé : (auteur) Process-based ecosystem models are increasingly used to simulate the effects of a changing environment on vegetation growth in the past, present, and future. To improve the simulation, the multimodel ensemble mean (MME) and ensemble Bayesian model averaging (EBMA) methods are often used in optimizing the integration of ecosystem model ensemble. These two methods were compared with four other optimization techniques, including genetic algorithm (GA), particle swarm optimization (PSO), cuckoo search (CS), and interior-point method (IPM), to evaluate their efficiency in this article. Here, we focused on eight commonly used ecosystem models to simulate vegetation growth, represented by the growing season leaf area index (LAIgs), collected globally from 2000 to 2014. The performances of the multimodel ensembles and individual models were compared using the satellite-observed LAI products as the reference. Generally, ensemble simulations provide more accurate estimates than individual models. There were significant performance differences among the six tested methods. The IPM ensemble model simulated LAIgs more accurately than the other tested models, as the reduction in the root-mean-square error was 84.99% higher than the MME results and 61.50% higher than the EBMA results. Thus, IPM optimization can reproduce LAIgs trends accurately for 91.62% of the global vegetated area, which is double the area of the results from MME. Furthermore, the contributions and uncertainties of the individual models in the final simulated IPM LAIgs changes indicated that the best individual model (CABLE) showed the greatest area fraction for the maximum IPM weight (32.49%), especially in the low-lalitude to midlatitude areas. Numéro de notice : A2021-111 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.12.014 Date de publication en ligne : 03/06/2020 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=96913 [article]

G-band radar for humidity and cloud remote sensing / Ken B. Cooper in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1106 - 1117 Titre : G-band radar for humidity and cloud remote sensing Type de document : Article/Communication Auteurs : Ken B. Cooper, Auteur ; Richard J. Roy, Auteur ; Robert Dengler, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1106 - 1117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] antenne radar [Termes IGN] bruit thermique [Termes IGN] humidité de l'air [Termes IGN] modèle atmosphérique [Termes IGN] nuage [Termes IGN] rapport signal sur bruit [Termes IGN] réflectivité [Termes IGN] télédétection en hyperfréquence Résumé : (auteur) VIPR (vapor in-cloud profiling radar) is a tunable G-band radar designed for humidity and cloud remote sensing. VIPR uses all-solid-state components and operates in a frequency-modulated continuous-wave (FMCW) radar mode, offering a transmit power of 200–300 mW. Its typical chirp bandwidth of 10 MHz over a center-frequency tuning span of 167–174.8 GHz results in a nominal range resolution of 15 m. The radar’s measured noise figure over the transmit band is between 7.4 and 10.4 dB, depending on its frequency and hardware configuration, and its calculated antenna gain is 58 dB. These parameters mean that with typical 1 ms chirp times, single-pulse cloud reflectivities as low as −26 dBZ are detectable with unity signal-to-noise at 5 km. Experimentally, radar returns from ice clouds above 10 km in height have been observed from the ground. VIPR’s absolute sensitivity was validated using a spherical metal target in the radar antenna’s far-field, and a G-band switch has been implemented in an RF calibration loop for periodic recalibration. The radar achieves high sensitivity with thermal noise limited detection both by virtue of its low-noise RF architecture and by using a quasioptical duplexing method that preserves ultrahigh transmit/receive isolation despite operation in an FMCW mode with a single primary antenna shared by the transmitter and receiver. Numéro de notice : A2021-112 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2995325 Date de publication en ligne : 04/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2995325 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96916 [article]

Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1200 - 1213 Titre : Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification Type de document : Article/Communication Auteurs : Zitong Wu, Auteur ; Biao Hou, Auteur ; Licheng Jiao, Auteur Année de publication : 2021 Article en page(s) : pp 1200 - 1213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] attention (apprentissage automatique) [Termes IGN] classification contextuelle [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image radar moirée Résumé : (auteur) Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. Numéro de notice : A2021-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3004911 Date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3004911 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96918 [article]

SAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1231 - 1249 Titre : SAR image speckle reduction based on nonconvex hybrid total variation model Type de document : Article/Communication Auteurs : Yuli Sun, Auteur ; Lin Lei, Auteur ; Dongdong Guan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1231 - 1249 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] artefact [Termes IGN] chatoiement [Termes IGN] détection de contours [Termes IGN] distribution de Fisher [Termes IGN] gradient [Termes IGN] image radar moirée [Termes IGN] régularisation d'image [Termes IGN] variable Résumé : (auteur) Speckle noise inherent in synthetic aperture radar (SAR) images seriously affects the visual effect and brings great difficulties to the postprocessing of the SAR image. Due to the edge-preserving feature, total variation (TV) regularization-based techniques have been extensively utilized to reduce the speckle. However, the strong scatters in SAR image with radiometry several orders of magnitude larger than their surrounding regions limit the effectiveness of TV regularization. Meanwhile, the ℓ1 -norm first-order TV regularization sometimes causes staircase artifacts as it favors solutions that are piecewise constant, and it usually underestimates high-amplitude components of image gradient as the ℓ1 -norm uniformly penalizes the amplitude. To overcome these shortcomings, a new hybrid variation model, called Fisher–Tippett (FT) distribution- ℓp -norm first-and second-order hybrid TVs (HTpVs), is proposed to reduce the speckle after removing the strong scatters. Especially, the FT-HTpV inherits the advantages of the distribution based data fidelity term, the nonconvex regularization, and the higher order TV regularization. Therefore, it can effectively remove the speckle while preserving point scatters and edges and reducing staircase artifacts well. To efficiently solve the nonconvex minimization problem, an iterative framework with a nonmonotone-accelerated proximal gradient (nmAPG) method and a matrix-vector acceleration strategy are used. Extensive experiments on both the simulated and real SAR images demonstrate the effectiveness of the proposed method. Numéro de notice : A2021-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002561 Date de publication en ligne : 08/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002561 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96924 [article]

Study of systematic bias in measuring surface deformation with SAR interferometry / Homa Ansari in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1285 - 1301 Titre : Study of systematic bias in measuring surface deformation with SAR interferometry Type de document : Article/Communication Auteurs : Homa Ansari, Auteur ; Francesco De Zan, Auteur ; Alessandro Parizzi, Auteur Année de publication : 2021 Article en page(s) : pp 1285 - 1301 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] atténuation du signal [Termes IGN] décorrélation [Termes IGN] déformation de surface [Termes IGN] erreur de phase [Termes IGN] erreur systématique [Termes IGN] image radar moirée [Termes IGN] image Sentinel-SAR [Termes IGN] interféromètrie par radar à antenne synthétique [Termes IGN] processus stochastique [Termes IGN] rapport signal sur bruit [Termes IGN] série temporelle Résumé : (auteur) This article investigates the presence of a new interferometric signal in multilooked synthetic aperture radar (SAR) interferograms that cannot be attributed to the atmospheric or Earth-surface topography changes. The observed signal is short-lived and decays with the temporal baseline; however, it is distinct from the stochastic noise attributed to temporal decorrelation. The presence of such a fading signal introduces a systematic phase component, particularly in short temporal baseline interferograms. If unattended, it biases the estimation of Earth surface deformation from SAR time series. Here, the contribution of the mentioned phase component is quantitatively assessed. The biasing impact on the deformation-signal retrieval is further evaluated. A quality measure is introduced to allow the prediction of the associated error with the fading signals. Moreover, a practical solution for the mitigation of this physical signal is discussed; special attention is paid to the efficient processing of Big Data from modern SAR missions such as Sentinel-1 and NISAR. Adopting the proposed solution, the deformation bias is shown to decrease significantly. Based on these analyses, we put forward our recommendations for efficient and accurate deformation-signal retrieval from large stacks of multilooked interferograms. Numéro de notice : A2021-115 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3003421 Date de publication en ligne : 30/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3003421 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96929 [article]

Correntropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1453 - 1471 Titre : Correntropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaorun Li, Auteur ; Risheng Huang, Auteur ; Liaolying Zhao, Auteur Année de publication : 2021 Article en page(s) : pp 1453 - 1471 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] corrélation [Termes IGN] entropie [Termes IGN] image hyperspectrale [Termes IGN] méthode robuste [Termes IGN] signature spectrale Résumé : (auteur) Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims at estimating a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. The performance of HU is often seriously degraded by various kinds of noise existing in hyperspectral images (HSIs). Most of existing robust HU methods are based on the assumption that noise or outlier only exists in one kind of formulation, e.g., band noise or pixel noise. However, in real-world applications, HSIs are unavoidably corrupted by noisy bands and noisy pixels simultaneously, which require robust HU in both the spatial dimension and spectral dimension. Meanwhile, the sparsity of abundances is an inherent property of HSIs and different regions in an HSI may possess various sparsity levels across locations. This article proposes a correntropy-based spatial-spectral robust sparsity-regularized unmixing model to achieve 2-D robustness and adaptive weighted sparsity constraint for abundances simultaneously. The updated rules of the proposed model are efficient to be implemented and carried out by a half-quadratic technique. The experimental results obtained by both synthetic and real hyperspectral data demonstrate the superiority of the proposed method compared to the state-of-the-art methods. Numéro de notice : A2021-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2999936 Date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2999936 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96930 [article]

Curved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives / Jingwei Song in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1660 - 1674 Titre : Curved buildings reconstruction from airborne LiDAR data by matching and deforming geometric primitives Type de document : Article/Communication Auteurs : Jingwei Song, Auteur ; Shaobo Xia, Auteur ; Jun Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1660 - 1674 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] courbe [Termes IGN] déformation géométrique [Termes IGN] détection de contours [Termes IGN] données lidar [Termes IGN] primitive géométrique [Termes IGN] reconstruction 3D du bâti [Termes IGN] semis de points [Termes IGN] stockage de données Numéro de notice : A2021-117 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2995732 Date de publication en ligne : 08/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2995732 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96931 [article]