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  • Rural Digital Europe
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  • IEEE Transactions on Geoscience and...

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Thanh Huy Nguyen; Sophie Ricci; Andrea Piacentini; Ehouarn Simon; +2 Authors

    Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from spatial Earth Observation. This paper focuses on the assimilation of 2D flood observations derived from Synthetic Aperture Radar (SAR) images acquired during a flood event with a dual state-parameter Ensemble Kalman Filter (EnKF). Binary wet/dry maps are here expressed in terms of wet surface ratios (WSR) over a number of subdomains of the floodplain. This ratio is further assimilated jointly with in-situ water-level observations to improve the flow dynamics within the floodplain. However, the non-Gaussianity of the observation errors associated with SAR-derived measurements break a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this paper lies in the treatment of the non-Gaussianity of the SAR-derived WSR observations with a Gaussian anamorphosis process (GA). This DA strategy was validated and applied over the Garonne Marmandaise catchment (South-west of France) represented with the TELEMAC-2D hydrodynamic model, first in a twin experiment and then for a major flood event that occurred in January-February 2021. It was shown that assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations significantly improves the representation of the flood dynamics. Also, the GA transformation brings further improvement to the DA analysis, while not being a critical component in the DA strategy. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote-sensing datasets. Comment: 19 pages, 13 figures. Submitted to the IEEE Transactions on Geoscience and Remote Sensing

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ arXiv.org e-Print Ar...arrow_drop_down
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    arXiv.org e-Print Archive
    Other literature type . Preprint . 2023
    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
    IEEE Transactions on Geoscience and Remote Sensing
    Article . 2024 . Peer-reviewed
    License: IEEE Copyright
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    https://doi.org/10.48550/arxiv...
    Article . 2023
    License: arXiv Non-Exclusive Distribution
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      image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ arXiv.org e-Print Ar...arrow_drop_down
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      arXiv.org e-Print Archive
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      IEEE Transactions on Geoscience and Remote Sensing
      Article . 2024 . Peer-reviewed
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      https://doi.org/10.48550/arxiv...
      Article . 2023
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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Giuseppe Guarino; Matteo Ciotola; Gemine Vivone; Giuseppe Scarpa;

    Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges. It consists in a pixel-level fusion between a lower-resolution hyperspectral datacube and a higher-resolution single-band image, the panchromatic image, with the goal of providing a hyperspectral datacube at panchromatic resolution. Thanks to their powerful representational capabilities, deep learning models have succeeded to provide unprecedented results on many general purpose image processing tasks. However, when moving to domain specific problems, as in this case, the advantages with respect to traditional model-based approaches are much lesser clear-cut due to several contextual reasons. Scarcity of training data, lack of ground-truth, data shape variability, are some such factors that limit the generalization capacity of the state-of-the-art deep learning networks for hyperspectral pansharpening. To cope with these limitations, in this work we propose a new deep learning method which inherits a simple single-band unsupervised pansharpening model nested in a sequential band-wise adaptive scheme, where each band is pansharpened refining the model tuned on the preceding one. By doing so, a simple model is propagated along the wavelength dimension, adaptively and flexibly, with no need to have a fixed number of spectral bands, and, with no need to dispose of large, expensive and labeled training datasets. The proposed method achieves very good results on our datasets, outperforming both traditional and deep learning reference methods. The implementation of the proposed method can be found on https://github.com/giu-guarino/R-PNN

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    arXiv.org e-Print Archive
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    IEEE Transactions on Geoscience and Remote Sensing
    Article . 2024 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
    Article . 2023
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      arXiv.org e-Print Archive
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      IEEE Transactions on Geoscience and Remote Sensing
      Article . 2024 . Peer-reviewed
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      https://doi.org/10.48550/arxiv...
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    Authors: Ahmed J. Afifi; Samuel T. Thiele; Aldino Rizaldy; Sandra Lorenz; +5 Authors

    The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. The point cloud is dense and contains 3,242,964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256.

    image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ROBISarrow_drop_down
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    IEEE Transactions on Geoscience and Remote Sensing
    Article . 2024 . Peer-reviewed
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    KITopen
    Article . 2023
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    https://doi.org/10.5445/ir/100...
    Article . 2023
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    arXiv.org e-Print Archive
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    image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
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    https://doi.org/10.48550/arxiv...
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Sining Chen; Yilei Shi; Zhitong Xiong; Xiao Xiang Zhu;

    3D geo-information is of great significance for understanding the living environment; however, 3D perception from remote sensing data, especially on a large scale, is restricted. To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data. As an ill-posed problem, monocular height estimation requires well-designed networks for enhanced representations to improve performance. Moreover, the distribution of height values is long-tailed with the low-height pixels, e.g., the background, as the head, and thus trained networks are usually biased and tend to underestimate building heights. To solve the problems, instead of formalizing the problem as a regression task, we propose HTC-DC Net following the classification-regression paradigm, with the head-tail cut (HTC) and the distribution-based constraints (DCs) as the main contributions. HTC-DC Net is composed of the backbone network as the feature extractor, the HTC-AdaBins module, and the hybrid regression process. The HTC-AdaBins module serves as the classification phase to determine bins adaptive to each input image. It is equipped with a vision transformer encoder to incorporate local context with holistic information and involves an HTC to address the long-tailed problem in monocular height estimation for balancing the performances of foreground and background pixels. The hybrid regression process does the regression via the smoothing of bins from the classification phase, which is trained via DCs. The proposed network is tested on three datasets of different resolutions, namely ISPRS Vaihingen (0.09 m), DFC19 (1.3 m) and GBH (3 m). Experimental results show the superiority of the proposed network over existing methods by large margins. Extensive ablation studies demonstrate the effectiveness of each design component. Comment: 18 pages, 10 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing

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    Authors: Songyan Zhu; Jian Xu; Meng Fan; Chao Yu; +6 Authors
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    IEEE Transactions on Geoscience and Remote Sensing
    Article . 2023 . Peer-reviewed
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Jin-Liang Xiao; Ting-Zhu Huang; Liang-Jian Deng; Zhong-Cheng Wu; +2 Authors

    Pansharpening (which stands for panchromatic (PAN) sharpening) involves the fusion between a multispectral (MS) image with a higher spectral content than a fine spatial resolution PAN image to generate a high spatial resolution MS (HRMS) image. A widely used concept is the construction of the relationship between PAN and HRMS images by designing pixel-based coefficients. Previous pixel-based methods compute the coefficients pixel-by-pixel while suffering from inaccuracies in some areas leading to spatial distortion. However, we found that the coefficients inherit the spatial properties of the HRMS image, e.g., the local smoothness and nonlocal self-similarity, and the spatial correlation between the coefficients and the HRMS image can increase the accuracy of the estimation process. In this article, we propose a novel spatial fidelity with nonlocal regression (SFNLR) to describe the relationship between PAN and HRMS images. Unlike from the pixel-based perspective, the SFNLR can jointly use the local smoothness and nonlocal self-similarity of the coefficients for preserving spatial information. Besides, the SFNLR is integrated with a widely used spectral fidelity to formulate a new variational model for the pansharpening problem. An effective algorithm based on the alternating direction method of multiplier (ADMM) framework is designed to solve the proposed model. Qualitative and quantitative assessments on reduced and full resolution datasets from different satellites demonstrate that the proposed approach outperforms several state-of-the-art methods. The code is available at: https://github.com/Jin-liangXiao/SFNLR .

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    IEEE Transactions on Geoscience and Remote Sensing
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Chao Song; Yanghua Wang; Alan Richardson; Cai Liu;

    Full-waveform inversion (FWI) is a popularly used high-resolution seismic inversion method. It relies on the measure of the misfit between observed data and predicted data. Due to the sinusoidal nature of seismic waves, a direct comparison of observed data and predicted data using the l2 norm may cause cycle skipping. A variety of objective functions for FWI have been proposed to resolve this issue over the years. Based on the gradient optimization method, an explicit expression of the model gradient of the defined objective function is needed to be derived and calculated. This complicated step can be circumvented by using an automatic gradient calculation technique, called automatic differentiation (AD). AD allows calculation of the gradients of the model parameters, as well as those of the inputs using the chain rule. Taking advantage of the deep-learning framework, FWI with different objective functions can be automatically optimized using AD. To improve the accuracy and applicability of FWI on real data, we propose a new objective function that we refer to as the weighted envelope-correlation inversion (WECI), which combines two correlation-based waveform inversions. The weights imposed on these two terms in this new objective function can be dynamically adjusted by the sigmoid function during the optimization process. We show the versatility and effectiveness of AD-based waveform inversions using different objective functions through numerical tests. We also demonstrate the superiority of the proposed WECI method on synthetic data and real data.

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    IEEE Transactions on Geoscience and Remote Sensing
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Valentine Bellet; Mathieu Fauvel; Jordi Inglada;

    In this article, we propose an approach based on Gaussian Processes (GP) for large scale land cover pixel-basedclassification with Sentinel-2 satellite image time-series (SITS). We used a sparse approximation of the posterior combined with variational inference to learn the GP’s parameters. We applied stochastic gradient descent and GPU computing to optimize our GP models on massive data sets. The proposed GP model can be trained with hundreds of thousands of samples, compared to few thousands for traditional GP methods. Moreover, we included the spatial information by adding the geographic coordinates into the GP’s covariance function to efficiently exploit the spatio-spectro-temporal structure of the SITS. We ran experiments with Sentinel-2 SITS of the full year 2018 over an area of 200 000 km 2 (about 2 billion pixels) in the south of France, which is representative of an operational setting. Adding the spatial information significantly improved the results in terms of classification accuracy. With spatial information, GP models have an overall accuracy of 79.8. They are more than three points above Random Forest (the method used for current operational systems) and more than one point above a multi-layer perceptron. Compared to a Transformer-based model (which provides state ofthe art results in the literature, but are not applied in operational systems), GP models are only one point below. International audience

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    Hyper Article en Ligne
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    IEEE Transactions on Geoscience and Remote Sensing
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      image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Janne Alatalo; Tuomo Sipola; Mika Rantonen;

    Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images.

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    arXiv.org e-Print Archive
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    IEEE Transactions on Geoscience and Remote Sensing
    Article . 2023 . Peer-reviewed
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    https://doi.org/10.48550/arxiv...
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      IEEE Transactions on Geoscience and Remote Sensing
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      https://doi.org/10.48550/arxiv...
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    Authors: Pálsson, Burkni;

    Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning) og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar. Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og útfærslur á þeim. í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með sjálfkóðurum. Megin framlag ritgerðarinnar er eftirfarandi: - Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm til að aðgreina grennd af rófum í einu. - Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches) og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina. - Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum. The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds. The Icelandic Research Fund under Grants 174075-05 and 207233-051

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    Authors: Thanh Huy Nguyen; Sophie Ricci; Andrea Piacentini; Ehouarn Simon; +2 Authors

    Flood simulation and forecast capability have been greatly improved thanks to advances in data assimilation (DA) strategies incorporating various types of observations; many are derived from spatial Earth Observation. This paper focuses on the assimilation of 2D flood observations derived from Synthetic Aperture Radar (SAR) images acquired during a flood event with a dual state-parameter Ensemble Kalman Filter (EnKF). Binary wet/dry maps are here expressed in terms of wet surface ratios (WSR) over a number of subdomains of the floodplain. This ratio is further assimilated jointly with in-situ water-level observations to improve the flow dynamics within the floodplain. However, the non-Gaussianity of the observation errors associated with SAR-derived measurements break a major hypothesis for the application of the EnKF, thus jeopardizing the optimality of the filter analysis. The novelty of this paper lies in the treatment of the non-Gaussianity of the SAR-derived WSR observations with a Gaussian anamorphosis process (GA). This DA strategy was validated and applied over the Garonne Marmandaise catchment (South-west of France) represented with the TELEMAC-2D hydrodynamic model, first in a twin experiment and then for a major flood event that occurred in January-February 2021. It was shown that assimilating SAR-derived WSR observations, in complement to the in-situ water-level observations significantly improves the representation of the flood dynamics. Also, the GA transformation brings further improvement to the DA analysis, while not being a critical component in the DA strategy. This study heralds a reliable solution for flood forecasting over poorly gauged catchments thanks to available remote-sensing datasets. Comment: 19 pages, 13 figures. Submitted to the IEEE Transactions on Geoscience and Remote Sensing

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    IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Giuseppe Guarino; Matteo Ciotola; Gemine Vivone; Giuseppe Scarpa;

    Hyperspectral pansharpening is receiving a growing interest since the last few years as testified by a large number of research papers and challenges. It consists in a pixel-level fusion between a lower-resolution hyperspectral datacube and a higher-resolution single-band image, the panchromatic image, with the goal of providing a hyperspectral datacube at panchromatic resolution. Thanks to their powerful representational capabilities, deep learning models have succeeded to provide unprecedented results on many general purpose image processing tasks. However, when moving to domain specific problems, as in this case, the advantages with respect to traditional model-based approaches are much lesser clear-cut due to several contextual reasons. Scarcity of training data, lack of ground-truth, data shape variability, are some such factors that limit the generalization capacity of the state-of-the-art deep learning networks for hyperspectral pansharpening. To cope with these limitations, in this work we propose a new deep learning method which inherits a simple single-band unsupervised pansharpening model nested in a sequential band-wise adaptive scheme, where each band is pansharpened refining the model tuned on the preceding one. By doing so, a simple model is propagated along the wavelength dimension, adaptively and flexibly, with no need to have a fixed number of spectral bands, and, with no need to dispose of large, expensive and labeled training datasets. The proposed method achieves very good results on our datasets, outperforming both traditional and deep learning reference methods. The implementation of the proposed method can be found on https://github.com/giu-guarino/R-PNN

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    IEEE Transactions on Geoscience and Remote Sensing
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Ahmed J. Afifi; Samuel T. Thiele; Aldino Rizaldy; Sandra Lorenz; +5 Authors

    The increasing use of deep learning techniques has reduced interpretation time and, ideally, reduced interpreter bias by automatically deriving geological maps from digital outcrop models. However, accurate validation of these automated mapping approaches is a significant challenge due to the subjective nature of geological mapping and the difficulty in collecting quantitative validation data. Additionally, many state-of-the-art deep learning methods are limited to 2D image data, which is insufficient for 3D digital outcrops, such as hyperclouds. To address these challenges, we present Tinto, a multi-sensor benchmark digital outcrop dataset designed to facilitate the development and validation of deep learning approaches for geological mapping, especially for non-structured 3D data like point clouds. Tinto comprises two complementary sets: 1) a real digital outcrop model from Corta Atalaya (Spain), with spectral attributes and ground-truth data, and 2) a synthetic twin that uses latent features in the original datasets to reconstruct realistic spectral data (including sensor noise and processing artifacts) from the ground-truth. The point cloud is dense and contains 3,242,964 labeled points. We used these datasets to explore the abilities of different deep learning approaches for automated geological mapping. By making Tinto publicly available, we hope to foster the development and adaptation of new deep learning tools for 3D applications in Earth sciences. The dataset can be accessed through this link: https://doi.org/10.14278/rodare.2256.

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    Authors: Sining Chen; Yilei Shi; Zhitong Xiong; Xiao Xiang Zhu;

    3D geo-information is of great significance for understanding the living environment; however, 3D perception from remote sensing data, especially on a large scale, is restricted. To tackle this problem, we propose a method for monocular height estimation from optical imagery, which is currently one of the richest sources of remote sensing data. As an ill-posed problem, monocular height estimation requires well-designed networks for enhanced representations to improve performance. Moreover, the distribution of height values is long-tailed with the low-height pixels, e.g., the background, as the head, and thus trained networks are usually biased and tend to underestimate building heights. To solve the problems, instead of formalizing the problem as a regression task, we propose HTC-DC Net following the classification-regression paradigm, with the head-tail cut (HTC) and the distribution-based constraints (DCs) as the main contributions. HTC-DC Net is composed of the backbone network as the feature extractor, the HTC-AdaBins module, and the hybrid regression process. The HTC-AdaBins module serves as the classification phase to determine bins adaptive to each input image. It is equipped with a vision transformer encoder to incorporate local context with holistic information and involves an HTC to address the long-tailed problem in monocular height estimation for balancing the performances of foreground and background pixels. The hybrid regression process does the regression via the smoothing of bins from the classification phase, which is trained via DCs. The proposed network is tested on three datasets of different resolutions, namely ISPRS Vaihingen (0.09 m), DFC19 (1.3 m) and GBH (3 m). Experimental results show the superiority of the proposed network over existing methods by large margins. Extensive ablation studies demonstrate the effectiveness of each design component. Comment: 18 pages, 10 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing

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    Authors: Songyan Zhu; Jian Xu; Meng Fan; Chao Yu; +6 Authors
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    Authors: Jin-Liang Xiao; Ting-Zhu Huang; Liang-Jian Deng; Zhong-Cheng Wu; +2 Authors

    Pansharpening (which stands for panchromatic (PAN) sharpening) involves the fusion between a multispectral (MS) image with a higher spectral content than a fine spatial resolution PAN image to generate a high spatial resolution MS (HRMS) image. A widely used concept is the construction of the relationship between PAN and HRMS images by designing pixel-based coefficients. Previous pixel-based methods compute the coefficients pixel-by-pixel while suffering from inaccuracies in some areas leading to spatial distortion. However, we found that the coefficients inherit the spatial properties of the HRMS image, e.g., the local smoothness and nonlocal self-similarity, and the spatial correlation between the coefficients and the HRMS image can increase the accuracy of the estimation process. In this article, we propose a novel spatial fidelity with nonlocal regression (SFNLR) to describe the relationship between PAN and HRMS images. Unlike from the pixel-based perspective, the SFNLR can jointly use the local smoothness and nonlocal self-similarity of the coefficients for preserving spatial information. Besides, the SFNLR is integrated with a widely used spectral fidelity to formulate a new variational model for the pansharpening problem. An effective algorithm based on the alternating direction method of multiplier (ADMM) framework is designed to solve the proposed model. Qualitative and quantitative assessments on reduced and full resolution datasets from different satellites demonstrate that the proposed approach outperforms several state-of-the-art methods. The code is available at: https://github.com/Jin-liangXiao/SFNLR .

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    IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Chao Song; Yanghua Wang; Alan Richardson; Cai Liu;

    Full-waveform inversion (FWI) is a popularly used high-resolution seismic inversion method. It relies on the measure of the misfit between observed data and predicted data. Due to the sinusoidal nature of seismic waves, a direct comparison of observed data and predicted data using the l2 norm may cause cycle skipping. A variety of objective functions for FWI have been proposed to resolve this issue over the years. Based on the gradient optimization method, an explicit expression of the model gradient of the defined objective function is needed to be derived and calculated. This complicated step can be circumvented by using an automatic gradient calculation technique, called automatic differentiation (AD). AD allows calculation of the gradients of the model parameters, as well as those of the inputs using the chain rule. Taking advantage of the deep-learning framework, FWI with different objective functions can be automatically optimized using AD. To improve the accuracy and applicability of FWI on real data, we propose a new objective function that we refer to as the weighted envelope-correlation inversion (WECI), which combines two correlation-based waveform inversions. The weights imposed on these two terms in this new objective function can be dynamically adjusted by the sigmoid function during the optimization process. We show the versatility and effectiveness of AD-based waveform inversions using different objective functions through numerical tests. We also demonstrate the superiority of the proposed WECI method on synthetic data and real data.

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    IEEE Transactions on Geoscience and Remote Sensing
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Valentine Bellet; Mathieu Fauvel; Jordi Inglada;

    In this article, we propose an approach based on Gaussian Processes (GP) for large scale land cover pixel-basedclassification with Sentinel-2 satellite image time-series (SITS). We used a sparse approximation of the posterior combined with variational inference to learn the GP’s parameters. We applied stochastic gradient descent and GPU computing to optimize our GP models on massive data sets. The proposed GP model can be trained with hundreds of thousands of samples, compared to few thousands for traditional GP methods. Moreover, we included the spatial information by adding the geographic coordinates into the GP’s covariance function to efficiently exploit the spatio-spectro-temporal structure of the SITS. We ran experiments with Sentinel-2 SITS of the full year 2018 over an area of 200 000 km 2 (about 2 billion pixels) in the south of France, which is representative of an operational setting. Adding the spatial information significantly improved the results in terms of classification accuracy. With spatial information, GP models have an overall accuracy of 79.8. They are more than three points above Random Forest (the method used for current operational systems) and more than one point above a multi-layer perceptron. Compared to a Transformer-based model (which provides state ofthe art results in the literature, but are not applied in operational systems), GP models are only one point below. International audience

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    IEEE Transactions on Geoscience and Remote Sensing
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Janne Alatalo; Tuomo Sipola; Mika Rantonen;

    Satellite-based Synthetic Aperture Radar (SAR) images can be used as a source of remote sensed imagery regardless of cloud cover and day-night cycle. However, the speckle noise and varying image acquisition conditions pose a challenge for change detection classifiers. This paper proposes a new method of improving SAR image processing to produce higher quality difference images for the classification algorithms. The method is built on a neural network-based mapping transformation function that produces artificial SAR images from a location in the requested acquisition conditions. The inputs for the model are: previous SAR images from the location, imaging angle information from the SAR images, digital elevation model, and weather conditions. The method was tested with data from a location in North-East Finland by using Sentinel-1 SAR images from European Space Agency, weather data from Finnish Meteorological Institute, and a digital elevation model from National Land Survey of Finland. In order to verify the method, changes to the SAR images were simulated, and the performance of the proposed method was measured using experimentation where it gave substantial improvements to performance when compared to a more conventional method of creating difference images.

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    IEEE Transactions on Geoscience and Remote Sensing
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    https://doi.org/10.48550/arxiv...
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      IEEE Transactions on Geoscience and Remote Sensing
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    Authors: Pálsson, Burkni;

    Efni þessarar ritgerðar er aðgreining fjölrásamynda (e. blind hyperspectral unmixing) með sjálfkóðurum (e. autoencoders) byggðum á djúpum lærdómi (e. deep learning). Tvær aðferðir byggðar á sjálfkóðurum eru kynntar og rannsakaðar. Báðar aðferðirnar leitast við að nýta sér rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Ein aðferð með að nýta sér fjölbeitingarlærdóm (e. multitask learning) og hin með að nota sjálfkóðara útfærðan með földunartaugnaneti (e. convolutional neural network). Hvortveggja bætir samkvæmni og hæfni fjölrásagreiningarinnar. Ennfremur inniheldur ritgerðin yfirgripsmikið yfirlit yfir þær sjálfkóðaraaðferðir sem hafa verið birtar ásamt greinargóðri umræðu um mismunandi gerðir sjálfkóðara og útfærslur á þeim. í lok ritgerðarinnar er svo að finna gagnrýninn samanburð á 11 mismunandi aðferðum byggðum á sjálfkóðurum. Brottnáms (e. ablation) tilraunir eru gerðar til að svara spurningunni hvers vegna sjálfkóðarar eru svo árangursríkir í fjölrásagreiningu og stuttlega rætt um hvað framtíðin ber í skauti sér varðandi aðgreiningu fjölrásamynda með sjálfkóðurum. Megin framlag ritgerðarinnar er eftirfarandi: - Ný sjálfkóðaraaðferð, MTLAEU, sem nýtir á beinan hátt rúmfræðilega fylgni rófa í fjölrásamyndum til að bæta árangur aðgreiningar. Aðferðin notar fjölbeitingarlærdóm til að aðgreina grennd af rófum í einu. - Ný aðferð, CNNAEU, sem notar 2D földunartaugnanet fyrir bæði kóðara og afkóðara og er fyrsta birta aðferðin til að gera það. Aðferðin er þjálfuð á myndbútum (e.patches) og því er rúmfræðileg bygging myndarinnar sem greina á varðveitt í gegnum aðferðina. - Yfirgripsmikil og ítarlegt fræðilegt yfirlit yfir birtar sjálfkóðaraaðferðir fyrir fjölrásagreiningu. Gefinn er inngangur að sjálfkóðurum og elstu tegundir sjálfkóðara eru kynntar. Gefið er greinargott yfirlit yfir helstu birtar aðferðir fyrir fjölrásagreiningu sem byggja á sjálfkóðurum og gerður er gangrýninn samburður á 11 mismunandi sjálfkóðaraaðferðum. The subject of this thesis is blind hyperspectral unmixing using deep learning based autoencoders. Two methods based on autoencoders are proposed and analyzed. Both methods seek to exploit the spatial correlations in the hyperspectral images to improve the performance. One by using multitask learning to simultaneously unmix a neighbourhood of pixels while the other by using a convolutional neural network autoencoder. This increases the consistency and robustness of the methods. In addition, a review of the various autoencoder methods in the literature is given along with a detailed discussion of different types of autoencoders. The thesis concludes by a critical comparison of eleven different autoencoder based methods. Ablation experiments are performed to answer the question of why autoencoders are so effective in blind hyperspectral unmixing, and an opinion is given on what the future in autoencoder unmixing holds. The Icelandic Research Fund under Grants 174075-05 and 207233-051

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