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Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning / Xin Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 178 (August 2021)
[article]
Titre : Rapid and large-scale mapping of flood inundation via integrating spaceborne synthetic aperture radar imagery with unsupervised deep learning Type de document : Article/Communication Auteurs : Xin Jiang, Auteur ; Shijing Liang, Auteur ; Xinyue He, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 36 - 50 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] cartographie des risques
[Termes IGN] chaîne de traitement
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Fleuve bleu (Chine)
[Termes IGN] Google Earth Engine
[Termes IGN] image radar moirée
[Termes IGN] image Sentinel-SAR
[Termes IGN] inondation
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation d'image
[Termes IGN] superpixel
[Termes IGN] surveillance hydrologiqueRésumé : (auteur) Synthetic aperture radar (SAR) has great potential for timely monitoring of flood information as it penetrates the clouds during flood events. Moreover, the proliferation of SAR satellites with high spatial and temporal resolution provides a tremendous opportunity to understand the flood risk and its quick response. However, traditional algorithms to extract flood inundation using SAR often require manual parameter tuning or data annotation, which presents a challenge for the rapid automated mapping of large and complex flooded scenarios. To address this issue, we proposed a segmentation algorithm for automatic flood mapping in near-real-time over vast areas and for all-weather conditions by integrating Sentinel-1 SAR imagery with an unsupervised machine learning approach named Felz-CNN. The algorithm consists of three phases: (i) super-pixel generation; (ii) convolutional neural network-based featurization; (iii) super-pixel aggregation. We evaluated the Felz-CNN algorithm by mapping flood inundation during the Yangtze River flood in 2020, covering a total study area of 1,140,300 km2. When validated on fine-resolution Planet satellite imagery, the algorithm accurately identified flood extent with producer and user accuracy of 93% and 94%, respectively. The results are indicative of the usefulness of our unsupervised approach for the application of flood mapping. Meanwhile, we overlapped the post-disaster inundation map with a 10-m resolution global land cover map (FROM-GLC10) to assess the damages to different land cover types. Of these types, cropland and residential settlements were most severely affected, with inundation areas of 9,430.36 km2 and 1,397.50 km2, respectively, results that are in agreement with statistics from relevant agencies. Compared with traditional supervised classification algorithms that require time-consuming data annotation, our unsupervised algorithm can be deployed directly to high-performance computing platforms such as Google Earth Engine and PIE-Engine to generate a large-spatial map of flood-affected areas within minutes, without time-consuming data downloading and processing. Importantly, this efficiency enables the fast and effective monitoring of flood conditions to aid in disaster governance and mitigation globally. Numéro de notice : A2021-560 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.05.019 Date de publication en ligne : 09/06/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.05.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98118
in ISPRS Journal of photogrammetry and remote sensing > vol 178 (August 2021) . - pp 36 - 50[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2021081 SL Revue Centre de documentation Revues en salle Disponible 081-2021083 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2021082 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Atmospheric correction to passive microwave brightness temperature in snow cover mapping over china / Yubao Qiu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)
[article]
Titre : Atmospheric correction to passive microwave brightness temperature in snow cover mapping over china Type de document : Article/Communication Auteurs : Yubao Qiu, Auteur ; Lijuan Shi, Auteur ; Juha Lemmetyinen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6482 - 6495 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] capteur passif
[Termes IGN] Chine
[Termes IGN] correction atmosphérique
[Termes IGN] image NOAA
[Termes IGN] image SSMIS
[Termes IGN] image Terra-MODIS
[Termes IGN] manteau neigeux
[Termes IGN] modèle atmosphérique
[Termes IGN] neige
[Termes IGN] série temporelle
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] température de luminance
[Termes IGN] teneur en vapeur d'eauRésumé : (auteur) Variable atmospheric conditions are typically ignored in the retrieval of geophysical parameters of the Earth’s surface when using spaceborne passive microwave observations. However, high frequencies, for example, 91.7 GHz, are sensitive to variable atmospheric absorption, even in winter’s dry conditions. In this article, the influence of variable atmospheric absorption on snow cover extent (SCE) mapping was quantitatively investigated. A physical method was derived to enable atmospheric correction for variable atmospheric conditions. The total column precipitable water vapor (TPWV) from Moderate Resolution Imaging Spectroradiometer (MODIS) was parametrized into transmittances in this correction method. The corrected brightness temperature at 19 and 91.7 GHz from the Special Sensor Microwave Imager Sounder (SSMIS) was applied to the threshold algorithm for snow mapping over China. Compared with the Interactive Multisensor Snow and Ice Mapping System (IMS) data in winter from 2012 to 2013, for Qinghai–Tibet plateau (QTP), a significant improvement after correction was obtained from February to March over ephemeral and shallow snow, where the largest daily improvement of accuracy is up to 20%. The accuracy (incl. precision, recall, and F1 index) improved on average is from 0.77 (0.60, 0.68, and 0.63) to 0.79 (0.69, 0.7, and 0.68) over the full winter time from December to March. Over forest-rich Northeast China, where snow in winter is thicker, small improvement was observed at the onset of the snow season and over snow margin area. It was evidenced that high frequency is a promising way of snow cover mapping with the proposed atmospheric correction method. Numéro de notice : A2021-630 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3031837 Date de publication en ligne : 02/11/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3031837 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98279
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6482 - 6495[article]Unsupervised denoising for satellite imagery using wavelet directional cycleGAN / Shaoyang Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)
[article]
Titre : Unsupervised denoising for satellite imagery using wavelet directional cycleGAN Type de document : Article/Communication Auteurs : Shaoyang Kong, Auteur ; Cheng Hu, Auteur ; Rui Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6573 - 6585 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage non-dirigé
[Termes IGN] apprentissage profond
[Termes IGN] classification non dirigée
[Termes IGN] filtrage du bruit
[Termes IGN] image radar
[Termes IGN] Insecta
[Termes IGN] polarimétrie radar
[Termes IGN] réseau antagoniste génératif
[Termes IGN] transformation en ondelettesRésumé : (auteur) The measurement of insect radar cross section (RCS) is a prerequisite for the studies such as the quantitative estimation of insect population density and the identification of insects using entomological radar. In this article, we established a multiband polarimetric RCS measurement system in the microwave anechoic chamber. The targets’ range profile at different frequencies can be obtained based on the step frequency continuous wave, and meanwhile the clutter elimination and polarimetric calibration were applied to reduce the measuring error. The multifrequency (X-/Ku-/Ka-bands) polarimetric RCSs of 169 insects belonging to 21 species were measured and reported, which is the first time to systematically present the multifrequency polarimetric RCSs of insects. The mass of all specimens range from 25.6 to 964 mg, and their ventral-aspect RCSs range from −57.47 to −32.17 dBsm at X-band, from −48.27 to −33.87 dBsm at Ku-band and from −69.76 to −36.40 dBsm at Ka-band. For small insects less than 300 mg, the HH polarization RCS increases rapidly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases monotonically with frequency at X- and Ku-band. For larger insects, the HH polarization RCS decreased slowly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases with the frequency, then reaches the maximum, finally fluctuates with the frequency. At Ka-band, the measured polarization RCS versus frequency curves are smooth and all show similar variation. The measurement results verify the effectiveness and accuracy of the established system. Numéro de notice : A2021-631 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3025601 Date de publication en ligne : 08/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3025601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98281
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6573 - 6585[article]Target-constrained interference-minimized band selection for hyperspectral target detection / Xiaodi Shang in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)
[article]
Titre : Target-constrained interference-minimized band selection for hyperspectral target detection Type de document : Article/Communication Auteurs : Xiaodi Shang, Auteur ; Meiping Song, Auteur ; Yulei Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6044 - 6064 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande spectrale
[Termes IGN] détection de cible
[Termes IGN] image hyperspectrale
[Termes IGN] interférence
[Termes IGN] rapport signal sur bruitRésumé : (auteur) Wealthy spectral information provided by hyperspectral image (HSI) offers great benefits for many applications in hyperspectral data exploitation. However, processing such high-dimensional data volumes that may result in redundant bands due to its high interband correlation will be a challenge. For target detection and classification, this is particularly true since there may only need a relatively small number of bands that respond one particular target of interest well, while most of other bands do not. Band selection (BS) is a major dimensionality reduction technique to remove the redundant bands and selects a few bands to represent the entire image. However, how to eliminate the effect of uninteresting targets with similar spectra on detection of interesting targets is a severe issue arising in target detection for BS. This article develops a new approach called target-constrained interference-minimized BS (TCIMBS) which can be used to select band subset for specific target detection, while annihilating targets of no interest and suppressing interferers and background. Its idea is derived from target-constrained interference-minimized filter (TCIMF). By taking advantage of TCIMF, two band prioritization (BP) criteria called forward minimum variance BP (FMinV-BP) and backward maximum variance BP (BMaxV-BP) along with their three band search-based BS counterparts called sequential forward TCIMBS (SF-TCIMBS), sequential backward TCIMBS (SB-TCIMBS), and improved SB-TCIMBS (SB-TCIMBS*) are derived. The experimental results suggest that TCIMBS can improve the detection accuracy and also achieve better performance in comparison with several state-of-the-art methods. Numéro de notice : A2021-531 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3010826 Date de publication en ligne : 26/08/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3010826 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97987
in IEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 6044 - 6064[article]Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band / Xing Peng in Remote sensing, vol 13 n° 11 (June-1 2021)
[article]
Titre : Forest height estimation from a robust TomoSAR method in the case of small tomographic aperture with airborne dataset at L-band Type de document : Article/Communication Auteurs : Xing Peng, Auteur ; Xinwu Li, Auteur ; Yanan Du, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande L
[Termes IGN] données localisées 3D
[Termes IGN] forêt boréale
[Termes IGN] hauteur des arbres
[Termes IGN] image 3D
[Termes IGN] image radar moirée
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] itération
[Termes IGN] matrice de covariance
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] semis de points
[Termes IGN] Suède
[Termes IGN] tomographie radarRésumé : (auteur) Forest height is an essential input parameter for forest biomass estimation, ecological modeling, and the carbon cycle. Tomographic synthetic aperture radar (TomoSAR), as a three-dimensional imaging technique, has already been successfully used in forest areas to retrieve the forest height. The nonparametric iterative adaptive approach (IAA) has been recently introduced in TomoSAR, achieving a good compromise between high resolution and computing efficiency. However, the performance of the IAA algorithm is significantly degraded in the case of a small tomographic aperture. To overcome this shortcoming, this paper proposes the robust IAA (RIAA) algorithm for SAR tomography. The proposed approach follows the framework of the IAA algorithm, but also considers the noise term in the covariance matrix estimation. By doing so, the condition number of the covariance matrix can be prevented from being too large, improving the robustness of the forest height estimation with the IAA algorithm. A set of simulated experiments was carried out, and the results validated the superiority of the RIAA estimator in the case of a small tomographic aperture. Moreover, a number of fully polarimetric L-band airborne tomographic SAR images acquired from the ESA BioSAR 2008 campaign over the Krycklan Catchment, Northern Sweden, were collected for test purposes. The results showed that the RIAA algorithm performed better in reconstructing the vertical structure of the forest than the IAA algorithm in areas with a small tomographic aperture. Finally, the forest height was estimated by both the RIAA and IAA TomoSAR methods, and the estimation accuracy of the RIAA algorithm was 2.01 m, which is more accurate than the IAA algorithm with 3.25 m. Numéro de notice : A2021-441 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13112147 Date de publication en ligne : 29/05/2021 En ligne : https://doi.org/10.3390/rs13112147 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97828
in Remote sensing > vol 13 n° 11 (June-1 2021) . - n° 2147[article]PolSAR ship detection based on neighborhood polarimetric covariance matrix / Tao Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)PermalinkSpatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images / Bin Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)PermalinkA Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])PermalinkAboveground biomass estimates of tropical mangrove forest using Sentinel-1 SAR coherence data : The superiority of deep learning over a semi-empirical model / S.M. Ghosh in Computers & geosciences, vol 150 (May 2021)PermalinkAutomatic detection and classification of low-level orographic precipitation processes from space-borne radars using machine learning / Malarvizhi Arulraj in Remote sensing of environment, vol 257 (May 2021)PermalinkEvaluating P-Band TomoSAR for biomass retrieval in boreal forest / Erik Blomberg in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkForest height retrieval using P-band airborne multi-baseline SAR data: A novel phase compensation method / Hongliang Lu in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkRefinement of interferometric SAR parameters using digital terrain model as an external reference / Jyunpei Uemoto in ISPRS Journal of photogrammetry and remote sensing, vol 175 (May 2021)PermalinkSAR speckle removal using hybrid frequency modulations / Shuaiqi Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)PermalinkDetecting ground deformation in the built environment using sparse satellite InSAR data with a convolutional neural network / Nantheera Anantrasirichai in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)Permalink