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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6482 - 6495 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'eau Résumé : (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 [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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6573 - 6585 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 ondelettes Résumé : (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 [article]

ComNet: combinational neural network for object detection in UAV-borne thermal images / Minglei Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6662 - 6673 Titre : ComNet: combinational neural network for object detection in UAV-borne thermal images Type de document : Article/Communication Auteurs : Minglei Li, Auteur ; Xingke Zhao, Auteur ; Jiasong Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6662 - 6673 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] détection d'objet [Termes IGN] image captée par drone [Termes IGN] image thermique [Termes IGN] piéton [Termes IGN] saillance [Termes IGN] véhicule Résumé : (auteur) We propose a deep learning-based method for object detection in UAV-borne thermal images that have the capability of observing scenes in both day and night. Compared with visible images, thermal images have lower requirements for illumination conditions, but they typically have blurred edges and low contrast. Using a boundary-aware salient object detection network, we extract the saliency maps of the thermal images to improve the distinguishability. Thermal images are augmented with the corresponding saliency maps through channel replacement and pixel-level weighted fusion methods. Considering the limited computing power of UAV platforms, a lightweight combinational neural network ComNet is used as the core object detection method. The YOLOv3 model trained on the original images is used as a benchmark and compared with the proposed method. In the experiments, we analyze the detection performances of the ComNet models with different image fusion schemes. The experimental results show that the average precisions (APs) for pedestrian and vehicle detection have been improved by 2%~5% compared with the benchmark without saliency map fusion and MobileNetv2. The detection speed is increased by over 50%, while the model size is reduced by 58%. The results demonstrate that the proposed method provides a compromise model, which has application potential in UAV-borne detection tasks. Numéro de notice : A2021-632 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029945 Date de publication en ligne : 21/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98288 [article]

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

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6995 - 7010 Titre : Detail injection-based deep convolutional neural networks for pansharpening Type de document : Article/Communication Auteurs : Liang-Jian Deng, Auteur ; Gemine Vivone, Auteur ; Cheng Jin, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6995 - 7010 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multirésolution [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] image à basse résolution [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] injection d'image [Termes IGN] modèle non linéaire [Termes IGN] pansharpening (fusion d'images) Résumé : (auteur) The fusion of high spatial resolution panchromatic (PAN) data with simultaneously acquired multispectral (MS) data with the lower spatial resolution is a hot topic, which is often called pansharpening. In this article, we exploit the combination of machine learning techniques and fusion schemes introduced to address the pansharpening problem. In particular, deep convolutional neural networks (DCNNs) are proposed to solve this issue. The latter is combined first with the traditional component substitution and multiresolution analysis fusion schemes in order to estimate the nonlinear injection models that rule the combination of the upsampled low-resolution MS image with the extracted details exploiting the two philosophies. Furthermore, inspired by these two approaches, we also developed another DCNN for pansharpening. This is fed by the direct difference between the PAN image and the upsampled low-resolution MS image. Extensive experiments conducted both at reduced and full resolutions demonstrate that this latter convolutional neural network outperforms both the other detail injection-based proposals and several state-of-the-art pansharpening methods. Numéro de notice : A2021-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3031366 En ligne : https://doi.org/10.1109/TGRS.2020.3031366 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98293 [article]

Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners / Kai Tan in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 7038 - 7050 Titre : Leaf and wood separation for individual trees using the intensity and density data of terrestrial laser scanners Type de document : Article/Communication Auteurs : Kai Tan, Auteur ; Weiguo Zhang, Auteur ; Zhen Dong, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7038 - 7050 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] bois [Termes IGN] densité du feuillage [Termes IGN] données lidar [Termes IGN] données TLS (télémétrie) [Termes IGN] feuille (végétation) [Termes IGN] réflectance spectrale [Termes IGN] semis de points Résumé : (auteur) Terrestrial laser scanning (TLS) is a highly effective and noninvasive technology for retrieving the structural and biophysical attributes of trees using 3-D high-accuracy and high-density point clouds. The separation of leaf and wood points in TLS data is a prerequisite for the accurate and reliable derivation of these attributes. In this study, a new method is proposed to separate the leaf and wood points of individual trees by combining the TLS radiometric (intensity) and geometric (density) data. The leaf points are separated from the wood ones through three steps. First, the corrected intensity data are used to separate a part of the leaf points preliminarily given the differences in reflectance characteristics. Second, the density data are adopted for the further separation of another part of the leaf points because the density of the remaining leaf points is smaller than that of the wood points. Finally, a connectivity clustering algorithm is conducted to form several clusters with different sizes (points) and the remaining leaf points are separated in accordance with the cluster sizes. Eight different trees are selected to evaluate the performance of the proposed method. The averaged overall accuracy and kappa coefficient of the eight trees are approximately 95% and 0.81, respectively. The results suggest that the combination of TLS intensity and density data can perform a superior separation of leaf and wood points in terms of efficiency and accuracy, and the proposed separation method can be accurately and robustly used for various trees with different species, sizes, and structures. Numéro de notice : A2021-633 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3032167 Date de publication en ligne : 30/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3032167 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98295 [article]

Ordered subsets-constrained ART algorithm for ionospheric tomography by combining VTEC data / Dunyong Zheng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 7051 - 7061 Titre : Ordered subsets-constrained ART algorithm for ionospheric tomography by combining VTEC data Type de document : Article/Communication Auteurs : Dunyong Zheng, Auteur ; Yibin Yao, Auteur ; Wenfeng Nie, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 7051 - 7061 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] modèle ionosphérique [Termes IGN] teneur totale en électrons [Termes IGN] teneur verticale totale en électrons [Termes IGN] tomographie par GPS Résumé : (auteur) Computerized ionospheric tomography is an important technique for ionosphere investigation. However, it is an ill-posed problem owing to an insufficient amount of available data, because of which the distributions of ionospheric electron density (IED) cannot be reconstructed accurately. In light of this, the ordered subsets-constrained algebraic reconstruction technique (OS_CART) is developed here using vertical total electron content (VTEC) data to solve this problem, where the VTEC derived from the slant total electron content (STEC) of Global Navigation Satellite System (GNSS) signal paths is used to compensate for the lack of data provided by GNSS observations in inversion regions, and the OS_CART is also used to improve the spatial resolution and inversion efficiency. The proposed method was validated by conducting numerical experiments using GNSS and independent ionosonde data in both quiescent and disturbed ionospheric conditions. In contrast to classical methods of ionospheric tomography, the proposed method exhibited significantly higher reconstruction accuracy. While delivering a comparable accuracy to that of traditional methods in terms of self-consistency validation using STEC data and without overfitting, the proposed method yielded a more than 90% improvement over the self-consistency validation using VTEC data. In addition, a better daily description of the ionosphere was obtained using the proposed method, where an increase in the peak height and irregular changes to the IED, associated with variations in the number of epochs and the occurrence of magnetic storms, were observed. Overall, the results reveal that the proposed method is a useful tool for research on space weather. Numéro de notice : A2021-634 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029819 Date de publication en ligne : 28/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029819 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98297 [article]