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Region level SAR image classification using deep features and spatial constraints / Anjun Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 163 (May 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 163 (May 2020) . - pp 36-48 Titre : Region level SAR image classification using deep features and spatial constraints Type de document : Article/Communication Auteurs : Anjun Zhang, Auteur ; Xuezhi Yang, Auteur ; Shuai Fang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 36-48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] carte de confiance [Termes descripteurs IGN] champ aléatoire de Markov [Termes descripteurs IGN] chatoiement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] modélisation spatiale [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] superpixel Résumé : (auteur) The region-level SAR image classification algorithms which combine CNN (Convolutional Neural Networks) with super-pixel have been proposed to enhance the classification accuracy compared with the pixel-level algorithms. However, the spatial constraints between the super-pixel regions are not considered, which may limit the performance of these algorithms. To address this problem, an RCC-MRF (RCC, Region Category Confidence-degree) and CNN based region-level SAR image classification algorithm which explores the deep features extracted by CNN and the spatial constraints between super-pixel regions is proposed in this paper. The initial labels of super-pixel regions are obtained using a voting strategy based on the predicted labels CNN. The unary energy function of RCC-MRF is designed to find the category that a region most probably belongs to by using the RCC term which is constructed based on the probability distributions over all categories of pixels predicted by CNN. The binary energy function of RCC-MRF explores the spatial constraints between the adjacent super-pixel regions. In our proposed algorithm, the pixel-level misclassifications can be reduced by the smoothing within regions and the region-level misclassifications will be rectified by minimizing the energy function of RCC-MRF. Experiments have been done on simulated and real SAR images to evaluate the performance of the proposed algorithm. The experimental results demonstrate that the proposed algorithm notably outperforms the other CNN-based region-level SAR image classification algorithms. Numéro de notice : A2020-136 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.03.001 date de publication en ligne : 07/03/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.03.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94752 [article]

Deep SAR-Net: learning objects from signals / Zhongling Huang in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 179 - 193 Titre : Deep SAR-Net: learning objects from signals Type de document : Article/Communication Auteurs : Zhongling Huang, Auteur ; Mihai Datcu, Auteur ; Zongxu Pan, Auteur ; Bin Lei, Auteur Année de publication : 2020 Article en page(s) : pp 179 - 193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] image Terra [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] micro-onde [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] temps-fréquence Résumé : (Auteur) This paper introduces a novel Synthetic Aperture Radar (SAR) specific deep learning framework for complex-valued SAR images. The conventional deep convolutional neural networks based methods usually take the amplitude information of single-polarization SAR images as the input to learn hierarchical spatial features automatically, which may have difficulties in discriminating objects with similar texture but discriminative scattering patterns. Our novel deep learning framework, Deep SAR-Net, takes complex-valued SAR images into consideration to learn both spatial texture information and backscattering patterns of objects on the ground. On the one hand, we transfer the detected SAR images pre-trained layers to extract spatial features from intensity images. On the other hand, we dig into the Fourier domain to learn physical properties of the objects by joint time-frequency analysis on complex-valued SAR images. We evaluate the effectiveness of Deep SAR-Net on three complex-valued SAR datasets from Sentinel-1 and TerraSAR-X satellite and demonstrate how it works better than conventional deep CNNs, especially on man-made objects classes. The proposed datasets and the trained Deep SAR-Net model with all codes are provided. Numéro de notice : A2020-065 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.016 date de publication en ligne : 23/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.016 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94583 [article]

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Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series / Mathieu Fauvel in Remote sensing of environment, Vol 237 (February 2020)  

Public [article]  inRemote sensing of environment > Vol 237 (February 2020) . - 13 p. Titre : Prediction of plant diversity in grasslands using Sentinel-1 and -2 satellite image time series Type de document : Article/Communication Auteurs : Mathieu Fauvel, Auteur ; Maylis Lopes, Auteur ; Titouan Dubo, Auteur ; Justine Rivers-Moore, Auteur ; Pierre-Louis Frison , Auteur ; Nicolas Gross, Auteur ; Annie Ouin, Auteur Année de publication : 2020 Article en page(s) : 13 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] biodiversité végétale [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] Haute-Garonne (31) [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image RapidEye [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] indice de végétation [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] prairie [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] taxinomie Résumé : (auteur) The prediction of grasslands plant diversity using satellite image time series is considered in this article. Fifteen months of freely available Sentinel optical and radar data were used to predict taxonomic and functional diversity at the pixel scale (10 m × 10 m) over a large geographical extent (40,000 km2). 415 field measurements were collected in 83 grasslands to train and validate several statistical learning methods. The objective was to link the satellite spectro-temporal data to the plant diversity indices. Among the several diversity indices tested, Simpson and Shannon indices were best predicted with a coefficient of determination around 0.4 using a Random Forest predictor and Sentinel-2 data. The use of Sentinel-1 data was not found to improve significantly the prediction accuracy. Using the Random Forest algorithm and the Sentinel-2 time series, the prediction of the Simpson index was performed. The resulting map highlights the intra-parcel variability and demonstrates the capacity of satellite image time series to monitor grasslands plant taxonomic diversity from an ecological viewpoint. Numéro de notice : A2020-004 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2019.111536 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.1016/j.rse.2019.111536 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94296 [article]

Radial interpolation of GPS and leveling data of ground deformation in a resurgent caldera: application to Campi Flegrei (Italy) / Andrea Bevilacqua in Journal of geodesy, vol 94 n°2 (February 2020)  

Public [article]  inJournal of geodesy > vol 94 n°2 (February 2020) Titre : Radial interpolation of GPS and leveling data of ground deformation in a resurgent caldera: application to Campi Flegrei (Italy) Type de document : Article/Communication Auteurs : Andrea Bevilacqua, Auteur ; Augusto Neri, Auteur ; Prospero De Martino, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] caldeira [Termes descripteurs IGN] Campanie (Italie) [Termes descripteurs IGN] déformation horizontale de la croute terrestre [Termes descripteurs IGN] déformation verticale de la croute terrestre [Termes descripteurs IGN] donnée de nivellement [Termes descripteurs IGN] données géodésiques [Termes descripteurs IGN] données GPS [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] modèle de déformation tectonique [Termes descripteurs IGN] régression linéaire [Termes descripteurs IGN] réseau de nivellement [Termes descripteurs IGN] surveillance géologique [Termes descripteurs IGN] volcan Résumé : (auteur) This study presents a new method, called the Radial Interpolation Method, to interpolate data characterized by an approximately radial pattern around a relatively constrained central zone, such as the ground deformation patterns shown in many active volcanic areas. The method enables the fast production of short-term deformation maps on the base of spatially sparse ground deformation measurements and can provide uncertainty quantification on the interpolated values, fundamental for hazard assessment purposes and deformation source reconstruction. The presented approach is not dependent on a priori assumptions about the geometry, location and physical properties of the source, except for the requirement of a locally radial pattern, i.e., allowing multiple centers of symmetry. We test the new method on a synthetic point source example, and then, we apply the method to selected time intervals of real geodetic data collected at the Campi Flegrei caldera during the last 39 years, including examples of leveling, Geodetic Precise Traversing measurements and Global Positioning System. The maps of horizontal displacement, calculated inland, show maximum values lying along a semicircular annular region with a radius of about 2–3 km in size. This semi-annular area is marked by mesoscale structures such as faults, sand dikes and fractures. The maps of vertical displacement describe a linear relation between the maximum vertical uplift measured and the volume variation. The multiplicative factor in the linear relation is about 0.3 × 106 m3/cm if we estimate the proportion of the ΔV that is captured by the GPS network onland and we use this to estimate the full ΔV. In this case, the 95% confidence interval on K because of linear regression is ± 5%. Finally, we briefly discuss how the new method could be used for the production of short-term vent opening maps on the base of real-time geodetic measurements of the horizontal and vertical displacements. Numéro de notice : A2020-152 Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01355-x date de publication en ligne : 05/02/2020 En ligne : https://doi.org/10.1007/s00190-020-01355-x Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94785 [article]

Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data / Guo-Hui Yao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 1 (January 2020) . - pp 691 - 703 Titre : Identification of alpine glaciers in the central Himalayas using fully polarimetric L-Band SAR data Type de document : Article/Communication Auteurs : Guo-Hui Yao, Auteur ; Chang-qing Ke, Auteur ; Xiaobing Zhou, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 691 - 703 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse multiéchelle [Termes descripteurs IGN] bande L [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] glacier [Termes descripteurs IGN] Himalaya [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interferométrie différentielle [Termes descripteurs IGN] matrice de covariance [Termes descripteurs IGN] précision de la classification [Termes descripteurs IGN] segmentation Résumé : (auteur) To study the applicability of full polarimetric synthetic aperture radar (SAR) data to identify alpine glaciers in the central Himalayas, six polarimetric decomposition methods were used to obtain 20 polarimetric characteristic parameters based on the Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band SAR (PALSAR) data. Object-oriented multiscale segmentation was performed on a Landsat 8 Operational Land Imager (OLI) image prior to classification, and the vector boundaries of different types of training samples were selected from the segmented results. We performed a support vector machine (SVM)-based classification on the characteristic parameters from each polarimetric decomposition. All 20 parameters were then screened and combined according to different requirements: the degree of separability of different types of training samples and the type of scattering mechanisms. The results show that the classification accuracy of the incoherent decomposition characteristics based on the covariance matrix is the best, reaching 87%, and it can exceed 91% after adding the local incidence angle to the suite of classifiers. Eventually, more than 93% accuracy was achieved using a combination of multiple polarimetric parameters, which reduced the misclassification between bare ice and rock. We also analyzed the use of controlling factors on the accuracy of alpine glacier identification and found that the polarimetric information and aspect of the glacier surface are the most important factors. The former is the main basis for identification but the latter will confuse the feature distributions of different categories and cause misclassification. Numéro de notice : A2020-077 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2939430 date de publication en ligne : 25/09/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2939430 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94613 [article]

An implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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

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Inside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)  

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Matching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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On the value of corner reflectors and surface models in InSAR precise point positioning / Mengshi Yang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)  

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Polarization dependence of azimuth cutoff from quad-pol SAR images / Huimin Li in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)  

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Introducing spatial regularization in SAR tomography reconstruction / Clément Rambour in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

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Soil and vegetation scattering contributions in L-Band and P-Band polarimetric SAR observations / S. Hamed Alemohammad in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

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A temporal phase coherence estimation algorithm and its application on DInSAR pixel selection / Feng Zhao in IEEE Transactions on geoscience and remote sensing, vol 57 n° 11 (November 2019)  

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Combining machine learning and compact polarimetry for estimating soil moisture from C-Band SAR data / Emanuele Santi in Remote sensing, Vol 11 n° 20 (2 October 2019)  

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