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A novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images / David Pirrone in IEEE Transactions on geoscience and remote sensing, vol 58 n° 7 (July 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 7 (July 2020) . - pp 4780 - 4795 Titre : A novel framework based on polarimetric change vectors for unsupervised multiclass change detection in dual-pol intensity SAR images Type de document : Article/Communication Auteurs : David Pirrone, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2020 Article en page(s) : pp 4780 - 4795 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] classification automatique [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] coordonnées polaires [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] méthode des vecteurs de changement [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] radar à antenne synthétique Résumé : (auteur) Change detection (CD) is a crucial topic in many remote sensing applications. In the recent years, satellite polarimetric synthetic aperture radar (PolSAR) systems (e.g., the Sentinel-1 constellation) became a suitable tool for multitemporal monitoring due to the regular acquisitions with a short revisit time in different polarimetric channels. Methods for CD in PolSAR data mainly focus on binary CD (i.e., they provide information about the presence/absence of change only), whereas the polarimetric enhanced information provides multiple features that can be exploited for performing multiclass CD. In this article, we introduce a novel framework for the characterization of multitemporal changes in dual-polarimetric data. The framework is based on the definition of polarimetric change vectors (PCVs) and their representation in a polar coordinate system. PCVs allow characterizing and, thus, to separate multiclass changes in terms of target properties of the single-time scenes and the scattering theory. The proposed model is used to: 1) derive the statistical behaviors of change and no change classes in PolSAR multitemporal images; 2) design an automatic and unsupervised strategy to estimate the optimal number of changes; and 3) distinguish no change from change classes and the kinds of change from each other. An experimental analysis has been conducted on three multitemporal PolSAR data sets having different complexities in terms of number and kinds of change classes. The results confirm the effectiveness of the proposed approach and the better performance with respect to both specific techniques for CD in dual-pol SAR data and a general multiclass CD method, not designed for PolSAR data. Numéro de notice : A2020-390 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2966865 date de publication en ligne : 04/02/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2966865 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95373 [article]

Polarimetric SAR calibration and residual error estimation when corner reflectors are unavailable / Lei Shi in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 4454 - 4471 Titre : Polarimetric SAR calibration and residual error estimation when corner reflectors are unavailable Type de document : Article/Communication Auteurs : Lei Shi, Auteur ; Pingxiang Li, Auteur ; Jie Yang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 4454 - 4471 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] bruit (théorie du signal) [Termes descripteurs IGN] coin réflecteur [Termes descripteurs IGN] dégradation du signal [Termes descripteurs IGN] données polarimétriques [Termes descripteurs IGN] étalonnage [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interruption du signal [Termes descripteurs IGN] polarimétrie radar [Termes descripteurs IGN] polarisation croisée [Termes descripteurs IGN] rétrodiffusion de Bragg Résumé : (auteur) In this article, we propose a polarimetric calibration (PolCal) algorithm to estimate the system crosstalk, cross-polarization (x-pol), and co-polarization (co-pol) channel imbalance (CI) when ground corner reflectors (CRs) are unavailable. The current PolCal process requires at least one trihedral CR to determine the co-pol CI. However, the deployment of ground CRs is costly and may even be impossible in some areas. To calibrate a polarimetric image without CRs, our proposed method automatically extracts the volume-dominated and Bragg-like pixels as a reference to estimate the crosstalk, x-pol, and co-pol CI values. Then, a first-order polynomial model is exploited to fit the co-pol CI to further improve calibration accuracy. In the experimental section, we demonstrate the effectiveness of our proposed method with data from two of China’s newly developed very high-resolution systems. The experiments confirmed that the proposed workflow can be considered as a feasible calibration scheme when the ground deployment of CRs is impossible, and it is also an effective analysis tool for the assessment of calibrated products. Numéro de notice : A2020-286 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2964732 date de publication en ligne : 20/01/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2964732 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95109 [article]

Subpixel SAR image registration through parabolic interpolation of the 2-D cross correlation / Luca Pallotta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020) . - pp 4132 - 4144 Titre : Subpixel SAR image registration through parabolic interpolation of the 2-D cross correlation Type de document : Article/Communication Auteurs : Luca Pallotta, Auteur ; Gaetano Giunta, Auteur ; Carmine Clemente, Auteur Année de publication : 2020 Article en page(s) : pp 4132 - 4144 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] analyse infrapixellaire [Termes descripteurs IGN] corrélation croisée normalisée [Termes descripteurs IGN] image de drone [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] interpolation [Termes descripteurs IGN] précision infrapixellaire Résumé : (auteur) In this article, the problem of synthetic aperture radar (SAR) images coregistration is considered. In particular, a novel algorithm aimed at achieving a fine subpixel coregistration accuracy is developed. The procedure is based on the parabolic interpolation of the 2-D cross correlation computed between the two SAR images to be aligned. More precisely, from the 2-D cross correlation, a neighborhood of its peak value is extracted and the interpolation of both the 2-D paraboloid and the two alternative 1-D parabolas is computed to provide the finer misregistration estimation with subpixel accuracy. The main advantage of the proposed framework is that the overall computational burden is only due to the 2-D cross correlation estimation since the parabolic interpolation is calculated with a closed-form expression. The results obtained on real recorded unmanned aerial vehicle (UAV) SAR data highlight the effectiveness of the proposed approach as well as its capabilities to provide some benefits with respect to other available strategies. Numéro de notice : A2020-284 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2961245 date de publication en ligne : 15/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2961245 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95107 [article]

Unsupervised change detection between SAR images based on hypergraphs / Jun Wang in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 164 (June 2020) . - pp 61 - 72 Titre : Unsupervised change detection between SAR images based on hypergraphs Type de document : Article/Communication Auteurs : Jun Wang, Auteur ; Xuexi Yang, Auteur ; Xiangyu Yang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] approche pixel [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] corrélation automatique de points homologues [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] Hypergraph Based Data Structure [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] partition des données [Termes descripteurs IGN] précision de la classification Résumé : (auteur) The performance of synthetic aperture radar (SAR) image change detection is mainly relied on the quality of the difference image and the accuracy of the classification method. Considering the above mentioned issues, this paper proposes an unsupervised framework for SAR image change detection in which each pixel is taken as a vertex and the collection of pixels is represented by hyperedges in a hypergraph. Thus, the task of SAR image change detection is formulated as the problem of hypergraph matching and hypergraph partition. First, instead of using the K nearest neighbour rule, we propose a coupling neighbourhood based on the spatial-intensity constraint to gather the neighbours for each vertex. Then, hyperedges are constructed on the pixels and their coupling neighbours. The weight of hyperedge is computed via the sum of the patch-based pairwise affinities within the hyperedge. Through modelling the two hypergraphs on the bi-temporal SAR images, not only the change level of vertices is described, but also the changes of local grouping and consistency within hyperedge are excavated. Thus, the difference image with a good separability can be obtained by matching each vertex and hyperedge between the two hypergraphs. Finally, a generalized hypergraph partition technique is employed to classify changed and unchanged areas in the generated difference image. Experimental results on real SAR datasets confirm the validity of the proposed framework in improving the robustness and accuracy of change detection. Numéro de notice : A2020-251 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.04.007 date de publication en ligne : 19/04/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.04.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94995 [article]

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Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data / Thota Sivasankar in Geocarto international, Vol 35 n° 8 ([01/06/2020])  

Public [article]  inGeocarto international > Vol 35 n° 8 [01/06/2020] . - pp 905 - 915 Titre : Wheat leaf area index retrieval using RISAT-1 hybrid polarized SAR data Type de document : Article/Communication Auteurs : Thota Sivasankar, Auteur ; Dheeraj Kumar, Auteur ; Hari Shanker Srivastava, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 905 - 915 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] bande C [Termes descripteurs IGN] blé (céréale) [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Risat-1 [Termes descripteurs IGN] indice foliaire [Termes descripteurs IGN] polarisation [Termes descripteurs IGN] régression non linéaire [Termes descripteurs IGN] rétrodiffusion [Termes descripteurs IGN] séparateur à vaste marge [Termes descripteurs IGN] surveillance de la végétation Résumé : (auteur) Leaf Area Index (LAI) is a key parameter to characterize the canopy–atmosphere interface, where most of the energy fluxes exchange. Space-borne satellite images have shown their relevance for various applications including LAI retrieval over large areas. Although optical data have been used for this purpose in previous studies, the constraints to acquire optical data during extreme weather conditions due to the presence of clouds, haze, smoke etc. hinders its use for uninterrupted monitoring. This study aims to analyze the relationships of C-band RISAT-1 hybrid polarized SAR data (σ˚RH and σ˚RV) with wheat LAI. The results have shown the correlation coefficient (|r|) of 0.57 and 0.73 for RH and RV backscatter, respectively, using non-linear regression approach. It is also observed that the accuracy of LAI retrieval has been significantly improved with |r| and RMSE of 0.81 and 0.54 (m2/m2), respectively, by considering both RH and RV backscatter as inputs for support vector machine-based model. Numéro de notice : A2020-341 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10106049.2019.1566404 date de publication en ligne : 07/02/2019 En ligne : https://doi.org/10.1080/10106049.2019.1566404 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95219 [article]

Fusing adjacent-track InSAR datasets to densify the temporal resolution of time-series 3-D displacement estimation over mining areas with a prior deformation model and a generalized weighting least-squares method / Yuedong Wang in Journal of geodesy, vol 94 n° 5 (May 2020)  

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Incorporating Sentinel-1 SAR imagery with the MODIS MCD64A1 burned area product to improve burn date estimates and reduce burn date uncertainty in wildland fire mapping / Kristofer Lasko in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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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)  

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Saliency-guided single shot multibox detector for target detection in SAR images / Lan Du in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Soil moisture estimation with SVR and data augmentation based on alpha approximation method / Wei Xu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

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Adaptive Statistical Superpixel Merging With Edge Penalty for PolSAR Image Segmentation / Deliang Xiang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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What, Where, and How to Transfer in SAR Target Recognition Based on Deep CNNs / Zhongling Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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Radar Vegetation Index for assessing cotton crop condition using RISAT-1 data / Dipanwita Haldar in Geocarto international, vol 35 n° 4 ([15/03/2020])  

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

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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)  

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