Détail de l'auteur
Auteur Ling Chang |
Documents disponibles écrits par cet auteur (2)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
![Tris disponibles](./images/orderby_az.gif)
Spatio-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)
![]()
[article]
Titre : Spatio-temporal linking of multiple SAR satellite data from medium and high resolution Radarsat-2 images Type de document : Article/Communication Auteurs : Bin Zhang, Auteur ; Ling Chang, Auteur ; Alfred Stein, Auteur Année de publication : 2021 Article en page(s) : pp 222 - 236 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] déformation de surface
[Termes IGN] données spatiotemporelles
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] points homologues
[Termes IGN] série temporelleRésumé : (auteur) A recent development in Interferometric Synthetic Aperture Radar (InSAR) technology is integrating multiple SAR satellite data to dynamically extract ground features. This paper addresses two relevant challenges: identification of common ground targets from different SAR datasets in space, and concatenation of time series when dealing with temporal dynamics. To address the first challenge, we describe the geolocation uncertainty of InSAR measurements as a three-dimensional error ellipsoid. The points, among InSAR measurements, which have error ellipsoids with a positive cross volume are identified as tie-point pairs representing common ground objects from multiple SAR datasets. The cross volumes are calculated using Monte Carlo methods and serve as weights to achieve the equivalent deformation time series. To address the second challenge, the deformation time series model for each tie-point pair is estimated using probabilistic methods, where potential deformation models are efficiently tested and evaluated. As an application, we integrated two Radarsat-2 datasets in Standard and Extra-Fine modes to map the subsidence of the west of the Netherlands between 2010 and 2017. We identified 18128 tie-point pairs, 5 intersection types of error ellipsoids, 5 deformation models, and constructed their long-term deformation time series. The detected maximum mean subsidence velocity in Line-Of-Sight direction is up to 15 . We conclude that our method removes limitations that exist in single-viewing-geometry SAR when integrating multiple SAR data. In particular, the proposed time-series modeling method is useful to achieve a long-term deformation time series of multiple datasets. Numéro de notice : A2021-414 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.04.005 Date de publication en ligne : 08/05/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.04.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97745
in ISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 222 - 236[article]A probabilistic approach for InSAR time-series postprocessing / Ling Chang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)
![]()
[article]
Titre : A probabilistic approach for InSAR time-series postprocessing Type de document : Article/Communication Auteurs : Ling Chang, Auteur ; Ramon F. Hanssen, Auteur Année de publication : 2016 Article en page(s) : pp 421 - 430 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] estimation statistique
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] post-traitement
[Termes IGN] processus stochastique
[Termes IGN] série temporelleRésumé : (Auteur) Monitoring the kinematic behavior of enormous amounts of points and objects anywhere on Earth is now feasible on a weekly basis using radar interferometry from Earth-orbiting satellites. An increasing number of satellite missions are capable of delivering data that can be used to monitor geophysical processes, mining and construction activities, public infrastructure, or even individual buildings. The parameters estimated from these data are used to better understand various natural hazards, improve public safety, or enhance asset management activities. Yet, the mathematical estimation of kinematic parameters from interferometric data is an ill-posed problem as there is no unique solution, and small changes in the data may lead to significantly different parameter estimates. This problem results in multiple possible outcomes given the same data, hampering public acceptance, particularly in critical conditions. Here, we propose a method to address this problem in a probabilistic way, which is based on multiple hypotheses testing. We demonstrate that it is possible to systematically evaluate competing kinematic models in order to find an optimal model and to assign likelihoods to the results. Using the B-method of testing, a numerically efficient implementation is achieved, which is able to evaluate hundreds of competing models per point. Our approach will not solve the nonuniqueness problem of interferometric synthetic aperture radar (InSAR), but it will allow users to critically evaluate (conflicting) results, avoid overinterpretation, and thereby consolidate InSAR as a geodetic technique. Numéro de notice : A2016-078 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2459037 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2459037 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79858
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 421 - 430[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2016011 SL Revue Centre de documentation Revues en salle Disponible