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A simplified ICA-based local similarity stereo matching / Suting Chen in The Visual Computer, vol 37 n° 2 (February 2021)  

Public [article]  inThe Visual Computer > vol 37 n° 2 (February 2021) . - pp 411 - 419 Titre : A simplified ICA-based local similarity stereo matching Type de document : Article/Communication Auteurs : Suting Chen, Auteur ; Jinglin Zhang, Auteur ; Meng Jin, Auteur Année de publication : 2021 Article en page(s) : pp 411 - 419 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse en composantes indépendantes [Termes IGN] appariement d'images [Termes IGN] similitude Résumé : (auteur) Since the existing stereo matching methods may fail in the regions of non-textures, boundaries and tiny details, a simplified independent component correlation algorithm (ICA)-based local similarity stereo matching algorithm is proposed. In order to improve the DispNetC, the proposed algorithm first offers the simplified independent component correlation algorithm (SICA) cost aggregation. Then, the algorithm introduces the matching cost volume pyramid, which simplifies the pre-processing process for the ICA. Also, the SICA loss function is defined. Next, the region-wise loss function combined with the pixel-wise loss function is defined as a local similarity loss function to improve the spatial structure of the disparity map. Finally, the SICA loss function is combined with the local similarity loss function, which is defined to estimate the disparity map and to compensate the edge information of the disparity map. Experimental results on KITTI dataset show that the average absolute error of the proposed algorithm is about 37% lower than that of the DispNetC, and its runtime consuming is about 0.6 s lower than that of GC-Net. Numéro de notice : A2021-176 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00371-020-01811-x Date de publication en ligne : 15/02/2020 En ligne : https://doi.org/10.1007/s00371-020-01811-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97286 [article]

Toward a standardized encoding of remote sensing geo-positioning sensor models / Meng Jin in Remote sensing, vol 12 n° 9 (May 2020)  

Public [article]  inRemote sensing > vol 12 n° 9 (May 2020) . - n° 1530 Titre : Toward a standardized encoding of remote sensing geo-positioning sensor models Type de document : Article/Communication Auteurs : Meng Jin, Auteur ; Yuqi Bai, Auteur ; Emmanuel Devys , Auteur ; Liping Di, Auteur Année de publication : 2020 Projets : 3-projet - voir note / Article en page(s) : n° 1530 Note générale : bibliographie M.J. and Y.B. were funded by the National Key Research and Development Program of China (No. 2016YFF0202705, PI: Jiankun Guo). L.D. was funded by USGS/FGDC (No. G15AC00508, PI: Liping Di). Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] données localisées [Termes IGN] interopérabilité [Termes IGN] Sensor Web Enablement [Termes IGN] SensorML [Termes IGN] standard OGC Résumé : (auteur) Geolocation information is an important feature of remote sensing image data that is captured through a variety of passive or active observation sensors, such as push-broom electro-optical sensor, synthetic aperture radar (SAR), light detection and ranging (LIDAR) and sound navigation and ranging (SONAR). As a fundamental processing step to locate an image, geo-positioning is used to determine the ground coordinates of an object from image coordinates. A variety of sensor models have been created to describe geo-positioning process. In particular, Open Geospatial Consortium (OGC) has defined the Sensor Model Language (SensorML) specification in its Sensor Web Enablement (SWE) initiative to describe sensors including the geo-positioning process. It has been realized using syntax from the extensible markup language (XML). Besides, two standards defined by the International Organization for Standardization (ISO), ISO 19130-1 and ISO 19130-2, introduced a physical sensor model, a true replacement model, and a correspondence model for the geo-positioning process. However, a standardized encoding for geo-positioning sensor models is still missing for the remote sensing community. Thus, the interoperability of remote sensing data between application systems cannot be ensured. In this paper, a standardized encoding of remote sensing geo-positioning sensor models is introduced. It is semantically based on ISO 19130-1 and ISO 19130-2, and syntactically based on OGC SensorML. It defines a cross mapping of the sensor models defined in ISO 19130-1 and ISO 19130-2 to the SensorML, and then proposes a detailed encoding method to finalize the XML schema (an XML schema here is the structure to define an XML document), which will become a profile of OGC SensorML. It seamlessly unifies the sensor models defined in ISO 19130-1, ISO 19130-2, and OGC SensorML. By enabling a standardized description of sensor models used to produce remote sensing data, this standard is very promising in promoting data interoperability, mobility, and integration in the remote sensing domain. Numéro de notice : A2020-333 Affiliation des auteurs : IGN+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12091530 Date de publication en ligne : 11/05/2020 En ligne : https://doi.org/10.3390/rs12091530 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96867 [article]