ISPRS Journal of Photogrammetry and Remote Sensing
Call for Papers on ISPRS Journal of Photogrammetry and Remote Sensing: “Multi-view Satellite image processing”
• 大类 : 工程技术 - 1区
• 小类 : 自然地理 - 1区
• 小类 : 地球科学综合 - 1区
• 小类 : 成像科学与照相技术 - 1区
• 小类 : 遥感 - 1区
The increasing availability of spaceborne imaging sensors and satellite constellations has driven great interest in acquiring useful geospatial datasets at a global scale at a wide range of resolutions. Running 24/7, these sensors collect a vast amount of data, generating multiple views of places of interest over the earth and planet surfaces. Although photogrammetric concepts of turning these images into topographical information are not new, their practical potential for remote sensing applications was only brought up in recent years thanks to the incremental development of advanced and automated geometric processing techniques. Such data, with its increased spatial, spectral and temporal resolutions, volume, and sensor heterogeneities, bring unprecedented opportunities. However, challenges remain for photogrammetric processing, thematic mapping, 3D modeling, dynamic analysis, data fusion and their respective applications in remote sensing.
The “Multi-view satellite image processing” theme issue aims to provide a collection of current, state-of-the-art research in multi-view or satellite image 3D modeling and its use for remote sensing applications. Original contributions that deal with spaceborne stereo or multi-view data from the basic radiometric and geometric processing level to applications with multi-view or stereo data as part of their data sources are considered relevant to this theme issue. Example topics include but are not limited to:
Spaceborne sensor modeling, radiometric and geometric calibration, and quality assurance for multi-view satellite data
Bundle adjustment, multi-view and stereo view 3D reconstruction from along track and multi-date across track orbital data or using incidental multi-satellite collections (optical or Synthetic Aperture Radar (radargrammetry) )
3D semantic interpretation, transfer learning, multi-angle feature extraction, supervised/weakly supervised and unsupervised classification using classic and deep learning methods.
3D Change detection, time-series analysis for multi-temporal datasets
Contributions that integrate additional data sources such as terrestrial/oblique (for cross-view analysis), airborne data as well as other modality data such as SAR, LiDAR and hyperspectral data with multi-view and stereo satellite data.
Multi-view satellite data as the data source (or part of the data source) for urban, environmental, geological, and civilian applications.
The contributions should be original and have not been published or submitted elsewhere. Papers published or submitted for conference publications may be considered subject to significant extension to their original version. Papers must follow the instructions for authors athttp://www.elsevier.com/journals/isprs-journal-of-photogrammetry-and-remote-sensing/0924-2716/guide-for-authors.