Advances and Challenges on Multisource Remote Sensing Image Fusion: Datasets, New Technologies, and Applications (Second Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 January 2025
Special Issue Editors
Interests: Remote Sensing Image Processing; Photogrammetry; 3D Reconstruction; Light Field
Special Issues, Collections and Topics in MDPI journals
Interests: computer vision; photogrammetry; remote sensing; optical satellite image processing
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Today, various types of remote sensing images have been developed, including optical/near-infrared satellite images, SAR images, LiDAR intensity/depth images, thermal images, vector map images, etc. Each source of images encodes one aspect of information, and thus the fusion of different sources of images is conducive to the comprehensive utilization of their advantages. For example, SAR data can be effectively applied to geometrically orient optical satellite images without additional control points, exploiting the high geopositioning accuracy of SAR data. Image fusion has been researched for decades, and a range of techniques related to photogrammetry, computer vision, and artificial intelligence have been developed. However, accurate multisource remote sensing image fusion is still challenging, due to (1) large nonlinear intensity differences between the different sources of images; (2) ineffective rotation-handling strategies; (3) the absence of large-scale, multisource remote sensing image datasets that include various types of images; and (4) a lack of remote sensing images oriented to deep learning frameworks which consider the spectral characteristics, geoscientific prior knowledge, etc. The topics of this Special Issue include, but are not limited to, the following:
- Large-scale, multisource remote sensing image datasets;
- Multisource remote sensing data fusion methods that are robust when it comes to scale, rotation, and nonlinear radiation change;
- Machine learning (including deep learning, multitask learning, and transfer learning) for multisource remotely sensed images;
- Multisource image fusion for remote sensing applications, including, but not limited to, geometric orientation, 3D reconstruction, change detection, segmentation, etc.
Dr. Yuxuan Liu
Prof. Dr. Li Zhang
Dr. Zhihua Hu
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- remote sensing image data
- multisource remote sensing image processing
- data fusion
- deep learning
- remote sensing image-based application
- computer vision
