Dear Colleagues,

With the development of photogrammetry and remote sensing, more and more new sensor-carrying platforms and technologies are being introduced, significantly changing traditional ways of working with photogrammetry and remote sensing, as well as the degree of intelligence in algorithms. For example, the development of drones from the air to underwater has greatly expanded its field of applications, shortened the distance between the sensors and the research objects, and turned it into a useful technology that everyone can afford. Meanwhile, the impact and promotion of deep learning in remote sensing has extended its functions from the geometric information of the research target to the new era of semantics, which consistently brings hope and surprise to researchers and the public. The neural network-based learning architecture has been widely used in the fields of image matching, SLAM, three-dimensional reconstruction, target recognition and classification, and segmentation. Many problems have also arisen in the continuous expansion of application fields and intelligent transformation of photogrammetry and remote sensing. For example, the data quality problem caused by the mass consumer-grade sensors used in the new drone platforms. In the field of pure geometry, deep learning is still unable to achieve the orientation and positioning accuracy of classic methods such as bundle adjustment and SFM. In image matching and 3D geometric reconstruction, deep learning often requires a larger training data set to achieve sufficiently good results. In the work based on image semantic extraction and classification, although deep learning has been fully ahead of traditional machine learning methods, it is still limited by a large number of professional annotation work and much-needed model generalization capabilities. Therefore, there are still many scientific and technological problems in these fields that are worthy of research and innovation.

Potential topics include, but are not limited to: camera calibration; aerial triangulation; dense matching; 3D reconstruction; indoor drones; visual SLAM; autonomous underwater vehicles; underwater moving target monitoring; future perspectives for UAV photogrammetry and remote sesning; object recognition and segmentation; vision-based underwater manipulator control; semantic mapping.

Dr. Ming Li
Prof. Dr. Zhaohui Li
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.

• Drone photogrammetry
• Underwater photogrammetry
• Remote sensing
• Deep learning
• Image matching
• Semantic segmentation
• 3D reconstruction
• Dense matching
• Objective detection