Special Issue "Deep Learning and Computer Vision in Remote Sensing-III"
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 29 February 2024 | Viewed by 130
Special Issue Editors
2. Information Solutions, Geological Survey of Finland GTK, Espoo, Finland
Interests: computer vision; sensor fusion; autonomous systems; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; deep learning; data analysis
Special Issues, Collections and Topics in MDPI journals
Pouya Jafarzadeh
Interests: machine learning; deep learning; computer vision; data analysis; pose estimation
Interests: artificial intelligence; machine learning; deep learning; human-computer interaction
Special Issue Information
Dear Colleagues,
Deep Learning (DL) has been successfully applied to a wide range of computer vision tasks, exhibiting state-of-the-art performance. For this reason, most data fusion architectures for computer vision tasks are built based on DL. In addition, DL harbors the great potential to process multi-sensory data, which usually contain rich information in the raw data and are sensitive to the training time and model size.
We are pleased to announce this Part III Special Issue, which will follow on from Part I and II, focusing on deep learning and computer vision methods for remote sensing. This Special Issue will provide researchers with the opportunity to present the recent advances in deep learning, with a specific focus on three main computer vision tasks: classification, detection and segmentation. We seek collaborative contributions from academia and industry experts in the fields of deep learning, computer vision, data science, and remote sensing.
The scope of this Special Issue includes, but is not limited to, the following topics:
- Satellite image processing and analysis based on deep learning;
- Deep learning for object detection, image classification, and semantic and instance segmentation;
- Deep learning for remote sensing scene understanding and classification;
- Transfer learning and deep reinforcement learning for remote sensing;
- Supervised and unsupervised representation learning for remote sensing environments;
- Applications
Dr. Fahimeh Farahnakian
Prof. Dr. Jukka Heikkonen
Guest Editors
Pouya Jafarzadeh
Farshad Farahnakian
Guest Editor Assistants
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
- Computer vision
- Deep learning
- Machine learning
- Remote sensing
- Sensor fusion
- Autonomous systems
