Deep Learning for Remote Sensing in Data Scarce Regimes
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 28295
Special Issue Editors
Interests: machine learning in data scarce learning regimes
Interests: deep learning; reinforcement learning; optimizations; multiagent systems; materials informatics; remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: vegetation phenology; climate change; ecohydrology
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Special Issue Information
Dear Colleagues,
Recent advances in deep learning have led to high-performance supervised learning algorithms for the domain of electrooptical (EO) images. This success, however, is conditioned on generating huge annotated datasets using modern crowdsourcing data annotation platforms such as Amazon Mechanical Turk that recruit ordinary people for data annotation. Unlike the EO domain, data annotation in remote sensing domains is substantially more challenging, and for various reasons, using crowdsourcing platforms is not feasible. As a result, we frequently encounter data scarcity in solving supervised deep learning in remote sensing applications. This Special Issue serves as an outlet for articles covering but not limited to:
- Cross-domain transfer learning for remote sensing applications;
- Domain adaptation using synthetic data in remote sensing applications;
- Zero-shot and few-shot learning in remote sensing applications;
- Efficient approaches for remote sensing data annotation.
Dr. Mohammad Rostami
Dr. Senthilnath Jayavelu
Prof. Dr. Yongshuo Fu
Guest Editors
Manuscript Submission Information
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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.
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Keywords
- data-scarce learning regime
- zero-shot learning
- domain adaptation
- transfer learning
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