Advances in Remote Sensing Image Target Detection and Recognition
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 30 December 2025 | Viewed by 151
Special Issue Editor
Interests: representation learning; object detection; few-shot learning; semantic segmentation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing image target detection and recognition is a hot research topic in computer vison and can effectively extract valuable information from massive accessible remote sensing imagery data, supporting intelligent interpretation systems for earth observation. However, certain sophisticated challenges severely impact the performance of remote sensing image target detection and recognition, hindering intelligent interpretation algorithms’ application in practical systems. Specifically, in situations such as long-tail distribution, few-shot learning, domain shifts, real-time processing requirement, and so on, previously designed remote sensing image target detection and recognition algorithms deliver inferior performance. Thus, new mechanisms and methods need to be explored to improve the learning robustness, processing efficiency, and generalization ability of remote sensing image target detection and recognition, which will be crucial for establishing next-generation remote sensing intelligent interpretation systems.
This Special Issue aims to drive the development of target detection and recognition in the remote sensing domain, establishing a next-generation remote sensing detection and recognition algorithm. Topics may involve semi-supervised learning, transfer learning, and few-shot learning for remote sensing object detection or recognition, while considering specific challenges of remote sensing target characters, i.e., multi-scale, arbitrary orientation, tiny or weak objects, and so on.
Suggested themes and article types include the following:
- Few-shot remote sensing object detection and recognition;
- Zero-shot remote sensing object detection and recognition;
- Cross-domain object detection and recognition in remote sensing domain;
- Pretraining technology for remote sensing object detection and recognition;
- Long-tail distribution object detection and recognition;
- Open-vocabulary object detection in remote sensing domain.
Dr. Yin Zhuang
Guest Editor
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
- semi-supervised learning
- few-shot learning
- long-tail distribution
- domain adaptation
- open-vocabulary
- zero-shot learning
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