Artificial Intelligence-Driven Methods for Remote Sensing Target and Object Detection (Third Edition)
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 966
Special Issue Editors
Interests: hyperspectral remote sensing image processing; target detection; dimensionality reduction; classification; metric learning; transfer learning; deep learning; lithologic mapping; geological application of remote sensing
Special Issues, Collections and Topics in MDPI journals
Interests: distance metric learning; few-shot learning; hyperspectral image analysis; statistical classification
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral imagery; remote sensing; intelligent processing; machine learning; pattern recognition
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Remote sensing images include rich descriptions of the Earth’s surface in various modalities (hyperspectral data, high resolution data, multispectral data, synthetic aperture radar (SAR) data, etc.). Remote sensing target detection and object detection aim to determine whether there are targets or objects of interest in an image, playing a decisive role in various fields, such as resource detection, environmental monitoring, urban planning, national security, agriculture, forestry, climate, hydrology, etc. In recent years, artificial intelligence (AI) has undergone considerable development and been successfully applied for various applications, such as regression, clustering, classification, etc. Although AI-driven approaches can handle the massive quantities of data acquired by remote sensors, they require many high-quality labeled samples to deal with remote sensing big data, leading to fragile results; in other words, AI-driven approaches with strong feature extraction abilities have limited performance and are still far from meeting practical demands. Thus, target detection and object detection in the presence of complicated backgrounds with limited labeled samples remains a challenging mission. There is still much room for research on remote sensing target detection and object detection. The main goal of this Special Issue is to address advanced topics related to remote sensing target detection and object detection.
Topics of interests include, but are not limited to, the following:
- New AI-driven methods for remote sensing data, such as GNN, transformers, etc.;
- New remote sensing datasets, including hyperspectral, high resolution, SAR, etc.;
- Machine learning techniques for remote sensing applications, such as domain adaptation, few-shot learning, manifold learning, and metric learning;
- Machine learning-based drone detection and fine-grained detection;
- Target detection, object detection, and anomaly detection;
- Data-driven applications in remote sensing;
- Technique reviews on related topics.
Dr. Yanni Dong
Dr. Xiaochen Yang
Prof. Dr. Qian Du
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 250 words) can be sent to the Editorial Office for assessment.
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
- target detection
- artificial intelligence
- machine learning
- deep learning
- object detection
- new datasets
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