Advanced Techniques in Remote Sensing for Object Detection: From Few-Shot Learning to Open Vocabulary Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 50
Special Issue Editors
Interests: representation learning; object detection; few-shot learning; semantic segmentation
Interests: remote sensing object detection; multimodal large language models (MLLM); domain adaptation
Interests: object detection; onboard processing; multimodal fusion
Interests: object detection; multimodal learning; machine learning foundation model
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid advancement of remote sensing technologies and machine learning has opened up new avenues for the detection and analysis of objects in remote sensing images. This Special Issue is dedicated to exploring cutting-edge methodologies that enhance the ability of remote sensing to perform object detection through innovative learning frameworks and cross-domain applications.
We aim to showcase research that addresses critical challenges in remote sensing, with a focus on the following areas of interest:
- Few-Shot Learning for Remote Sensing Data: Developing models that effectively learn from limited labeled data to perform generalization across diverse remote sensing datasets well.
- Domain Adaptation in Remote Sensing: Enhancing the robustness of remote sensing models by enabling them to adapt across different domains, overcoming the challenges posed by the variability in environmental conditions and sensor specifications.
- Open Vocabulary Object Detection: Exploring methodologies that enable the detection of previously undefined or rare objects in remote sensing images, thus broadening the scope of detectable features without extensive retraining.
- Representation Learning from Remote Sensing Data: Investigating innovative approaches to represent and extract meaningful features from remote sensing data that significantly enhance the accuracy and efficiency of object detection.
- Pre-Training Methods for Object Detection Models: Examining strategies that facilitate the pre-training of models on diverse datasets to enhance their performance and adaptability in object detection tasks within remote sensing contexts.
- Advanced Multi-Temporal Change Detection: Finding critical changed information from multi-temporal remote sensing images that experience very complicated background interferences.
Contributions may include novel theoretical approaches, practical applications, and comprehensive reviews that contribute to the fields of environmental monitoring, urban planning, agricultural assessment, and beyond. We encourage submissions that not only present significant research findings, but also demonstrate the practical implications and applications of these advanced remote sensing techniques.
This Special Issue aims to gather contributions detailing diverse perspectives and methodologies in order to advance the boundaries of remote sensing in object detection, thus offering both insights into fundamental processes and frameworks for applied sciences.
Dr. Yin Zhuang
Dr. Guanqun Wang
Dr. Boya Zhao
Dr. Yue Zhang
Dr. Wenjia Xu
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.
Keywords
- object detection
- few-shot learning
- open vocabulary
- model pretraining
- domain adaptation
- representation learning
- change detection
