Object Detection in Remote Sensing Images: Progress and Challenges
A special issue of Journal of Imaging (ISSN 2313-433X).
Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 3237
Special Issue Editors
Interests: remote sensing image processing; object detection; deep learning; oil pollution
Interests: ship detection; oil spill detection; SAR; marine radar images
Special Issue Information
Dear Colleagues,
Object detection in remote sensing images plays a crucial role in various fields, such as urban planning, land cover classification, disaster management, and environmental monitoring.
The importance of object detection lies in its ability to extract valuable information from remote sensing data, enabling better decision making and analysis. By accurately identifying and delineating objects, it becomes possible to monitor changes over time, assess the impact of natural disasters, detect illegal activities, and plan for infrastructure development. Additionally, object detection aids in the creation of detailed land cover maps, which are essential for urban growth modeling, precision agriculture, and conservation efforts.
However, object detection in remote sensing images also presents several challenges. These include the presence of complex backgrounds and variations in object appearances due to different imaging conditions, scale differences, and occlusions caused by vegetation or other objects. Furthermore, the large size of remote sensing datasets requires efficient algorithms capable of handling big data.
The aim of this Special Issue is to collate original research as well as review articles on the subject of object detection in remote sensing images. Potential topics include, but are not limited to, the following:
- Object detection;
- Remote sensing images;
- Urban planning;
- Environmental monitoring;
- Disaster management;
- Deep learning;
- Mapping;
- Real-time detection.
Dr. Binxin Liu
Dr. Peng Chen
Dr. Tong Wang
Guest Editors
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