AI-Based Obstacle Detection and Avoidance in Remote Sensing Images
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 March 2023) | Viewed by 7023
Special Issue Editors
Interests: applied machine learning for remote sensing
Interests: hyperspectral image classification and detection
Interests: multimodality imagery fusion and analysis
2. State Key Lab. of Integrated Service Networks, School of Telecommunications Engineering, Xidian University, Xi’an 710071, China
Interests: hyperspectal target and anomaly detection
Special Issue Information
Dear Colleagues,
Recently, intelligent agents have rapidly grown in remote sensing with their autonomy, flexibility, and a broad range of application domains. A variety of intelligent agents with autonomous navigation capabilities have been developed for different remote sensing application scenarios, such as Unmanned Ground Vehicle, Maritime Autonomous Surface Ship, Unmanned Aerial Vehicle, and Planetary Lander et.al. With the development of remote sensing technology, the performance of sensors carried by intelligent agents is becoming more and more powerful. Advanced remote sensing image offers a range of beneficial data for the application of intelligent agents with high spectral, temporal, and spatial resolution, as well as accurate and reliable environment information in a wide range of scenes. This provides the necessary data guarantee for the agent to complete complex missions in the field of remote sensing.
Obstacle detection and avoidance are fundamental problems for intelligent agents because they must detect and avoid obstacles in their environment according to the collected information. However, remote sensing images have the characteristics of large scenes, small targets, and complex backgrounds, which makes it challenging to quickly and intelligently detect long-distance obstacles and make reasonable avoidance strategies in remote sensing images. Moreover, the adaptability of agents still needs to be improved for addressing the obstacle detection and avoidance problem in complex scenarios, including unfamiliar environments, unknown obstacles, and dynamic scenes. The current development of artificial intelligence technology provides an intelligent solution paradigm for many visual perception and decision-making problems. The performance of obstacle detection and avoidance would be advanced by the inclusion of Artificial Intelligence techniques in the design of remote sensing applications.
This Special Issue aims to take advantage of the cutting-aged artificial intelligence technology, developing intelligence obstacle detection methods with strong adaptability and a high degree of autonomy. The research papers will provide readers of Remote Sensing with a wide range of remote sensing image understanding, obstacle detection and avoidance, and advanced artificial intelligence technology with theoretical research and practical methods.
To highlight new solutions of AI algorithms for obstacle detection and avoidance in remote sensing images, manuscript submissions are encouraged from a broad range of related topics, which may include but are not limited to the following activities:
- Artificial Intelligence Approaches for Remote Sensing Image Understanding;
- Artificial Intelligence Approaches for Obstacle Detection and Avoidance;
- Obstacle Adaptive Perception and Learning Approach in Dynamic Scenes;
- Autonomous Navigation and Obstacle Avoidance Based on Remote Sensing Images;
- Data-driven Application for Obstacle Detection and Avoidance;
- Detection of unseen or merely seen obstacles;
- Weakly/semi-supervised detection approaches for Obstacle Detection and Avoidance;
- Obstacle detection based on image saliency and camouflage cues.
Dr. Zengmao Wang
Dr. Yang Xu
Prof. Dr. Bin Xiao
Prof. Dr. Jie Lei
Prof. Dr. Dingwen Zhang
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- obstacle detection
- collision avoidance
- remote sensing image understanding
- autonomous navigation
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