Emerging Research in Target Detection and Recognition in Remote Sensing Images
Deadline for manuscript submissions: 31 October 2024 | Viewed by 2095
Interests: intelligent interpretation of remote sensing images; remote sensing image object detection; remote sensing image target recognition
Interests: synthetic aperture radar (SAR); polarimetric SAR image processing; remote sensing
In the field of Earth observation, the massive remote sensing data obtained by a large number of in orbit satellites or aircrafts brings more observational information and processing challenges for remote sensing image interpretation. The detection, recognition, and tracking of various types of high-value artificial targets on the ground and sea has become one of the hotspots in the processing and application of Earth observation information. In recent years, a large amount of rapid target detection, recognition, and tracking methods based on artificial intelligence technology have brought many beneficial solutions to the processing of remote sensing Earth observation information, and have had a significant impact in the field of remote sensing. They have provided promising tools for solving many challenging issues in accuracy and reliability of target detection and recognition in remote sensing images.
In this special issue, we plan to compile a series of papers to report on new methods and technologies for object detection and recognition in remote sensing images that have emerged in recent years. We anticipate that new research will leverage new methods and technologies such as artificial intelligence to solve more practical problems in remote sensing image applications.
The article may cover, but is not limited to, the following topics:
- Advanced artificial intelligence based object detection/recognition/tracking;
- Remote sensing image change detection/semantic segmentation;
- Remote sensing multi-sensor data fusion/multimodal data analysis;
- Remote sensing image super-resolution/restoration;
- Unsupervised/weak supervised learning for remote sensing image target detection or recognition;
- Advanced artificial intelligence technology for remote sensing applications;
- Intelligent detection and recognition of composite targets based on knowledge graphs or knowledge reasoning.
Dr. Tao Tang
Dr. Canbin Hu
Dr. Yuli Sun
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. Information is an international peer-reviewed open access monthly 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 1600 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.
- remote sensing
- image processing
- image interpretation
- target detection
- deep networks
- knowledge graphs
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Remote Object Tracking Based on Optical Flow Reconstruction of Motion-Group Parameters
Authors: Simeon Karpuzov; George Petkov; Sylvia Ilieva; Alexander Petkov; Stiliyan Kalitzin
Affiliation: Image Sciences Institute, University Medical Center Utrecht
Abstract: Object tracking has significance in many applications ranging from control of unmanned vehicles to autonomous monitoring of specific situations and events, especially when providing safety for patients with specific adverse conditions such as epileptic seizures. Conventional tracking methods face many challenges, such as the need for dedicated attached devices or tags, high image noise, complex object movements and intensive computational requirements. Method. We propose a novel optical flow-based method for object tracking. It utilizes real-time image sequences from the camera and reconstructs global motion-group parameters of the content. A rectangular region of interest surrounding the moving object can be steered by these parameters to follow the target. The method successfully applies to multi-spectral data, further improving its effectiveness. Results. Experimental results on simulated tests and complex real-world data demonstrate the method's capabilities. The proposed optical flow reconstruction provides accurate, robust and faster results as compared to both pixel-level algorithms and segmentation-based approaches.