Image Change Detection Research in Remote Sensing
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 (30 November 2022) | Viewed by 38582
Special Issue Editors
Interests: photogrammetry; remote sensing; UAV; dense image matching; deep learning; image quality; image classification
Special Issues, Collections and Topics in MDPI journals
Interests: GPS; GLONASS; Galileo; SBAS; GBAS; accuracy; EGNOS; aircraft position; GNSS satellite positioning; accuracy analysis; elements of exterior orientation; UAV positioning; UAV orientation; UAV navigation; flight parameters of UAV
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
This Special Issue will focus on new change detection trends in remote sensing.
Change detection based on modern image processing algorithms and remote sensing data is important for monitoring changes on the Earth’s surface. Change detection is used both in military (e.g., imagery intelligence) and civilian areas. Examples of civilian applications include urban planning, environmental monitoring, precision agriculture, monitoring of land changes, and analysis of the movement of objects. In recent years, with the intensive development of many remote sensing platforms and deep learning algorithms, research into new methods of change detection has become increasingly important. The possibility of integrating data from many sources (e.g., radar and optical data), as well as the analysis of time series of navigation data, also play an important role.
Modern Remote Sensing software also offers many possibilities; thanks to the intensive development of change detection algorithms, this software allows the implementation of many remote sensing studies based not only on images obtained in the visible range, but also multispectral images, radar data, and laser scanning data. An interesting research issue also relates to problems in the implementation of deep learning methods for change detection, object tracking, and image understanding.
Thanks to the increasing availability of multi-source image data and new data processing methods based often on artificial intelligence, the proposed Special Issue of Remote Sensing will discuss the latest achievements and development directions of change detection methods and their practical application.
Dr. Damian Wierzbicki
Dr. Kamil Krasuski
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
- UAV, aerial, and satellite data fusion
- Radar and optical data fusion
- Image matching and co-registration
- Multi-temporal data classification
- Land use change
- Deep learning for change detection
- Deep learning for time-series analysis
- Deep learning for image processing and classification
- Deep learning for image understanding
- 3D change detection
- GNSS and image data fusion for change detection
- Image scene analysis
- Image quality assessment
- IMINT
- Artificial intelligence
- Digital terrain model (DTM)
- Digital surface model (DSM)
- Multitemporal
- Multispectral images
- Unsupervised classification
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