Recent Trends for Image Restoration Techniques Used 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 (31 December 2023) | Viewed by 2940
Special Issue Editors
Interests: multi-source remote sensing data processing
Special Issues, Collections and Topics in MDPI journals
Interests: digital signal processing; signal processing; signal, image and video processing; image processing; digital image processing; wavelet analysis; image enhancement; image fusion; image analysis; machine learning
Interests: computer vision; deep learning; remote sensing; machine learning
2. Institute of Advanced Research in Artificial Intelligence (IARAI), 1030 Wien, Austria
Interests: hyperspectral image interpretation; multisensor and multitemporal data fusion
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
As an effective approach to acquiring knowledge of objects in the area of interest from a distance, Remote sensing is changing our world and the way we think. Recent years have witnessed fast technological progress and unprecedented influence in numerous fields, e.g., agriculture, forestry, weather, biodiversity, etc. Sophisticated remote sensing platforms and instruments are increasingly being launched to capture remote sensing images. However, the captured remote sensing images may not represent the true distribution of ground objects, and the received signals by imaging instruments may be degraded, owing to environmental disturbances, atmospheric effects, and sensors’ hardware limitations. These degradations dramatically reduce the quality and usefulness of remote sensing images. Therefore, restoring clean remote sensing images from degraded ones has been a hot topic to improve the quality of remote sensing images.
Image restoration is a classic inverse problem, involving many cutting-edge techniques in the fields of advanced signal processing, mathematical optimization, computer vision, artificial intelligence, etc. In consequence, image restoration is an interdisciplinary problem in remote sensing community, including but not limited to noise removal, distortion recovery&correction, resolution, super-resolution, image fusion, and registration. Additionally, the diversity of remote sensing modes (e.g., SAR, LiDAR, optical, multi/hyperspectral) also poses new methodological challenges to image restoration.
This Special Issue aims to review and synthesize the latest progress in image restoration techniques in remote sensing. Prospective authors are invited to contribute to this Special Issue of Remote Sensing by submitting an original manuscript.
Prof. Dr. Wei Li
Dr. Na Liu
Dr. Mengmeng Zhang
Dr. Pedram Ghamisi
Guest Editors
Manuscript Submission Information
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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
- remote sensing image denoising
- remote sensing image despeckling
- remote sensing image destriping
- remote sensing image deblurring
- remote sensing image dehazing and de-raining
- remote sensing image cloud removal
- multi-source image fusion
- remote sensing image completion
- low-rank tensor approximation for sensing image restoration
- deep learning for remote sensing image restoration
- feature extraction and dimension reduction
- image restoration for remote sensing applications
- remote sensing object detection
- segmentation and classification
- datasets, benchmarks, toolboxes, and open resources for remote sensing image restoration
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