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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 1765

Special Issue Editors

Prof. Dr. Wei Li
E-Mail Website
Guest Editor
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: hyperspectral image analysis; object detection
Special Issues, Collections and Topics in MDPI journals
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
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
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Interests: computer vision; deep learning; remote sensing; machine learning
1.Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf (HZDR), D-09599 Freiberg, Germany
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

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

  • 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

Published Papers (1 paper)

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Research

21 pages, 7156 KiB  
Article
Thick Cloud Removal in Multi-Temporal Remote Sensing Images via Frequency Spectrum-Modulated Tensor Completion
Remote Sens. 2023, 15(5), 1230; https://doi.org/10.3390/rs15051230 - 23 Feb 2023
Cited by 2 | Viewed by 1129
Abstract
Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images. However, existing methods ignore different low-rank properties in the spatial and [...] Read more.
Clouds often contaminate remote sensing images, which leads to missing land feature information and subsequent application degradation. Low-rank tensor completion has shown great potential in the reconstruction of multi-temporal remote sensing images. However, existing methods ignore different low-rank properties in the spatial and temporal dimensions, such that they cannot utilize spatial and temporal information adequately. In this paper, we propose a new frequency spectrum-modulated tensor completion method (FMTC). First, remote sensing images are rearranged as third-order spatial–temporal tensors for each band. Then, Fourier transform (FT) is introduced in the temporal dimension of the rearranged tensor to generate a spatial–frequential tensor. In view of the fact that land features represent low-frequency components and fickle clouds represent high-frequency components in the time domain, we chose adaptive weights for the completion of different low-rank spatial matrixes, according to the frequency spectrum. Then, Invert Fourier Transform (IFT) was implemented. Through this method, the joint low-rank spatial–temporal constraint was achieved. The simulated data experiments demonstrate that FMTC is applicable on different land-cover types and different missing sizes. With real data experiments, we have validated the effectiveness and stability of FMTC for time-series remote sensing image reconstruction. Compared with other algorithms, the performance of FMTC is better in quantitative and qualitative terms, especially when considering the spectral accuracy and temporal continuity. Full article
(This article belongs to the Special Issue Recent Trends for Image Restoration Techniques Used in Remote Sensing)
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