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Applied Sciences
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New Approaches and Applications of Remote Sensing Image Restoration
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New Approaches and Applications of Remote Sensing Image Restoration

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Optics and Lasers".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 4364

Special Issue Editors

Prof. Dr. Hong Huang

E-Mail Website
Guest Editor
The College of Ophotoelectric Engineering, Chongqing University, Chongqing 400044, China
Interests: remote sensing; image processing; computer vision; deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Yule Duan

E-Mail Website
Guest Editor
College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China
Interests: deep learning; pattern recognition; remote sensing image classification; feature extraction

Special Issue Information

Dear Colleagues,

We are seeking submissions to a Special Issue on New Approaches and Applications of Remote Sensing Image Restoration.

Remote sensing images acquisition processes are generally influenced by various kinds of degradation, such as noise, geometric distortions, changes in illumination, blur (motion, atmospheric turbulence, out-of-focus), etc. Image restoration as an inverse imaging approach is becoming one of the central issues in the development of remote sensing, since it can estimate original images from the observed distorted ones. Remote sensing images restoration can be applied as a pre-processing technique to improve image quality, which supports further stages of data analysis, object detection and classification. Besides, image restoration can be used for remote sensing data at the post-processing stage, for reducing distortions caused by lossless coding of images (blocking and ringing artifacts).

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of remote sensing image restoration. Both theoretical and experimental studies are welcome, as well as comprehensive review and survey papers.

Prof. Dr. Hong Huang
Dr. Yule Duan
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. Applied Sciences 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 2400 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 deblurring
  • remote sensing image denoising
  • remote sensing image dehazing and de-raining
  • multimodal remote sensing image restoration
  • multi-sensor fusion remote sensing data restoration
  • deep learning for remote sensing image restoration
  • sparse representation for remote sensing image restoration
  • remote sensing image compression artifacts reduction
  • remote sensing image classification and clustering
  • remote sensing image detection
  • remote sensing image feature learning
  • spectral remote sensing image unmixing
  • spectral remote sensing image dimensionality reduction
  • remote sensing image super resolution
  • remote sensing image fusion
  • remote sensing image compression

Published Papers (2 papers)

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Research

12 pages, 2219 KiB  
Article
A GA and SVM Classification Model for Pine Wilt Disease Detection Using UAV-Based Hyperspectral Imagery
by Sulan Zhang, Hong Huang, Yunbiao Huang, Dongdong Cheng and Jinlong Huang
Appl. Sci. 2022, 12(13), 6676; https://doi.org/10.3390/app12136676 - 1 Jul 2022
Cited by 19 | Viewed by 1991
Abstract
Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, it is necessary to establish a feasible and effective method [...] Read more.
Pine wilt disease (PWD), caused by the pine wood nematode (Bursaphelenchus xylophilus), is a global destructive threat to forests and has led to serious economic losses all over the world. Therefore, it is necessary to establish a feasible and effective method to accurately monitor and estimate PWD infection. In this study, we used hyperspectral imagery (HI) collected by an unmanned airship with a hyperspectral imaging spectrometer to detect PWD in healthy, early, middle and serious infection stages. To avoid massive calculations on the full spectral dimensions of the HI, 16 spectral features were extracted from the HI, and a genetic algorithm (GA) was implemented to identify the optimal ones with the least fitness. Simultaneously, a support vector machine (SVM) classifier was established to predict the PWD infection stage for an individual pine tree. The following results were obtained: (1) the spectral characteristics for pine trees in different PWD infection stages were distinctive in the green region (510–580 nm), red edge (680–760 nm) and near-infrared (780–1000 nm) spectra; (2) the six optimal spectral features (Dgreen, SDgreen, Dred, DRE, DNIR, SDNIR) selected with the GA effectively distinguished the PWD infection stages of pine trees with a lower calculation cost; (3) compared with the traditional classifiers, such as k-nearest neighbor (KNN), random forest (RF) and single SVM, the proposed GA and SVM classifier achieved the highest overall accuracy (95.24%) and Kappa coefficient (0.9234). The approach could also be employed for monitoring and detecting other forest pests. Full article
(This article belongs to the Special Issue New Approaches and Applications of Remote Sensing Image Restoration)
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19 pages, 31670 KiB  
Article
Estimating Significant Wave Height from SAR with Long Integration Times
by Yawei Zhao, Jinsong Chong, Zongze Li, Xianen Wei and Lijie Diao
Appl. Sci. 2022, 12(5), 2341; https://doi.org/10.3390/app12052341 - 23 Feb 2022
Cited by 1 | Viewed by 1786
Abstract
Synthetic aperture radar (SAR) is an important means of estimating significant wave height with obvious advantages of all-day, all-weather, high resolution and wide swath coverage. At present, the estimation methods of significant wave height are based on visible ocean waves in SAR images. [...] Read more.
Synthetic aperture radar (SAR) is an important means of estimating significant wave height with obvious advantages of all-day, all-weather, high resolution and wide swath coverage. At present, the estimation methods of significant wave height are based on visible ocean waves in SAR images. However, due to the characteristic of long integration time for low-frequency SAR (such as P-band, L-band), the ocean waves are usually invisible in SAR images. In addition, in the case that there are multiple wave systems, significant wave height of only one wave system can be estimated for the reason that only a blurred wave system can be observed in SAR images. In order to solve the above two problems, a method of estimating significant wave height from SAR with long integration times is proposed in this paper. Firstly, each ocean wave system is refocused from single-look complex (SLC) data, respectively. Then, without any additional processing, the 180° ambiguity of wave propagation direction is removed based on the optimum focus setting. Finally, significant wave height is estimated in combination with azimuth cutoff, wavelength and propagation direction of ocean waves. This method is applied to two airborne SAR field data with long integration times. One case is that ocean waves are invisible in SAR images, the other is that there are two wave systems on the real ocean surface, but only one is visible in the SAR images. The results show that the proposed method can estimate significant wave height in the cases of invisible ocean waves and multiple ocean waves. The estimation results of significant wave height are compared with the European Centre for Medium-Range Weather Forecast (ECMWF) data, and the error is basically stable within 0.2 m, which verifies the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue New Approaches and Applications of Remote Sensing Image Restoration)
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