Special Issue "Image Super-Resolution 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: 31 December 2019.

Special Issue Editors

Dr. Igor Yanovsky
E-Mail Website
Guest Editor
Jet Propulsion Laboratory, California Institute of Technology and Joint Institute for Regional Earth System Science and Engineering, University of California, Los Angeles, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
Interests: data science; image processing; inverse problems; optimization; computational methods
Dr. Jing Qin
E-Mail Website
Guest Editor
Department of Mathematics, University of Kentucky, Lexington, KY 40506, USA
Interests: mathematical image processing; compressive sensing; inverse problems; optimization; high-dimensional signal processing

Special Issue Information

Dear Colleagues,

Remote-sensing images have been playing an important role in many areas including geology, oceanography, and weather forecasting. However, due to the limitations of imaging sensors, acquired images usually have limited spatial, spectral, and temporal resolutions. In addition, remote-sensing images often suffer from various types of degradations, such as noise, spatial distortion, and temporal blur. Reconstruction of a high-resolution image from a single image or a sequence of degraded, low-resolution images of the same scene, acquired from different views or at different conditions, is a challenging problem. Diverse novel and effective super-resolution approaches are being pursued in various remote-sensing applications. This Special Issue of Remote Sensing, which is focused on image super-resolution in remote sensing, aims to collect some of the most recent and promising super-resolution reconstruction techniques for remote-sensing images. It will consist of papers that showcase the latest research advances in the field of remote sensing. Authors are encouraged to submit high-quality, original research papers on remote-sensing image super-resolution. Topics of interest include, but not limited to, the following:

  • Spatial super-resolution
  • Temporal resolution enhancement
  • Spatio-temporal super-resolution
  • Spectral super-resolution
  • Single-frame and multi-frame resolution enhancement
  • Super-resolution from geometrically deformed remote-sensing images
  • Pansharpening of remote-sensing images
  • Fusion of multi-instrument data for enhancing its resolution

Dr. Igor Yanovsky
Dr. Jing Qin
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 papers will be 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 1800 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

  • super-resolution
  • resolution enhancement
  • spatial resolution
  • temporal resolution
  • deconvolution
  • deblurring
  • remote sensing
  • satellite imagery

Published Papers (3 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Open AccessArticle
Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution
Remote Sens. 2019, 11(20), 2333; https://doi.org/10.3390/rs11202333 - 09 Oct 2019
Abstract
Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively [...] Read more.
Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively shallow models, enormous parameters bring the risk of overfitting. In addition, due to the different scale of objects in images, the hierarchical features of deep CNN contain additional information for SR tasks, while most CNN models have not fully utilized these features. In this paper, we proposed a deep yet concise network to address these problems. Our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional LSTM (BiConvLSTM) layer is introduced to learn the correlations of features from each recursion and adaptively select the complementary information for the reconstruction layer. Experiments on multispectral satellite images, panchromatic satellite images, and nature high-resolution remote-sensing images showed that our proposed model outperformed state-of-the-art methods while utilizing fewer parameters, and ablation studies demonstrated the effectiveness of a BiConvLSTM layer for an image SR task. Full article
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Single Space Object Image Denoising and Super-Resolution Reconstructing Using Deep Convolutional Networks
Remote Sens. 2019, 11(16), 1910; https://doi.org/10.3390/rs11161910 - 15 Aug 2019
Abstract
Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors [...] Read more.
Space object recognition is the basis of space attack and defense confrontation. High-quality space object images are very important for space object recognition. Because of the large number of cosmic rays in the space environment and the inadequacy of optical lenses and detectors on satellites to support high-resolution imaging, most of the images obtained are blurred and contain a lot of cosmic-ray noise. So, denoising methods and super-resolution methods are two effective ways to reconstruct high-quality space object images. However, most super-resolution methods could only reconstruct the lost details of low spatial resolution images, but could not remove noise. On the other hand, most denoising methods especially cosmic-ray denoising methods could not reconstruct high-resolution details. So in this paper, a deep convolutional neural network (CNN)-based single space object image denoising and super-resolution reconstruction method is presented. The noise is removed and the lost details of the low spatial resolution image are well reconstructed based on one very deep CNN-based network, which combines global residual learning and local residual learning. Based on a dataset of satellite images, experimental results demonstrate the feasibility of our proposed method in enhancing the spatial resolution and removing the noise of the space objects images. Full article
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
Show Figures

Graphical abstract

Open AccessArticle
Deep Residual Squeeze and Excitation Network for Remote Sensing Image Super-Resolution
Remote Sens. 2019, 11(15), 1817; https://doi.org/10.3390/rs11151817 - 03 Aug 2019
Abstract
Recently, deep convolutional neural networks (DCNN) have obtained promising results in single image super-resolution (SISR) of remote sensing images. Due to the high complexity of remote sensing image distribution, most of the existing methods are not good enough for remote sensing image super-resolution. [...] Read more.
Recently, deep convolutional neural networks (DCNN) have obtained promising results in single image super-resolution (SISR) of remote sensing images. Due to the high complexity of remote sensing image distribution, most of the existing methods are not good enough for remote sensing image super-resolution. Enhancing the representation ability of the network is one of the critical factors to improve remote sensing image super-resolution performance. To address this problem, we propose a new SISR algorithm called a Deep Residual Squeeze and Excitation Network (DRSEN). Specifically, we propose a residual squeeze and excitation block (RSEB) as a building block in DRSEN. The RSEB fuses the input and its internal features of current block, and models the interdependencies and relationships between channels to enhance the representation power. At the same time, we improve the up-sampling module and the global residual pathway in the network to reduce the parameters of the network. Experiments on two public remote sensing datasets (UC Merced and NWPU-RESISC45) show that our DRSEN achieves better accuracy and visual improvements against most state-of-the-art methods. The DRSEN is beneficial for the progress in the remote sensing images super-resolution field. Full article
(This article belongs to the Special Issue Image Super-Resolution in Remote Sensing)
Show Figures

Figure 1

Back to TopTop