Special Issue "Advanced Theory, Methods, Technique and Applications for Remote Sensing Big Data"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: 1 July 2022.

Special Issue Editors

Prof. Dr. Weipeng Jing
E-Mail Website
Guest Editor
The College of Information and Computer Engineering, Northeast Forestry University, Harbin, China
Interests: remote sensing; image analysis; pattern recognition; cloud computing; computer vision.
Prof. Dr. Wei Wei
E-Mail Website
Guest Editor
Department of Computer Science, Xi'an University of Technology, Xi'an, China
Interests: wireless networks; wireless sensor networks application; image processing; mobile computing; distributed computing; pervasive computing; Internet of Things; and sensor data clouds
Dr. Rafal Scherer
E-Mail Website
Guest Editor
Institute of Computational Intelligence, Częstochowa University Of Technology, Czestochowa, Poland
Interests: machine learning; neural networks; computer vision; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Big data is a very important topic in many research areas. Remote sensing, as one of the sources for big data, is generating earth-observation data and analysis results daily from the platforms of satellites, manned/unmanned aircrafts, and ground-based structures. These different kinds of remote-sensing data include SAR, multi-spectral optical data and hyper-spectral optical data. Those data sets comprise different spectral bandwidths (dimensionality), spatial resolutions, and radiometric resolutions. The increasing growth of remote sensing data and geoscience research pushes earth sciences strongly and poses great challenges to data science for remote sensing big data, including collection, storage, management, analysis and interpretation. This Special Issue is expected to bring together experts from different research areas to discover and realize the values of big data in various remote sensing areas.

This Special Issue is intended to present the current state-of-the-art theoretical, methodological, technique and application research on remote sensing big data. The aim of this Special Issue is to share the experiences in processing remote sensing images with large volumes and variant modes, and intelligent interpretation with advance algorithms. We expect that these research results will provide a necessary effort towards the incorporation of this technology into the remote sensing field and also help academia, governments, and industries to gain insights into the potential of using big data techniques and concepts in remote-sensing applications.

Prof. Dr. Robertas Damaševičius
Prof. Dr. Weipeng Jing
Prof. Dr. Wei Wei
Prof. Dr. Marcin Woźniak
Dr. Rafal Scherer
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 2500 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
  • big data
  • SAR
  • scene classification
  • target detection

Published Papers (2 papers)

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Research

Article
An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
Remote Sens. 2021, 13(18), 3636; https://doi.org/10.3390/rs13183636 - 11 Sep 2021
Cited by 1 | Viewed by 629
Abstract
As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. [...] Read more.
As one of the main sources of remote sensing big data, synthetic aperture radar (SAR) can provide all-day and all-weather Earth image acquisition. However, speckle noise in SAR images brings a notable limitation for its big data applications, including image analysis and interpretation. Deep learning has been demonstrated as an advanced method and technology for SAR image despeckling. Most existing deep-learning-based methods adopt supervised learning and use synthetic speckled images to train the despeckling networks. This is because they need clean images as the references, and it is hard to obtain purely clean SAR images in real-world conditions. However, significant differences between synthetic speckled and real SAR images cause the domain gap problem. In other words, they cannot show superior performance for despeckling real SAR images as they do for synthetic speckled images. Inspired by recent studies on self-supervised denoising, we propose an advanced SAR image despeckling method by virtue of Bernoulli-sampling-based self-supervised deep learning, called SSD-SAR-BS. By only using real speckled SAR images, Bernoulli-sampled speckled image pairs (input–target) were obtained as the training data. Then, a multiscale despeckling network was trained on these image pairs. In addition, a dropout-based ensemble was introduced to boost the network performance. Extensive experimental results demonstrated that our proposed method outperforms the state-of-the-art for speckle noise suppression on both synthetic speckled and real SAR datasets (i.e., Sentinel-1 and TerraSAR-X). Full article
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Article
PolSAR Image Land Cover Classification Based on Hierarchical Capsule Network
Remote Sens. 2021, 13(16), 3132; https://doi.org/10.3390/rs13163132 - 07 Aug 2021
Cited by 1 | Viewed by 616
Abstract
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classification due to their powerful feature learning capabilities. However, a [...] Read more.
Polarimetric synthetic aperture radar (PolSAR) image classification is one of the basic methods of PolSAR image interpretation. Deep learning algorithms, especially convolutional neural networks (CNNs), have been widely used in PolSAR image classification due to their powerful feature learning capabilities. However, a single neuron in the CNN cannot represent multiple polarimetric attributes of the land cover. The capsule network (CapsNet) uses vectors instead of the single neuron to characterize the polarimetric attributes, which improves the classification performance compared with traditional CNNs. In this paper, a hierarchical capsule network (HCapsNet) is proposed for the land cover classification of PolSAR images, which can consider the deep features obtained at different network levels in the classification. Moreover, we adopt three attributes to uniformly describe the scattering mechanisms of different land covers: phase, amplitude, and polarimetric decomposition parameters, which improves the generalization performance of HCapsNet. Furthermore, conditional random field (CRF) is added to the classification framework to eliminate small isolated regions of the intra-class. Comprehensive evaluations are performed on three PolSAR datasets acquired by different sensors, which demonstrate that our proposed method outperforms other state-of-the-art methods. Full article
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