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Remote Sensing
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SAR in Big Data Era III
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SAR in Big Data Era III

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 (30 April 2024) | Viewed by 1627

Special Issue Editors

Prof. Dr. Chao Wang

E-Mail Website
Guest Editor
Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
Interests: SAR image understanding; PolSAR and InSAR applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Alberto Moreira

E-Mail Website
Guest Editor
DLR, Microwaves and Radar Institute, Muenchener Str. 20, D-82234 Wessling, Germany
Interests: SAR technology; SAR missions; SAR applications
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mihai P. Datcu

E-Mail Website
Guest Editor
Deutsches Zentrum für Luft- und Raumfahrt (DLR), Koln, Germany
Interests: earth obvervation; sentinel-2
Dr. Sirui Tian

E-Mail Website
Guest Editor
Department of Electronic Engineering, Nanjing University of Science and Technology, Nanjing, China
Interests: artificial intelligence; machine learning; image processing
Dr. Yixian Tang

E-Mail Website
Guest Editor
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: microwave remote sensing; synthetic aperture radar remote sensing; synthetic aperture radar interferometry and their applications

Special Issue Information

Dear Colleagues,

Nearly a hundred synthetic aperture radar (SAR) satellites fly around the world, providing earth observation data regardless of the weather or solar illumination conditions. SAR in the Big Data era introduces opportunities to tackle the challenges of food supply, disaster mitigation, global change, social and economic prosperity, and the fulfillment of sustainable development goals (SDGs) proposed by the UN. However, applying a huge volume of SAR data from various missions with variant observing configurations to efficiently achieve our goals is our primary concern.     

This Special Issue aims to collate studies covering the processing techniques of large-scale and time-series SAR data and various SDG applications and assessments. We would like to invite you to contribute articles about your recent research, experimental work, as well as reviews related to these issues. Contributions may include, but are not limited, to the following topics:

  • SAR/InSAR cloud/edge computing;
  • SAR/InSAR AI models;
  • SAR/InSAR time-series processing;
  • PolSAR processing and land use/land cover applications;
  • InSAR processing and geohazard monitoring;
  • SAR for sustainable development goals.

Prof. Dr. Chao Wang
Prof. Dr. Alberto Moreira
Prof. Dr. Mihai P. Datcu
Dr. Sirui Tian
Dr. Yixian Tang
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

  • SAR/InSAR
  • big data
  • sustainable development goals (SDGs)
  • high performance computing
  • artificial intelligence
  • PolSAR processing
  • TomoSAR processing
  • SAR applications
  • geohazards
  • global change

Related Special Issues

Published Papers (2 papers)

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Research

20 pages, 11220 KiB  
Article
Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China
by Xin Tian, Jiejie Li, Fanyi Zhang, Haibo Zhang and Mi Jiang
Remote Sens. 2024, 16(6), 1074; https://doi.org/10.3390/rs16061074 - 19 Mar 2024
Viewed by 808
Abstract
The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different [...] Read more.
The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha. Full article
(This article belongs to the Special Issue SAR in Big Data Era III)
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18 pages, 16894 KiB  
Article
Robust Two-Dimensional InSAR Phase Unwrapping via FPA and GAU Dual Attention in ResDANet
by Xiaomao Chen, Shanshan Zhang, Xiaofeng Qin and Jinfeng Lin
Remote Sens. 2024, 16(6), 1058; https://doi.org/10.3390/rs16061058 - 16 Mar 2024
Viewed by 480
Abstract
Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address [...] Read more.
Two-dimensional phase unwrapping (2-D PU) is vital for reconstructing Earth’s surface topography and displacement from interferometric synthetic aperture radar (InSAR) data. Conventional algorithms rely on the postulate, but this assumption is often insufficient due to abrupt topographic changes and severe noise. To address this challenge, our research proposes a novel approach utilizing deep convolutional neural networks inspired by the U-Net architecture to estimate phase gradient information. Our approach involves downsampling the input data to extract crucial features, followed by upsampling to restore spatial resolution. We incorporate two attention mechanisms—feature pyramid attention (FPA) and global attention upsample (GAU)—and a residual structure in the network’s structure. Thus, we construct ResDANet (residual and dual attention net). We rigorously train ResDANet utilizing simulated datasets and employ an L1-norm objective function to minimize the disparity between unwrapped phase gradients and those calculated by ResDANet, yielding the final 2-D PU results. The network is rigorously trained using two distinct training strategies and encompassing three types of simulated datasets. ResDANet exhibits excellent robust performance and efficiency on simulated data and real data, such as China’s Three Gorges and an Italian volcano. Full article
(This article belongs to the Special Issue SAR in Big Data Era III)
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