Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.3
4.2
Journals
Remote Sensing
Special Issues
SAR in Big Data Era III
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 31 October 2024 | Viewed by 6029

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Related Special Issues

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 1677 KiB  
Article
CPINet: Towards A Novel Cross-Polarimetric Interaction Network for Dual-Polarized SAR Ship Classification
by Jinglu He, Ruiting Sun, Yingying Kong, Wenlong Chang, Chenglu Sun, Gaige Chen, Yinghua Li, Zhe Meng and Fuping Wang
Remote Sens. 2024, 16(18), 3479; https://doi.org/10.3390/rs16183479 - 19 Sep 2024
Viewed by 615
Abstract
With the rapid development of the modern world, it is imperative to achieve effective and efficient monitoring for territories of interest, especially for the broad ocean area. For surveillance of ship targets at sea, a common and powerful approach is to take advantage [...] Read more.
With the rapid development of the modern world, it is imperative to achieve effective and efficient monitoring for territories of interest, especially for the broad ocean area. For surveillance of ship targets at sea, a common and powerful approach is to take advantage of satellite synthetic aperture radar (SAR) systems. Currently, using satellite SAR images for ship classification is a challenging issue due to complex sea situations and the imaging variances of ships. Fortunately, the emergence of advanced satellite SAR sensors has shed much light on the SAR ship automatic target recognition (ATR) task, e.g., utilizing dual-polarization (dual-pol) information to boost the performance of SAR ship classification. Therefore, in this paper we have developed a novel cross-polarimetric interaction network (CPINet) to explore the abundant polarization information of dual-pol SAR images with the help of deep learning strategies, leading to an effective solution for high-performance ship classification. First, we establish a novel multiscale deep feature extraction framework to fully mine the characteristics of dual-pol SAR images in a coarse-to-fine manner. Second, to further leverage the complementary information of dual-pol SAR images, we propose a mixed-order squeeze–excitation (MO-SE) attention mechanism, in which the first- and second-order statistics of the deep features from one single-polarized SAR image are extracted to guide the learning of another polarized one. Then, the intermediate multiscale fused and MO-SE augmented dual-polarized deep feature maps are respectively aggregated by the factorized bilinear coding (FBC) pooling method. Meanwhile, the last multiscale fused deep feature maps for each single-polarized SAR image are also individually aggregated by the FBC. Finally, four kinds of highly discriminative deep representations are obtained for loss computation and category prediction. For better network training, the gradient normalization (GradNorm) method for multitask networks is extended to adaptively balance the contribution of each loss component. Extensive experiments on the three- and five-category dual-pol SAR ship classification dataset collected from the open and free OpenSARShip database demonstrate the superiority and robustness of CPINet compared with state-of-the-art methods for the dual-polarized SAR ship classification task. Full article
(This article belongs to the Special Issue SAR in Big Data Era III)
►▼ Show Figures

Figure 1

20 pages, 7378 KiB  
Article
A Lightweight Pyramid Transformer for High-Resolution SAR Image-Based Building Classification in Port Regions
by Bo Zhang, Qian Wu, Fan Wu, Jiajia Huang and Chao Wang
Remote Sens. 2024, 16(17), 3218; https://doi.org/10.3390/rs16173218 - 30 Aug 2024
Viewed by 458
Abstract
Automatic classification of buildings within port areas from synthetic aperture radar (SAR) images is crucial for effective port monitoring and planning. Yet, the unique challenges of SAR imaging, such as side-looking geometry, multi-bouncing scattering, and the compact arrangement of structures, often lead to [...] Read more.
Automatic classification of buildings within port areas from synthetic aperture radar (SAR) images is crucial for effective port monitoring and planning. Yet, the unique challenges of SAR imaging, such as side-looking geometry, multi-bouncing scattering, and the compact arrangement of structures, often lead to incomplete building structures and blurred boundaries in classification results. To address these issues, this paper introduces SPformer, an efficient and lightweight pyramid transformer model tailored for semantic segmentation. The SPformer utilizes a pyramid transformer encoder with spatially separable self-attention (SSSA) to refine both local and global spatial information and to process multi-scale features, enhancing the accuracy of building structure delineation. It also integrates a lightweight all multi-layer perceptron (ALL-MLP) decoder to consolidate multi-scale information across various depths and attention scopes, refining detail processing. Experimental results on the Gaofen-3 (GF-3) 1 m port building classification dataset demonstrate the effectiveness of SPformer, achieving competitive performance compared to state-of-the-art models, with mean intersection over union (mIoU) and mean F1-score (mF1) reaching 77.14% and 87.04%, respectively, while maintaining a compact model size and lower computational requirements. Experiments conducted on the entire scene of SAR images covering port area also show the good capabilities of the proposed method. Full article
(This article belongs to the Special Issue SAR in Big Data Era III)
►▼ Show Figures

Figure 1

23 pages, 47555 KiB  
Article
Refined InSAR Mapping Based on Improved Tropospheric Delay Correction Method for Automatic Identification of Wide-Area Potential Landslides
by Lu Li, Jili Wang, Heng Zhang, Yi Zhang, Wei Xiang and Yuanzhao Fu
Remote Sens. 2024, 16(12), 2187; https://doi.org/10.3390/rs16122187 - 16 Jun 2024
Viewed by 757
Abstract
Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based [...] Read more.
Slow-moving landslides often occur in areas of high relief, which are significantly affected by tropospheric delay. In general, tropospheric delay correction methods in the synthetic-aperture radar interferometry (InSAR) field can be broadly divided into those based on external auxiliary information and those based on traditional empirical models. External auxiliary information is hindered by the low spatial–temporal resolution. Traditional empirical models can be adaptable for the spatial heterogeneity of tropospheric delay, but are limited by preset window sizes and models. In this regard, this paper proposes an improved tropospheric delay correction method based on the multivariable move-window variation model (MMVM) to adaptively determine the window size and the empirical model. Considering topography and surface deformation, the MMVM uses multivariate variogram models with iterative weight to determine the window size and model, and uses the Levenberg–Marquardt (LM) algorithm to enhance convergence speed and robustness. The high-precision surface deformation is then derived. Combined with hotspot analysis (HSA), wide-area potential landslides can be automatically identified. The reservoir area of the Baihetan hydropower station in the lower reaches of the Jinsha River was selected as the study area, using 118 Sentinel-1A images to compare with four methods in three aspects: corrected interferograms, derived deformation rate, and stability of time-series deformation. In terms of mean standard deviation, the MMVM achieved the lowest value for the unwrapped phase in the non-deformed areas, representing a reduction of 56.4% compared to the original value. Finally, 32 landslides were identified, 16 of which posed a threat to nearby villages. The experimental results demonstrate the superiority of the proposed method and provide support to disaster investigation departments. Full article
(This article belongs to the Special Issue SAR in Big Data Era III)
►▼ Show Figures

Figure 1

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
Cited by 2 | Viewed by 2562
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)
►▼ Show Figures

Figure 1

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 904
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop