Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
7.3
3.4
Journals
Sensors
Special Issues
Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 1080

Special Issue Editor

Prof. Sheng Chang

E-Mail Website
Guest Editor
Department of Space Microwave Remote Sensing System, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China
Interests: spaceborne synthetic aperture radar; multi-channel in elevation; multi-channel in azimuth; space-time waveform encoding; high-resolution wide-swath; range ambiguity; azimuth ambiguity; echo separation

Special Issue Information

Dear Colleagues,

Synthetic Aperture Radar (SAR) technology has been under continuous development for over seventy years, during which numerous innovative SAR system architectures have emerged. These advancements aim to overcome the limitations of traditional spaceborne SAR systems, catering to ever-growing application demands. Recent developments in new SAR architectures, methodologies, and applications have demonstrated significant progress in enhancing imaging quality, particularly in achieving high-resolution and wide-swath imaging capabilities. These capabilities are vital for the next generation of spaceborne SAR systems, providing substantial contributions to a broad range of real-world applications, such as environmental monitoring, disaster management, agricultural assessment, and urban planning. This Special Issue focuses on the latest advancements in SAR remote sensing, covering novel system designs, innovative signal processing techniques, and emerging applications pushing current SAR technologies' boundaries.

Prof. Sheng Chang
Guest Editor

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. Sensors 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 2600 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

  • spaceborne SAR
  • high-resolution imaging
  • wide-swath imaging
  • SAR signal processing
  • advanced SAR methodologies
  • environmental monitoring

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.

Published Papers (1 paper)

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

Research

22 pages, 7881 KiB  
Article
Bidirectional Mamba with Dual-Branch Feature Extraction for Hyperspectral Image Classification
by Ming Sun, Jie Zhang, Xiaoou He and Yihe Zhong
Sensors 2024, 24(21), 6899; https://doi.org/10.3390/s24216899 - 28 Oct 2024
Viewed by 833
Abstract
The hyperspectral image (HSI) classification task is widely used in remote sensing image analysis. The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. However, they cannot well utilize the sequential properties of spectral features and face [...] Read more.
The hyperspectral image (HSI) classification task is widely used in remote sensing image analysis. The HSI classification methods based on convolutional neural networks (CNNs) have greatly improved the classification performance. However, they cannot well utilize the sequential properties of spectral features and face the challenge of increasing computational cost with the increase in network depth. To address these shortcomings, this paper proposes a novel network with a CNN-Mamba architecture, called DBMamba, which uses a bidirectional Mamba to process spectral feature sequences at a linear computational cost. In the DBMamba, principal component analysis (PCA) is first used to extract the main features of the data. Then, a dual-branch CNN structure, with the fused features from spectral–spatial features by 3D-CNN and spatial features by 2D-CNN, is used to extract shallow spectral–spatial features. Finally, a bidirectional Mamba is used to effectively capture global contextual information in features and significantly enhance the extraction of spectral features. Experimental results on the Indian Pines, Salinas, and Pavia University datasets demonstrate that the classification performance surpasses that of many cutting-edge methods, improving by 1.04%, 0.15%, and 0.09%, respectively, over the competing SSFTT method. The research in this paper enhances the existing knowledge on HSI classification and provides valuable insights for future research in this field. Full article
(This article belongs to the Special Issue Recent Advances in Synthetic Aperture Radar (SAR) Remote Sensing)
►▼ Show Figures

Figure 1

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