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Remote Sensing
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Signal Processing Theory and Methods in Remote Sensing (Second Edition)
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Signal Processing Theory and Methods in Remote Sensing (Second Edition)

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

Deadline for manuscript submissions: 30 September 2024 | Viewed by 885

Special Issue Editors

Dr. Shaohui Mei

E-Mail Website
Guest Editor
School of Electronics and Information, Northwestern Polytechnical University, Xi'an 710129, China
Interests: remote sensing image processing; pattern recognition and machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Junhui Hou

E-Mail Website
Guest Editor
Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong 999077, China
Interests: image/video representations and analysis; semi-supervised/unsupervised data modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Kun Hu

E-Mail Website
Guest Editor
School of Computer Science, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: multimedia computing; remote sensing applications; graph learning
Dr. Mingyang Ma

E-Mail
Guest Editor
School of Information and Communications Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Interests: remote sensing image processing; video understanding
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of signal processing is closely intertwined with the advancement of remote sensing. The continual evolution of signal processing techniques provides essential tools for the processing and interpretation of remote sensing data, while the development of remote sensing technology offers rich data sources and practical application scenarios for signal processing. With the progress of remote sensing technology, the volume and variety of data acquired have steadily increased. The development of signal processing techniques provides robust tools and methods for handling these diverse data, including image processing, time-series analysis, feature extraction, pattern recognition, and more. Thus, the development of signal processing and remote sensing mutually reinforce each other, collectively driving the widespread application of remote sensing technology in fields such as earth science, environmental monitoring, and resource management.

This Special Issue aims to explore the latest advancements and innovative applications of signal processing theory and methods in remote sensing. We invite contributions focusing on innovative signal processing techniques for the enhancement, analysis, and interpretation of remote sensing data across different domains. Topics of interest include, but are not limited to, the following:

  • Feature extraction for remote sensing;
  • Time-series analysis for remote sensing observations;
  • Fusion techniques for multi-source remote sensing data;
  • Real-time signal processing for remote sensing;
  • Large-scale image processing for remote sensing;
  • Compressed sensing for remote sensing;
  • Deep learning approaches for remote sensing;
  • Signal processing platforms for remote sensing;
  • Signal processing in remote sensing applications.

Dr. Shaohui Mei
Dr. Junhui Hou
Dr. Kun Hu
Dr. Mingyang Ma
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

  • signal processing
  • remote sensing
  • image processing
  • deep learning
  • information fusion
  • time-series analysis

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Published Papers (1 paper)

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Research

21 pages, 4506 KiB  
Article
SREDet: Semantic-Driven Rotational Feature Enhancement for Oriented Object Detection in Remote Sensing Images
by Zehao Zhang, Chenhan Wang, Huayu Zhang, Dacheng Qi, Qingyi Liu, Yufeng Wang and Wenrui Ding
Remote Sens. 2024, 16(13), 2317; https://doi.org/10.3390/rs16132317 - 25 Jun 2024
Viewed by 638
Abstract
Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature [...] Read more.
Significant progress has been achieved in the field of oriented object detection (OOD) in recent years. Compared to natural images, objects in remote sensing images exhibit characteristics of dense arrangement and arbitrary orientation while also containing a large amount of background information. Feature extraction in OOD becomes more challenging due to the diversity of object orientations. In this paper, we propose a semantic-driven rotational feature enhancement method, termed SREDet, to fully leverage the joint semantic and spatial information of oriented objects in the remote sensing images. We first construct a multi-rotation feature pyramid network (MRFPN), which leverages a fusion of multi-angle and multiscale feature maps to enhance the capability to extract features from different orientations. Then, considering feature confusion and contamination caused by the dense arrangement of objects and background interference, we present a semantic-driven feature enhancement module (SFEM), which decouples features in the spatial domain to separately enhance the features of objects and weaken those of backgrounds. Furthermore, we introduce an error source evaluation metric for rotated object detection to further analyze detection errors and indicate the effectiveness of our method. Extensive experiments demonstrate that our SREDet method achieves superior performance on two commonly used remote sensing object detection datasets (i.e., DOTA and HRSC2016). Full article
(This article belongs to the Special Issue Signal Processing Theory and Methods in Remote Sensing (Second Edition))
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