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Remote Sensing
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Space-Photogrammetry for High-Precision Measurement by Multi-Sensor Data Fusion
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Space-Photogrammetry for High-Precision Measurement by Multi-Sensor Data Fusion

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

Deadline for manuscript submissions: 28 February 2025 | Viewed by 1092

Special Issue Editors

Prof. Dr. Rujin Zhao

E-Mail Website
Guest Editor
University of Chinese Academy of Sciences, Beijing, China
Interests: remote sensing image processing on satellite; space target detection; recognition and measurement
Prof. Dr. Zhenming Peng

E-Mail Website
Guest Editor
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731 China
Interests: remote sensing image processing; object detection and tracking; scene understanding
Special Issues, Collections and Topics in MDPI journals
Dr. Xiong Xu

E-Mail Website
Guest Editor
College of Surveying and Geoinformatics, Tongji University, Shanghai 200000, China
Interests: multi-source data fusion; optical image processing; target detection; 3D reconstruction; deep learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Existing spaceflight requirements are no longer limited to detecting and identifying targets but also precise measurement of target information. Space measurement equipment such as optical cameras, LIDAR, inertial guidance systems, and other new sensors provide multi-modal information for space targets with their unique capabilities and comprehensively support high-precision measurements of multi-dimensional information such as pose, morphology, and spectrum of space targets.

This Special Issue explores the challenges and opportunities associated with detection, identification, and measurement technologies for space targets and extraterrestrial objects (such as Lunar), with an emphasis on the development of intelligent measurement systems and algorithms to improve the existing detection capabilities and measurement accuracy and on the realization of a variety of applications, including asteroid measurement and capture, lunar exploration, and resource development, through the complementary advantages of multimodal detection and the fusion of multi-source information by means of multi-sensors.

The aim of this Special Issue is to explore the potential of precision measurement technology in space target measurement and lunar exploration through the discussion of the latest progress and future research topics

This Special Issue aims to showcase the latest advances and innovations in measurement-oriented high-precision space targeting and lunar exploration. We invite high-quality research that spans theoretical foundations, enabling technologies, and impactful applications in this exciting field. Topics of interest include, but are not limited to, the following:

  1. Space target detection and recognition;
  2. High-precision space optoelectronic measurements;
  3. Lunar remote sensing;
  4. High-precision Moon surface measurement
  5. Multi-source Information fusion for space targets;
  6. Spatial spectral measurement and analysis;
  7. New Sensors for space target measurement.

We welcome research articles and review articles for submission. Submissions will undergo a rigorous peer-review process to ensure they are of the highest quality.

Prof. Dr. Rujin Zhao
Prof. Dr. Zhenming Peng
Dr. Xiong Xu
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

  • space target
  • high-precision measurement
  • remote sensing
  • information fusion
  • space photogrammetry
  • spectral measurement

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

Published Papers (2 papers)

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Research

24 pages, 2066 KiB  
Article
A Self-Supervised Feature Point Detection Method for ISAR Images of Space Targets
by Shengteng Jiang, Xiaoyuan Ren, Canyu Wang, Libing Jiang and Zhuang Wang
Remote Sens. 2025, 17(3), 441; https://doi.org/10.3390/rs17030441 - 28 Jan 2025
Viewed by 253
Abstract
Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and [...] Read more.
Feature point detection in inverse synthetic aperture radar (ISAR) images of space targets is the foundation for tasks such as analyzing space target motion intent and predicting on-orbit status. Traditional feature point detection methods perform poorly when confronted with the low texture and uneven brightness characteristics of ISAR images. Due to the nonlinear mapping capabilities, neural networks can effectively learn features from ISAR images of space targets, providing new ideas for feature point detection. However, the scarcity of labeled ISAR image data for space targets presents a challenge for research. To address the issue, this paper introduces a self-supervised feature point detection method (SFPD), which can accurately detect the positions of feature points in ISAR images of space targets without true feature point positions during the training process. Firstly, this paper simulates an ISAR primitive dataset and uses it to train the proposed basic feature point detection model. Subsequently, the basic feature point detection model and affine transformation are utilized to label pseudo-ground truth for ISAR images of space targets. Eventually, the labeled ISAR image dataset is used to train SFPD. Therefore, SFPD can be trained without requiring ground truth for the ISAR image dataset. The experiments demonstrate that SFPD has better performance in feature point detection and feature point matching than usual algorithms. Full article
(This article belongs to the Special Issue Space-Photogrammetry for High-Precision Measurement by Multi-Sensor Data Fusion)
22 pages, 16287 KiB  
Article
SFDA-MEF: An Unsupervised Spacecraft Feature Deformable Alignment Network for Multi-Exposure Image Fusion
by Qianwen Xiong, Xiaoyuan Ren, Huanyu Yin, Libing Jiang, Canyu Wang and Zhuang Wang
Remote Sens. 2025, 17(2), 199; https://doi.org/10.3390/rs17020199 - 8 Jan 2025
Viewed by 512
Abstract
Optical image sequences of spacecraft acquired by space-based monocular cameras are typically imaged through exposure bracketing. The spacecraft feature deformable alignment network for multi-exposure image fusion (SFDA-MEF) aims to synthesize a High Dynamic Range (HDR) spacecraft image from a set of Low Dynamic [...] Read more.
Optical image sequences of spacecraft acquired by space-based monocular cameras are typically imaged through exposure bracketing. The spacecraft feature deformable alignment network for multi-exposure image fusion (SFDA-MEF) aims to synthesize a High Dynamic Range (HDR) spacecraft image from a set of Low Dynamic Range (LDR) images with varying exposures. The HDR image contains details of the observed target in LDR images captured within a specific luminance range. The relative attitude of the spacecraft in the camera coordinate system undergoes continuous changes during the orbital rendezvous, which leads to a large proportion of moving pixels between adjacent frames. Concurrently, subsequent tasks of the In-Orbit Servicing (IOS) system, such as attitude estimation, are highly sensitive to variations in multi-view geometric relationships, which means that the fusion result should preserve the shape of the spacecraft with minimal distortion. However, traditional methods and unsupervised deep-learning methods always exhibit inherent limitations in dealing with complex overlapping regions. In addition, supervised methods are not suitable when ground truth data are scarce. Therefore, we propose an unsupervised learning framework for the multi-exposure fusion of optical spacecraft image sequences. We introduce a deformable convolution in the feature deformable alignment module and construct an alignment loss function to preserve its shape with minimal distortion. We also design a feature point extraction loss function to render our output more conducive to subsequent IOS tasks. Finally, we present a multi-exposure spacecraft image dataset. Subjective and objective experimental results validate the effectiveness of SFDA-MEF, especially in retaining the shape of the spacecraft. Full article
(This article belongs to the Special Issue Space-Photogrammetry for High-Precision Measurement by Multi-Sensor Data Fusion)
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