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Advanced Methods for Motion Estimation in Remote Sensing

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 (31 May 2024) | Viewed by 1628

Special Issue Editors


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Guest Editor
1. Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91109, USA
2. Department of Mathematics, University of California, Los Angeles, CA 90095, USA
Interests: data science; remote sensing; image processing; inverse problems; optimization; computational methods
Special Issues, Collections and Topics in MDPI journals
Department of Mathematics, University of Kentucky, Lexington, KY 40506, USA
Interests: mathematical image processing; compressive sensing; inverse problems; optimization; high-dimensional signal processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The science of remote sensing is rapidly advancing, leveraging increasingly sophisticated computational methods to understand and predict various dynamical phenomena on our planet. A domain at the forefront of these advancements is the use of image alignment, optical flow, and image registration techniques for estimating motion in a variety of atmospheric and oceanic settings. This Special Issue, entitled “Advanced Methods for Motion Estimation in Remote Sensing”, aims to highlight the latest developments, applications, and challenges in this area, aligned with the journal’s scope in publishing innovative research in remote sensing methodologies.

Motion estimation plays a pivotal role in understanding various atmospheric and terrestrial dynamics. Traditional methods have limitations in terms of accuracy and granularity. However, the advent of advanced image alignment, optical flow, and image registration methods presents an opportunity to obtain more detailed and precise motion estimations.

This Special Issue aims to serve as a platform for researchers to share their findings, innovations, and insights in the application of advanced methods for motion estimation in remote sensing. We are particularly interested in manuscripts that showcase the use of these methods in real-world scenarios, bridging the gap between theoretical developments and practical applications.

Articles may address, but are not limited to, the following topics:

  • The retrieval of atmospheric motion vectors;
  • Oceanic current dynamics and estimation;
  • Cloud motion patterns and their implications;
  • Dynamics of glacial movements;
  • Technical advancements and computational challenges in image registration, optical flow, and image alignment techniques;
  • Comparative studies highlighting the benefits and limitations of different motion estimation methods

Dr. Igor Yanovsky
Dr. Jing Qin
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

  • motion estimation
  • optical flow
  • image alignment
  • remote sensing methodologies
  • atmospheric motion vectors
  • oceanic current dynamics
  • image registration techniques
  • cloud motion analysis
  • glacial movements
  • computational challenges in remote sensing

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Published Papers (1 paper)

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13 pages, 1052 KiB  
Technical Note
Geolocalization from Aerial Sensing Images Using Road Network Alignment
by Yongfei Li, Dongfang Yang, Shicheng Wang, Lin Shi and Deyu Meng
Remote Sens. 2024, 16(3), 482; https://doi.org/10.3390/rs16030482 - 26 Jan 2024
Viewed by 1283
Abstract
Estimating the geographic positions in GPS-denied environments is of great significance to the safe flight of unmanned aerial vehicles (UAVs). In this paper, we propose a novel geographic position estimation method for UAVs after road network alignment. We discuss the generally overlooked issue, [...] Read more.
Estimating the geographic positions in GPS-denied environments is of great significance to the safe flight of unmanned aerial vehicles (UAVs). In this paper, we propose a novel geographic position estimation method for UAVs after road network alignment. We discuss the generally overlooked issue, namely, how to estimate the geographic position of the UAV after successful road network alignment, and propose a precise robust solution. In our method, the optimal initial solution of the geographic position of the UAV is first estimated from the road network alignment result, which is typically presented as a homography transformation between the observed road map and the reference one. The geographic position estimation is then modeled as an optimization problem to align the observed road with the reference one to improve the estimation accuracy further. Experiments on synthetic and real flight aerial image datasets show that the proposed algorithm can estimate more accurate geographic position of the UAV in real time and is robust to the errors from homography transformation estimation compared to the currently commonly-used method. Full article
(This article belongs to the Special Issue Advanced Methods for Motion Estimation in Remote Sensing)
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