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Remote Sensing
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Polarimetric Remote Sensing
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Polarimetric Remote Sensing

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

Deadline for manuscript submissions: closed (31 January 2022) | Viewed by 3912

Special Issue Editors

Prof. Dr. Sang-Eun Park

E-Mail Website
Guest Editor
Department of Geoinformation Engineering, Sejong University, Seoul 143-747, Republic of Korea
Interests: polarimetric SAR classification; forward and inverse modeling of microwave vegetation and surface backscattering; multisource data integration for mapping natural disasters and monitoring environmental changes
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Toshifumi Moriyama

E-Mail Website
Guest Editor
Graduate School of Engineering, Nagasaki University, 1-14 Bunkyo-machi, Nagasaki 852-8521, Japan
Interests: radar polarimetry; inverse scattering; microwave remote-sensing; wireless sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The polarization state of electromagnetic waves plays an essential role in the interaction of radar signals with targets and/or propagating media. In radar remote sensing, characteristic information about the targets’ scattering mechanism can be obtained by implementing polarization control. In comparison to single-polarization observation, the inclusion of radar polarimetry consequently can lead to significant improvements in the retrieval of bio- and geophysical parameters, the classification of different scattering objects, and the scale and accuracy of various practical applications.

Due to the advantages of radar polarimetry, it has become common to include polarimetric observation functionalities in most recent and future advanced SAR systems. In particular, ALOS/PALSAR, the first spaceborne L-band polarimetric SAR system, provided invaluable polarimetric data and demonstrated the usability of polarimetry in various applications. With the increase in global polarimetric SAR observations, various advanced polarimetric data processing technologies have been developed over the past two decades.

The purpose of this Special Issue is to summarize the latest academic achievements in various technologies and applications related to polarimetric remote sensing. Moreover, we expect that this Special Issue can promote further research to deepen our knowledge on dynamic Earth. We would like to invite you to submit articles about your recent studies linked to this Special Issue “Polarimetric Remote Sensing”.

Topics of this special issue:

Measurement technologies: PolSAR, Pol-In-SAR, compact polarimetry, circularly polarized synthetic aperture radar (CP-SAR), polarimetric calibration, etc.

Analysis technologies: polarimetric decomposition, classification, change detection, etc.

Inversion technologies: biomass, soil moisture, topography, ionosphere, etc.

Applications: forest, ocean, disasters, cryosphere, etc.

Prof. Dr. Sang-Eun Park
Prof. Dr. Toshifumi Moriyama
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.

Published Papers (2 papers)

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Research

17 pages, 3511 KiB  
Article
Forest Height Retrieval Based on the Dual PolInSAR Images
by Tayebe Managhebi, Yasser Maghsoudi and Meisam Amani
Remote Sens. 2022, 14(18), 4503; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14184503 - 09 Sep 2022
Cited by 4 | Viewed by 1368
Abstract
A new algorithm for forest height estimation based on dual polarimetric interferometric SAR data is presented in this study. The main objective is to consider the efficiency of the dual-polarization data compared to the full polarimetric images with respect to forest height retrieval. [...] Read more.
A new algorithm for forest height estimation based on dual polarimetric interferometric SAR data is presented in this study. The main objective is to consider the efficiency of the dual-polarization data compared to the full polarimetric images with respect to forest height retrieval. Accordingly, the forest height estimation based on the random volume over the ground model is examined using a geometrical procedure named the three-stage method. An exhaustive search polarization optimization technique is also applied to improve the results by employing the efficiency of all the polarization bases based on the four-dimensional lexicographic PolInSAR vector. The repeat-pass experimental SAR (ESAR) images, which include both L- and P-band full polarimetric data, are employed for the accuracy assessment of the dual PolInSAR data and the newly proposed method for forest height estimation. The experimental results on the L-band PolInSAR data show the ability of the dual PolInSAR data for forest height estimation with an average root mean square error (RMSE) of 4.97 m against Lidar data based on the conventional three-stage method. Additionally, the proposed method results in an accuracy of 2.95 m for forest height estimation, indicating its high potential for tree height retrieval. Full article
(This article belongs to the Special Issue Polarimetric Remote Sensing)
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20 pages, 845 KiB  
Article
Polarization Estimation with a Single Vector Sensor for Radar Detection
by Yaomin He and Jian Yang
Remote Sens. 2022, 14(5), 1137; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14051137 - 25 Feb 2022
Cited by 4 | Viewed by 1509
Abstract
Target detection using radar has important applications in military and civilian fields. Aimed at targets containing interference, radar polarimetry can facilitate discrimination between the target and interference. Since existing methods require the utilization of interference signals without targets in advance, they have a [...] Read more.
Target detection using radar has important applications in military and civilian fields. Aimed at targets containing interference, radar polarimetry can facilitate discrimination between the target and interference. Since existing methods require the utilization of interference signals without targets in advance, they have a poor effect on interference with variable polarization. To solve this problem, this paper proposes a novel synchronous method to estimate the parameters of interference. First, we introduce a definition of the pulse compression signal-to-noise ratio, and prove that it is the polarization invariant in the virtual polarization adaptation. Then, for signals containing a target, interference, and noise, we propose a novel synchronous estimation method. Subsequently, we propose the two-dimensional golden selected method to further optimize the method with minimum calculation, and prove that the method presented in this paper is convergent and globally optimal. Finally, we analyze the presented method from three aspects: robustness, complexity, and applicability; the results of which demonstrate the efficacy of the method presented in this paper. Full article
(This article belongs to the Special Issue Polarimetric Remote Sensing)
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