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Remote Sensing for Subsurface Imaging

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 September 2019)

Special Issue Editor


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Guest Editor
Department of Meteorology, University of Reading, PO Box 243, Reading RG6 6BB. UK
Interests: SAR; sub-surface imaging; soil moisture; snow and ice

Special Issue Information

Dear Colleagues,

This special issue of Remote Sensing will focus on the exploitation of remote sensing for terrestrial subsurface imaging. It looks to capture the breadth and capability of remote sensing applied to this task from both air and space, and across the entire electromagnetic spectrum, from gamma rays through to radio. Different spectral bands and imaging techniques can have very different sensitivities with regard to both scale and target type. As such, the activities are relevant both for the retrieval of features on the geological scale—such as in geological prospection or ice-sheet sounding—and also in the retrieval of subsurface geophysical parameters, such as snow water equivalent from snowpacks, or moisture distribution in soils. Submissions are welcome from any application using passive or active techniques, and it is expected that the Special Issue will be particularly relevant to the fields of physical geography, geology, archaeology, and environmental monitoring. Papers may want to report on the methodology or application of subsurface imaging, signal and image processing schemes, sensitivities and parameter extraction, or sensor design and performance. Future instrument proposals or new mission concepts with a clear focus on subsurface imaging would also be welcome.

Prof. Keith Morrison
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. 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

  • terrestrial
  • signal processing
  • soil
  • snow and ice
  • archeology
  • radar sounding
  • hyperspectral
  • gamma ray

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Published Papers (1 paper)

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Research

15 pages, 3212 KiB  
Article
Application of Mathematical Morphological Filtering to Improve the Resolution of Chang’E-3 Lunar Penetrating Radar Data
by Jianmin Zhang, Zhaofa Zeng, Ling Zhang, Qi Lu and Kun Wang
Remote Sens. 2019, 11(5), 524; https://doi.org/10.3390/rs11050524 - 4 Mar 2019
Cited by 10 | Viewed by 4361
Abstract
As one of the important scientific instruments of lunar exploration, the Lunar Penetrating Radar (LPR) onboard China’s Chang’E-3 (CE-3) provides a unique opportunity to image the lunar subsurface structure. Due to the low-frequency and high-frequency noises of the data, only a few geological [...] Read more.
As one of the important scientific instruments of lunar exploration, the Lunar Penetrating Radar (LPR) onboard China’s Chang’E-3 (CE-3) provides a unique opportunity to image the lunar subsurface structure. Due to the low-frequency and high-frequency noises of the data, only a few geological structures are visible. In order to better improve the resolution of the data, band-pass filtering and empirical mode decomposition filtering (EMD) methods are usually used, but in this paper, we present a mathematical morphological filtering (MMF) method to reduce the noise. The MMF method uses two structural elements with different scales to extract certain scale-range information from the original signal, at the same time, the noise beyond the scale range of the two different structural elements is suppressed. The application on synthetic signals demonstrates that the morphological filtering method has a better performance in noise suppression compared with band-pass filtering and EMD methods. Then, we apply band-pass filtering, EMD, and MMF methods to the LPR data, and the MMF method also achieves a better result. Furthermore, according to the result by MMF method, three stratigraphic zones are revealed along the rover’s route. Full article
(This article belongs to the Special Issue Remote Sensing for Subsurface Imaging)
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