Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm
Radar sounders are unique instruments for subsurface investigation in both terrestrial and space applications. They are widely employed for monitoring changes to the polar ice sheets and for the study of planetary bodies (e.g., Mars). The analysis of the very large amount of data produced by such sy...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
---|---|
Main Authors: | , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
2016
|
Subjects: | |
Online Access: | http://hdl.handle.net/11572/168339 https://doi.org/10.1109/TGRS.2016.2616949 https://ieeexplore.ieee.org/document/7731235 |
id |
ftutrentoiris:oai:iris.unitn.it:11572/168339 |
---|---|
record_format |
openpolar |
spelling |
ftutrentoiris:oai:iris.unitn.it:11572/168339 2024-02-11T10:06:57+01:00 Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm Carrer, Leonardo Bruzzone, Lorenzo Carrer, Leonardo Bruzzone, Lorenzo 2016 ELETTRONICO http://hdl.handle.net/11572/168339 https://doi.org/10.1109/TGRS.2016.2616949 https://ieeexplore.ieee.org/document/7731235 eng eng info:eu-repo/semantics/altIdentifier/wos/WOS:000392391800028 volume:55 issue:2 firstpage:962 lastpage:977 numberofpages:16 journal:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING http://hdl.handle.net/11572/168339 doi:10.1109/TGRS.2016.2616949 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84994351541 https://ieeexplore.ieee.org/document/7731235 info:eu-repo/semantics/closedAccess Electrical and Electronic Engineering Earth and Planetary Sciences (all) Ground-penetrating radar (GPR) hidden Markov model (HMM) layer boundaries radar signal enhancement radar sounding radargram denoising Viterbi algorithm (VA) info:eu-repo/semantics/article 2016 ftutrentoiris https://doi.org/10.1109/TGRS.2016.2616949 2024-01-23T23:08:24Z Radar sounders are unique instruments for subsurface investigation in both terrestrial and space applications. They are widely employed for monitoring changes to the polar ice sheets and for the study of planetary bodies (e.g., Mars). The analysis of the very large amount of data produced by such systems requires the development of automatic techniques for an objective, accurate, and fast extraction of relevant information from radargrams. In this paper, we propose a novel technique for the automatic detection of layer boundaries based on a local scale hidden Markov model (HMM), which models the radar response in the presence of a layer boundary, and the Viterbi algorithm (VA, which performs the inference step). The proposed technique is based on a divide and conquer strategy that executes the VA using the observation data and the HMM to infer the most likely layer boundary location within a small radargram portion. Finally, a detection strategy is defined to chain together the inferred local layer locations. Furthermore, a novel radargram enhancement and denoising technique tailored to support the detection step is presented. The effectiveness of the proposed technique has been confirmed by testing it on different radargrams acquired by shallow radar over the north pole of Mars. The results obtained point out the superiority of the proposed method in retrieving the position of each layer boundary (and thus of the related intensity and geometric properties) with respect to the state-of-the-art techniques. Article in Journal/Newspaper North Pole Università degli Studi di Trento: CINECA IRIS North Pole IEEE Transactions on Geoscience and Remote Sensing 55 2 962 977 |
institution |
Open Polar |
collection |
Università degli Studi di Trento: CINECA IRIS |
op_collection_id |
ftutrentoiris |
language |
English |
topic |
Electrical and Electronic Engineering Earth and Planetary Sciences (all) Ground-penetrating radar (GPR) hidden Markov model (HMM) layer boundaries radar signal enhancement radar sounding radargram denoising Viterbi algorithm (VA) |
spellingShingle |
Electrical and Electronic Engineering Earth and Planetary Sciences (all) Ground-penetrating radar (GPR) hidden Markov model (HMM) layer boundaries radar signal enhancement radar sounding radargram denoising Viterbi algorithm (VA) Carrer, Leonardo Bruzzone, Lorenzo Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm |
topic_facet |
Electrical and Electronic Engineering Earth and Planetary Sciences (all) Ground-penetrating radar (GPR) hidden Markov model (HMM) layer boundaries radar signal enhancement radar sounding radargram denoising Viterbi algorithm (VA) |
description |
Radar sounders are unique instruments for subsurface investigation in both terrestrial and space applications. They are widely employed for monitoring changes to the polar ice sheets and for the study of planetary bodies (e.g., Mars). The analysis of the very large amount of data produced by such systems requires the development of automatic techniques for an objective, accurate, and fast extraction of relevant information from radargrams. In this paper, we propose a novel technique for the automatic detection of layer boundaries based on a local scale hidden Markov model (HMM), which models the radar response in the presence of a layer boundary, and the Viterbi algorithm (VA, which performs the inference step). The proposed technique is based on a divide and conquer strategy that executes the VA using the observation data and the HMM to infer the most likely layer boundary location within a small radargram portion. Finally, a detection strategy is defined to chain together the inferred local layer locations. Furthermore, a novel radargram enhancement and denoising technique tailored to support the detection step is presented. The effectiveness of the proposed technique has been confirmed by testing it on different radargrams acquired by shallow radar over the north pole of Mars. The results obtained point out the superiority of the proposed method in retrieving the position of each layer boundary (and thus of the related intensity and geometric properties) with respect to the state-of-the-art techniques. |
author2 |
Carrer, Leonardo Bruzzone, Lorenzo |
format |
Article in Journal/Newspaper |
author |
Carrer, Leonardo Bruzzone, Lorenzo |
author_facet |
Carrer, Leonardo Bruzzone, Lorenzo |
author_sort |
Carrer, Leonardo |
title |
Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm |
title_short |
Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm |
title_full |
Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm |
title_fullStr |
Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm |
title_full_unstemmed |
Automatic Enhancement and Detection of Layering in Radar Sounder Data Based on a Local Scale Hidden Markov Model and the Viterbi Algorithm |
title_sort |
automatic enhancement and detection of layering in radar sounder data based on a local scale hidden markov model and the viterbi algorithm |
publishDate |
2016 |
url |
http://hdl.handle.net/11572/168339 https://doi.org/10.1109/TGRS.2016.2616949 https://ieeexplore.ieee.org/document/7731235 |
geographic |
North Pole |
geographic_facet |
North Pole |
genre |
North Pole |
genre_facet |
North Pole |
op_relation |
info:eu-repo/semantics/altIdentifier/wos/WOS:000392391800028 volume:55 issue:2 firstpage:962 lastpage:977 numberofpages:16 journal:IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING http://hdl.handle.net/11572/168339 doi:10.1109/TGRS.2016.2616949 info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-84994351541 https://ieeexplore.ieee.org/document/7731235 |
op_rights |
info:eu-repo/semantics/closedAccess |
op_doi |
https://doi.org/10.1109/TGRS.2016.2616949 |
container_title |
IEEE Transactions on Geoscience and Remote Sensing |
container_volume |
55 |
container_issue |
2 |
container_start_page |
962 |
op_container_end_page |
977 |
_version_ |
1790604984599969792 |