{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T17:05:39Z","timestamp":1776186339036,"version":"3.50.1"},"reference-count":23,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T00:00:00Z","timestamp":1664409600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Ground penetrating radar (GPR) is one of the most generally used underground sensing equipment, but it is frequently contaminated by clutter and noise during data acquisition, which has a significant impact on the detection performance of buried targets. The purpose of this letter is to present a novel clutter suppression method based on the principal component Gaussian curvature decomposition (PCGCD). First, the GPR B-scan data are divided into different sub-components using principal component analysis (PCA). Then, a Gaussian curvature decomposition (GCD) method is proposed, which can be applied to PCA domain subspaces to recover more target structure information from random noise. The PCGCD method\u2019s performance is evaluated using both numerical simulation and real-world GPR datasets. The visualization and quantitative results demonstrated our method\u2019s superiority in protecting the underground target structure, removing complex random noise, and improving the detection ability of buried targets.<\/jats:p>","DOI":"10.3390\/rs14194879","type":"journal-article","created":{"date-parts":[[2022,9,29]],"date-time":"2022-09-29T23:09:29Z","timestamp":1664492969000},"page":"4879","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["GPR Image Clutter Suppression Using Gaussian Curvature Decomposition in the PCA Domain"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1277-317X","authenticated-orcid":false,"given":"Qibin","family":"Su","sequence":"first","affiliation":[{"name":"The College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Beizhen","family":"Bi","sequence":"additional","affiliation":[{"name":"The College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Pengyu","family":"Zhang","sequence":"additional","affiliation":[{"name":"The College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Liang","family":"Shen","sequence":"additional","affiliation":[{"name":"The College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Xiaotao","family":"Huang","sequence":"additional","affiliation":[{"name":"The College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]},{"given":"Qin","family":"Xin","sequence":"additional","affiliation":[{"name":"The College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"3271","DOI":"10.1109\/TGRS.2018.2882912","article-title":"Adaptive ground clutter reduction in ground penetrating radar data based on principal component analysis","volume":"57","author":"Chen","year":"2019","journal-title":"IEEE Trans. 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