{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T18:44:23Z","timestamp":1773081863385,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,6,25]],"date-time":"2023-06-25T00:00:00Z","timestamp":1687651200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["12174078"],"award-info":[{"award-number":["12174078"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Non-Gaussian impulsive noise in marine environments strongly influences the detection of weak spectral lines. However, existing detection algorithms based on the Gaussian noise model are futile under non-Gaussian impulsive noise. Therefore, a deep-learning method called AINP+LR-DRNet is proposed for joint detection and the reconstruction of weak spectral lines. First, non-Gaussian impulsive noise suppression was performed by an impulsive noise preprocessor (AINP). Second, a special detection and reconstruction network (DRNet) was proposed. An end-to-end training application learns to detect and reconstruct weak spectral lines by adding into an adaptive weighted loss function based on dual classification. Finally, a spectral line-detection algorithm based on DRNet (LR-DRNet) was proposed to improve the detection performance. The simulation indicated that the proposed AINP+LR-DRNet can detect and reconstruct weak spectral line features under non-Gaussian impulsive noise, even for a mixed signal-to-noise ratio as low as \u221226 dB. The performance of the proposed method was validated using experimental data. The proposed AINP+LR-DRNet detects and reconstructs spectral lines under strong background noise and interference with better reliability than other algorithms.<\/jats:p>","DOI":"10.3390\/rs15133268","type":"journal-article","created":{"date-parts":[[2023,6,26]],"date-time":"2023-06-26T03:14:56Z","timestamp":1687749296000},"page":"3268","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Joint Detection and Reconstruction of Weak Spectral Lines under Non-Gaussian Impulsive Noise with Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhen","family":"Li","sequence":"first","affiliation":[{"name":"Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China"},{"name":"College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"given":"Junyuan","family":"Guo","sequence":"additional","affiliation":[{"name":"Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China"},{"name":"College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5714-2013","authenticated-orcid":false,"given":"Xiaohan","family":"Wang","sequence":"additional","affiliation":[{"name":"Acoustic Science and Technology Laboratory, Harbin Engineering University, Harbin 150001, China"},{"name":"Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China"},{"name":"College of Underwater Acoustic Engineering, Harbin Engineering University, Harbin 150001, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,25]]},"reference":[{"key":"ref_1","first-page":"193","article-title":"The detection of single frequency component of underwater radiated noise of target: Theoretical analysis","volume":"33","author":"Li","year":"2008","journal-title":"Acta Acust."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"941","DOI":"10.1109\/5.30749","article-title":"Time-frequency distributions\u2014A review","volume":"77","author":"Cohen","year":"1989","journal-title":"Proc. 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