{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:49:36Z","timestamp":1774367376939,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T00:00:00Z","timestamp":1704326400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Mangrove monitoring and change factor analysis based on multi-source satellite remote sensing data","award":["202301001"],"award-info":[{"award-number":["202301001"]}]},{"name":"Mangrove monitoring and change factor analysis based on multi-source satellite remote sensing data","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]},{"name":"Mangrove monitoring and change factor analysis based on multi-source satellite remote sensing data","award":["57971"],"award-info":[{"award-number":["57971"]}]},{"name":"Integration and Application Demonstration in the Marine Field","award":["202301001"],"award-info":[{"award-number":["202301001"]}]},{"name":"Integration and Application Demonstration in the Marine Field","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]},{"name":"Integration and Application Demonstration in the Marine Field","award":["57971"],"award-info":[{"award-number":["57971"]}]},{"name":"Automated Identifying of Environment Changes Using Satellite Time-Series, Dragon 5 Cooperation 2020\u20132024","award":["202301001"],"award-info":[{"award-number":["202301001"]}]},{"name":"Automated Identifying of Environment Changes Using Satellite Time-Series, Dragon 5 Cooperation 2020\u20132024","award":["2020010004"],"award-info":[{"award-number":["2020010004"]}]},{"name":"Automated Identifying of Environment Changes Using Satellite Time-Series, Dragon 5 Cooperation 2020\u20132024","award":["57971"],"award-info":[{"award-number":["57971"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When it comes to the application of the photon data gathered by the Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2), accurately removing noise is crucial. In particular, conventional denoising algorithms based on local density are susceptible to missing some signal photons when there is uneven signal density distribution, as well as being susceptible to misclassifying noise photons near the signal photons; the application of deep learning remains untapped in this domain as well. To solve these problems, a method for extracting signal photons based on a GoogLeNet model fused with a Convolutional Block Attention Module (CBAM) is proposed. The network model can make good use of the distribution information of each photon\u2019s neighborhood, and simultaneously extract signal photons with different photon densities to avoid misclassification of noise photons. The CBAM enhances the network to focus more on learning the crucial features and improves its discriminative ability. In the experiments, simulation photon data in different signal-to-noise ratios (SNR) levels are utilized to demonstrate the superiority and accuracy of the proposed method. The results from signal extraction using the proposed method in four experimental areas outperform the conventional methods, with overall accuracy exceeding 98%. In the real validation experiments, reference data from four experimental areas are collected, and the elevation of signal photons extracted by the proposed method is proven to be consistent with the reference elevation, with R2 exceeding 0.87. Both simulation and real validation experiments demonstrate that the proposed method is effective and accurate for extracting signal photons.<\/jats:p>","DOI":"10.3390\/rs16010203","type":"journal-article","created":{"date-parts":[[2024,1,4]],"date-time":"2024-01-04T04:05:38Z","timestamp":1704341138000},"page":"203","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Novel ICESat-2 Signal Photon Extraction Method Based on Convolutional Neural Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8394-9240","authenticated-orcid":false,"given":"Wenjun","family":"Qin","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yan","family":"Song","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China"}]},{"given":"Yarong","family":"Zou","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China"},{"name":"National Satellite Ocean Application Service, Beijing 100081, China"}]},{"given":"Haitian","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Ocean Remote Sensing and Application, Ministry of Natural Resources, Beijing 100081, China"},{"name":"National Satellite Ocean Application Service, Beijing 100081, China"}]},{"given":"Haiyan","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Remote Sensing & Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.rse.2019.111325","article-title":"The Ice, Cloud, and Land Elevation Satellite-2 mission: A global geolocated photon product derived from the Advanced Topographic Laser Altimeter System","volume":"233","author":"Neumann","year":"2019","journal-title":"Remote Sens. 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