{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:06:29Z","timestamp":1775837189920,"version":"3.50.1"},"reference-count":26,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T00:00:00Z","timestamp":1682640000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Social Development Science and Technology Project","award":["21DZ1205400"],"award-info":[{"award-number":["21DZ1205400"]}]},{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Social Development Science and Technology Project","award":["61991450"],"award-info":[{"award-number":["61991450"]}]},{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Social Development Science and Technology Project","award":["61991453"],"award-info":[{"award-number":["61991453"]}]},{"name":"Shanghai \u201cScience and Technology Innovation Action Plan\u201d Social Development Science and Technology Project","award":["42106180"],"award-info":[{"award-number":["42106180"]}]},{"name":"National Natural Science Foundation of China","award":["21DZ1205400"],"award-info":[{"award-number":["21DZ1205400"]}]},{"name":"National Natural Science Foundation of China","award":["61991450"],"award-info":[{"award-number":["61991450"]}]},{"name":"National Natural Science Foundation of China","award":["61991453"],"award-info":[{"award-number":["61991453"]}]},{"name":"National Natural Science Foundation of China","award":["42106180"],"award-info":[{"award-number":["42106180"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As an active remote sensing technology, airborne LIDAR can work at all times while emitting specific wavelengths of laser light that can penetrate seawater. Airborne LIDAR bathymetry (ALB) records an object\u2019s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater\u2019s absorption and scattering and the seafloor\u2019s reflectivity effect, the seafloor\u2019s amplitude of seafloor echoes varies greatly. Seafloor echoes with low signal-to-noise ratios are not easily detected using waveform processing methods, which can lead to insufficient seafloor topography depth and incomplete seafloor topography coverage. To extract faint seafloor echoes, we proposed a depth extraction method based on the PointConv deep learning model, called FWConv. The method assumed that spatially adjacent echoes were correlated. We converted all the spatially adjacent multi-frame waveforms into a point cloud. Each point represented a bin value in the waveform, and the points\u2019 properties contained spatial coordinates and the amplitude in the waveform. In the semantic segmentation of these point clouds using deep learning models, we considered not only each centroid\u2019s amplitude, but also its neighboring points\u2019 distance and amplitude. This enriched the centroids\u2019 features and allowed the model to better discriminate between background noise and seafloor echoes. The results showed that FWConv could extract faint seafloor echoes in the experimental area and was not easily affected by noise, and that the correctness reached 99.82%. The number of point clouds increased by 158%, and the seafloor elevation accuracy reached 0.20 m concerning the multibeam echo sounder data.<\/jats:p>","DOI":"10.3390\/rs15092326","type":"journal-article","created":{"date-parts":[[2023,4,28]],"date-time":"2023-04-28T04:36:15Z","timestamp":1682656575000},"page":"2326","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model"],"prefix":"10.3390","volume":"15","author":[{"given":"Yifan","family":"Huang","sequence":"first","affiliation":[{"name":"Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China"},{"name":"Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Yan","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China"},{"name":"Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Department of Guanlan Ocean Science Satellites, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China"}]},{"given":"Xiaolei","family":"Zhu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China"}]},{"given":"Jiayong","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Civil Engineering, Anhui Jianzhu University, Hefei 230601, China"}]},{"given":"Yongqiang","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Space Laser Communication and Detection Technology, Shanghai Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Shanghai 201800, China"},{"name":"Center of Materials Science and Optoelectronics Engineering, University of Chinese Academy of Sciences, Beijing 100049, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,28]]},"reference":[{"key":"ref_1","first-page":"191","article-title":"Assessment of depth and turbidity with airborne Lidar bathymetry and multiband satellite imagery in shallow water bodies of the Alaskan North Slope","volume":"58","author":"Saylam","year":"2017","journal-title":"Int. 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