Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model

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’s full return waveform, including the water surface, water column, seafloor, and the objects...
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Gespeichert in:

Autor*in:	Yifan Huang [verfasserIn] Yan He [verfasserIn] Xiaolei Zhu [verfasserIn] Jiayong Yu [verfasserIn] Yongqiang Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	LiDAR bathymetry deep learning point cloud semantic segmentation

Übergeordnetes Werk:	In: Remote Sensing - MDPI AG, 2009, 15(2023), 9, p 2326
Übergeordnetes Werk:	volume:15 ; year:2023 ; number:9, p 2326

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/rs15092326

Katalog-ID:	DOAJ090339851

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520			\|a 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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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.
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spelling	10.3390/rs15092326 doi (DE-627)DOAJ090339851 (DE-599)DOAJ22ca2c88a9d74d6489db6afa7af7d58b DE-627 ger DE-627 rakwb eng Yifan Huang verfasserin aut Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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. LiDAR bathymetry deep learning point cloud semantic segmentation Science Q Yan He verfasserin aut Xiaolei Zhu verfasserin aut Jiayong Yu verfasserin aut Yongqiang Chen verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2326 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2326 https://doi.org/10.3390/rs15092326 kostenfrei https://doaj.org/article/22ca2c88a9d74d6489db6afa7af7d58b kostenfrei https://www.mdpi.com/2072-4292/15/9/2326 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2326
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allfieldsGer	10.3390/rs15092326 doi (DE-627)DOAJ090339851 (DE-599)DOAJ22ca2c88a9d74d6489db6afa7af7d58b DE-627 ger DE-627 rakwb eng Yifan Huang verfasserin aut Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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. LiDAR bathymetry deep learning point cloud semantic segmentation Science Q Yan He verfasserin aut Xiaolei Zhu verfasserin aut Jiayong Yu verfasserin aut Yongqiang Chen verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2326 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2326 https://doi.org/10.3390/rs15092326 kostenfrei https://doaj.org/article/22ca2c88a9d74d6489db6afa7af7d58b kostenfrei https://www.mdpi.com/2072-4292/15/9/2326 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2326
allfieldsSound	10.3390/rs15092326 doi (DE-627)DOAJ090339851 (DE-599)DOAJ22ca2c88a9d74d6489db6afa7af7d58b DE-627 ger DE-627 rakwb eng Yifan Huang verfasserin aut Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier 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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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. LiDAR bathymetry deep learning point cloud semantic segmentation Science Q Yan He verfasserin aut Xiaolei Zhu verfasserin aut Jiayong Yu verfasserin aut Yongqiang Chen verfasserin aut In Remote Sensing MDPI AG, 2009 15(2023), 9, p 2326 (DE-627)608937916 (DE-600)2513863-7 20724292 nnns volume:15 year:2023 number:9, p 2326 https://doi.org/10.3390/rs15092326 kostenfrei https://doaj.org/article/22ca2c88a9d74d6489db6afa7af7d58b kostenfrei https://www.mdpi.com/2072-4292/15/9/2326 kostenfrei https://doaj.org/toc/2072-4292 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2108 GBV_ILN_2111 GBV_ILN_2119 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4392 GBV_ILN_4700 AR 15 2023 9, p 2326
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author	Yifan Huang
spellingShingle	Yifan Huang misc LiDAR misc bathymetry misc deep learning misc point cloud semantic segmentation misc Science misc Q Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model
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topic_title	Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model LiDAR bathymetry deep learning point cloud semantic segmentation
topic	misc LiDAR misc bathymetry misc deep learning misc point cloud semantic segmentation misc Science misc Q
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title	Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model
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title_sort	faint echo extraction from alb waveforms using a point cloud semantic segmentation model
title_auth	Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model
abstract	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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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.
abstractGer	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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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.
abstract_unstemmed	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’s full return waveform, including the water surface, water column, seafloor, and the objects on it. Due to the seawater’s absorption and scattering and the seafloor’s reflectivity effect, the seafloor’s 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’ 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’s amplitude, but also its neighboring points’ distance and amplitude. This enriched the centroids’ 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.
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title_short	Faint Echo Extraction from ALB Waveforms Using a Point Cloud Semantic Segmentation Model
url	https://doi.org/10.3390/rs15092326 https://doaj.org/article/22ca2c88a9d74d6489db6afa7af7d58b https://www.mdpi.com/2072-4292/15/9/2326 https://doaj.org/toc/2072-4292
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up_date	2024-07-03T14:11:13.977Z
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