Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud

SU Dianpeng; HUANG Yu; YANG Fanlin; ZHAO Dineng; YANG Anxiu; LIU Jiaoyang

doi:10.11834/jrs.20222283

Models and Methods | Views : 0 下载量: 163 CSCD: 0 更多指标

R-PDF
PDF
Export
Share
Collection
Album

Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud
Vol. 27, Issue 9, Pages: 2219-2228(2023)
Published： 07 September 2023 ，
DOI： 10.11834/jrs.20222283

扫描看全文

宿殿鹏，黄昱，阳凡林，赵荻能，杨安秀，刘骄阳.2023.顾及特征优选的机载LiDAR测深海底点云底质分类.遥感学报，27（9）： 2219-2228

Su D P， Huang Y， Yang F L，Zhao D N， Yang A X and Liu J Y. 2023. Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud. National Remote Sensing Bulletin， 27（9）：2219-2228
宿殿鹏，黄昱，阳凡林，赵荻能，杨安秀，刘骄阳.2023.顾及特征优选的机载LiDAR测深海底点云底质分类.遥感学报，27（9）： 2219-2228 DOI： 10.11834/jrs.20222283.

Su D P， Huang Y， Yang F L，Zhao D N， Yang A X and Liu J Y. 2023. Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud. National Remote Sensing Bulletin， 27（9）：2219-2228 DOI： 10.11834/jrs.20222283.

摘要

基于机载LiDAR测深ALB（Airborne LiDAR Bathymetry）技术的海底底质分类能够为浅海水域的海洋资源开发利用、海洋环境保护、海洋工程建设等提供基础数据，对海洋活动与海洋科学研究具有重要意义。针对ALB海底底质分类存在的特征冗余问题，本文提出了一种顾及波形和地形特征优选的底质分类算法。在提取波形和地形特征的基础上，构建Relief-F特征优选模型，通过计算各特征在底质分类中的贡献率，实现多元特征优选；然后，利用随机森林RF（Random Forest）、支持向量机SVM（Support Vector Machine）、BP神经网络BPNN（Back Propagation Neural Network）3种分类器进行监督分类，提取珊瑚礁、砾石、砂、植被、海岸带5类底质。为验证所提分类方法的有效性，利用西沙甘泉岛实测ALB数据进行实验，结果表明：利用Relief-F算法进行特征优选后，RF、SVM与BPNN的分类精度分别提高了1.1%、1.1%和2.7%；其中，随机森林底质分类具有更高的分类精度，其总体分类精度OA（Overall Accuracy）和Kappa系数分别达到了95.36%和0.94。本文研究成果能够为海洋工程等领域的海底底质分类需求提供有效的技术支撑。

Abstract

Airborne LiDAR bathymetry (ALB) seabed sediment classification can provide basic data for the development and utilization of marine resources

marine environmental protection

marine engineering construction

and other fields

which has great relevance to marine activities and marine scientific research. To solve the feature redundancy problem in ALB seabed sediment classification

this paper proposes a sediment classification algorithm considering optimal waveform and topographic features. Based on the extracted waveform and topographic features

the Relief-F feature optimization model is constructed

and multivariate features are optimized by calculating the contribution rate of each feature in the sediment classification. Then

random forest

support vector machine

and BP neural network classifiers are used to classify coral reefs

gravel

sand

vegetation

and coastal zones five types of sediments. The proposed method is verified using the ALB data captured around Ganquan Island in the Xisha Archipelago. The experiment results showed that after using the Relief-F algorithm for feature optimization

the classification accuracies of RF

SVM

and BP neural network improve by 1.1%

1.1%

and 2.7%

respectively. The random forest sediment classification has higher classification accuracy

and the overall accuracy and Kappa coefficient reach 95.36% and 0.94

respectively. The research results can provide effective technical support for the seabed sediment classification in the fields of marine engineering and other fields.

关键词

机载LiDAR测深底质分类波形特征地形特征Relief-F特征优选模型图像处理海洋

Keywords

airborne LiDAR bathymetrysediment classificationwaveform featurestopographic featuresRelief-F feature optimization modelimage processingocean

references

Abdallah H, Baghdadi N, Bailly J S, Pastol Y and Fabre F. 2012. Wa-LiD: a new LiDAR simulator for waters. IEEE Geoscience and Remote Sensing Letters, 9(4): 744-748 [DOI: 10.1109/LGRS.2011.2180506http://dx.doi.org/10.1109/LGRS.2011.2180506]

Alexander C, Tansey K, Kaduk J, Holland D and Tate N J. 2010. Backscatter coefficient as an attribute for the classification of full-waveform airborne laser scanning data in urban areas. ISPRS Journal of Photogrammetry and Remote Sensing, 65(5): 423-432 [DOI: 10.1016/j.isprsjprs.2010.05.002http://dx.doi.org/10.1016/j.isprsjprs.2010.05.002]

Ayustina R, Aulia Z, Mustakin H, Alam F, Amron A, Yuwono D, Ahmad T, Prayogo A and Sari F. 2018. Classification of shallow water seabed profile based on Landsat 8 imagery and in-situ data. Case study in Gili Matra Island Lombok, Indonesia. E3S Web of Conferences, 47: 04002 [DOI: 10.1051/e3sconf/20184704002http://dx.doi.org/10.1051/e3sconf/20184704002]

Chen J, Mao X C, Liu Z K and Deng H. 2020. Three-dimensional metallogenic prediction based on random forest classification algorithm for the Dayingezhuang gold deposit. Geotectonica et Metallogenia, 44(2): 231-241

陈进, 毛先成, 刘占坤, 邓浩. 2020. 基于随机森林算法的大尹格庄金矿床三维成矿预测. 大地构造与成矿学, 44(2): 231-241 [DOI: 10.16539/j.ddgzyckx.2020.02.007http://dx.doi.org/10.16539/j.ddgzyckx.2020.02.007]

Chen J B, Wu Z Y, Zhao D N, Zhou J Q, Li S J and Shang J H. 2017. Back propagation neural network classification of sediment seabed acoustic sonar images based on particle swarm optimization algorithms. Acta Oceanologica Sinica, 39(9): 51-57

陈佳兵, 吴自银, 赵荻能, 周洁琼, 李守军, 尚继宏. 2017. 基于粒子群优化算法的PSO-BP海底声学底质分类方法. 海洋学报, 39(9): 51-57 [DOI: 10.3969/j.issn.0253-4193.2017.09.005http://dx.doi.org/10.3969/j.issn.0253-4193.2017.09.005]

Chen K. 2021. Research on Feature Selection Methods for High-Dimensional Classification. Ji’nan: Shandong University

陈科. 2021. 面向高维数据的分类特征选择方法研究. 济南: 山东大学 [DOI: 10.27272/d.cnki.gshdu.2021.000376http://dx.doi.org/10.27272/d.cnki.gshdu.2021.000376]

Dai M F, Xing S, Xu Q, Li P C and Chen K. 2022. Semantic segmentation of airborne LiDAR point cloud based on multi-feature fusion and geometric convolution. Journal of Image and Graphics, 27(2): 574-585

戴莫凡, 邢帅, 徐青, 李鹏程, 陈坤. 2022. 多特征融合与几何卷积的机载LiDAR点云地物分类. 中国图象图形学报, 27(2): 574-585 [DOI: 10.11834/jig.210555http://dx.doi.org/10.11834/jig.210555]

Eren F, Pe'Eri S, Rzhanov Y and Ward L. 2018. Bottom characterization by using airborne lidar bathymetry (ALB) waveform features obtained from bottom return residual analysis. Remote Sensing of Environment, 206: 260-274 [DOI: 10.1016/j.rse.2017.12.035http://dx.doi.org/10.1016/j.rse.2017.12.035]

Ge Q, Ruan F X, Qiao B J, Zhang Q, Zuo X Y and Dang L X. 2021. Side-scan sonar image classification based on style transfer and pre-trained convolutional neural networks. Electronics, 10(15): 1823 [DOI: 10.3390/electronics10151823http://dx.doi.org/10.3390/electronics10151823]

Hou D J, Wu C, Li C Z, Guo S M and Wu J J. 2019. Energy calibration and performance testing of LEGe detector. The Journal of Engineering, 2019(23): 9031-9034 [DOI: 10.1049/joe.2018.9175http://dx.doi.org/10.1049/joe.2018.9175]

Huang L L, Tang J, Sun D D and Luo B. 2012. Feature selection algorithm based on multi-label ReliefF. Journal of Computer Applications, 32(10): 2888-2890, 2898

黄莉莉, 汤进, 孙登第, 罗斌. 2012. 基于多标签ReliefF的特征选择算法. 计算机应用, 32(10): 2888-2890, 2898 [DOI: 10.3724/SP.J.1087.2012.02888http://dx.doi.org/10.3724/SP.J.1087.2012.02888]

Joanes D N and Gill C A. 1998. Comparing measures of sample skewness and kurtosis. Journal of the Royal Statistical Society: Series D (The Statistician), 47(1): 183-189 [DOI: 10.1111/1467-9884.00122http://dx.doi.org/10.1111/1467-9884.00122]

Lai X D and Zheng M. 2015. A denoising method for LiDAR full-waveform data. Mathematical Problems in Engineering, 2015: 164318 [DOI: 10.1155/2015/164318http://dx.doi.org/10.1155/2015/164318]

Lee A L, Bartle K D and Lewis A C. 2001. A model of peak amplitude enhancement in orthogonal two-dimensional gas chromatography. Analytical Chemistry, 73(6): 1330-1335 [DOI: 10.1021/ac001120shttp://dx.doi.org/10.1021/ac001120s]

Li L F, Liu F, Wang C and Chen L S. 2012. Measurement and control of residual amplitude modulation in optical phase modulation. Review of Scientific Instruments, 83(4): 043111 [DOI: 10.1063/1.4704084http://dx.doi.org/10.1063/1.4704084]

Liu Y M, Deng R R, Qin Y and Liang Y H. 2017. Data processing methods and applications of airborne LiDAR bathymetry. Journal of Remote Sensing, 21(6): 982-995

刘永明, 邓孺孺, 秦雁, 梁业恒. 2017. 机载激光雷达测深数据处理与应用. 遥感学报, 21(6): 982-995 [DOI: 10.11834/jrs.20176395http://dx.doi.org/10.11834/jrs.20176395]

Liu Y X, Guo K, He X F, Xu W X and Feng Y K. 2017. Research progress of airborne laser bathymetry technology. Geomatics and Information Science of Wuhan University, 42(9): 1185-1194

刘焱雄, 郭锴, 何秀凤, 徐文学, 冯义楷. 2017. 机载激光测深技术及其研究进展. 武汉大学学报: 信息科学版, 42(9): 1185-1194 [DOI: 10.13203/j.whugis20150779http://dx.doi.org/10.13203/j.whugis20150779]

Matsatsinis N F and Samaras A P. 2000. Brand choice model selection based on consumers’ multicriteria preferences and experts’ knowledge. Computers and Operations Research, 27(7/8): 689-707 [DOI: 10.1016/S0305-0548(99)00114-8http://dx.doi.org/10.1016/S0305-0548(99)00114-8]

Qi C, Su D P, Wang X K, Wang M W, Shi B and Yang F L. 2019. Fitting algorithm for airborne laser bathymetric waveforms based on layered heterogeneous model. Infrared and Laser Engineering, 48(2): 206004

亓超, 宿殿鹏, 王贤昆, 王明伟, 石波, 阳凡林. 2019. 基于分层异构模型的机载激光测深波形拟合算法. 红外与激光工程, 48(2): 206004 [DOI: 10.3788/IRLA201948.0206004http://dx.doi.org/10.3788/IRLA201948.0206004]

Qi C, Zhou F N, Wu J W, Su D P, Wang X K and Yang F L. 2021. Extraction method for diffuse attenuation coefficient based on airborne LiDAR bathymetric water column waveform. Acta Oceanologica Sinica, 43(1): 147-154

亓超, 周丰年, 吴敬文, 宿殿鹏, 王贤昆, 阳凡林. 2021. 基于机载LiDAR测深水体波形的漫衰减系数提取方法. 海洋学报, 43(1): 147-154 [DOI: 10.12284/hyxb2021005http://dx.doi.org/10.12284/hyxb2021005]

Qiao J G, Liu X P and Zhang Y H. 2011. Land cover classification using LiDAR height texture and ANNs. Journal of Remote Sensing, 15(3): 539-553

乔纪纲, 刘小平, 张亦汉. 2011. 基于LiDAR高度纹理和神经网络的地物分类. 遥感学报, 15(3): 539-553 [DOI: 10.11834/jrs.20119282http://dx.doi.org/10.11834/jrs.20119282]

Su D P, Yang F L, Ma Y, Zhang K, Huang J and Wang M W. 2019. Classification of coral reefs in the South China Sea by combining airborne LiDAR bathymetry bottom waveforms and bathymetric features. IEEE Transactions on Geoscience and Remote Sensing, 57(2): 815-828 [DOI: 10.1109/TGRS.2018.2860931http://dx.doi.org/10.1109/TGRS.2018.2860931]

Tang Q H, Li J, Zhou X H, Lu K and Zhang Z Z. 2014. Seabed sonar image analysis and acoustic seabed classification in the south of the Cheju Island. Acta Oceanologica Sinica, 36(7): 133-141

唐秋华, 李杰, 周兴华, 陆凯, 张志繤. 2014. 济州岛南部海域海底声呐图像分析与声学底质分类. 海洋学报, 36(7): 133-141 [DOI: 10.3969/j.issn.0253-4193.2014.07.015http://dx.doi.org/10.3969/j.issn.0253-4193.2014.07.015]

Velasco J, Molina I, Martinez E, Arquero A and Prieto J F. 2014. Sea bottom classification by means of bathymetric LIDAR data. IEEE Latin America Transactions, 12(4): 590-595 [DOI: 10.1109/TLA.2014.6868859http://dx.doi.org/10.1109/TLA.2014.6868859]

Xu C. 2014. Study on Seabed Classification Technology Based on Multibeam Bathymetry Sonar. Harbin: Harbin Engineering University

徐超. 2014. 多波束测深声呐海底底质分类技术研究. 哈尔滨: 哈尔滨工程大学

Yang F L, Zhu Z R, Li J B, Feng C K, Xing Z and Wu Z Y. 2021. Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network. Acta Geodaetica et Cartographica Sinica, 50(1): 71-84

阳凡林, 朱正任, 李家彪, 冯成凯, 邢喆, 吴自银. 2021. 利用深层卷积神经网络实现地形辅助的多波束海底底质分类. 测绘学报, 50(1): 71-84 [DOI: 10.11947/j.AGCS.2021.20200065http://dx.doi.org/10.11947/j.AGCS.2021.20200065]

Yang M L, Fan Y G and Li B Y. 2020. Research on dimensionality reduction and classification of hyperspectral images based on LDA and ELM. Journal of Electronic Measurement and Instrumentation, 34(5): 190-196

杨明莉, 范玉刚, 李宝芸. 2020. 基于LDA和ELM的高光谱图像降维与分类方法研究. 电子测量与仪器学报, 34(5): 190-196 [DOI: 10.13382/j.jemi.B1902756http://dx.doi.org/10.13382/j.jemi.B1902756]

Zang Z, Lin H and Yang M H. 2014. Dimensional reduction and classification of hyperspectral data for tree species using PCA algorithm. Science of Surveying and Mapping, 39(2): 146-149

臧卓, 林辉, 杨敏华. 2014. 利用PCA算法进行乔木树种高光谱数据降维与分类. 测绘科学, 39(2): 146-149 [DOI: 10.16251/j.cnki.1009-2307.2014.02.031http://dx.doi.org/10.16251/j.cnki.1009-2307.2014.02.031]

Zhang H. 2012. Characteristic weighting FCM based on improved ReliefF algorithm. Shipboard Electronic Countermeasure, 35(1): 79-82, 85

张鸿. 2012. 基于改进ReliefF算法的特征加权FCM. 舰船电子对抗, 35(1): 79-82, 85 [DOI: 10.16426/j.cnki.jcdzdk.2012.01.030http://dx.doi.org/10.16426/j.cnki.jcdzdk.2012.01.030]

Zhao Y X and Zhao T. 2020. Survey of the intelligent seabed sediment classification technology based on sonar images. CAAI Transactions on Intelligent Systems, 15(3): 587-600

赵玉新, 赵廷. 2020. 海底声呐图像智能底质分类技术研究综述. 智能系统学报, 15(3): 587-600 [DOI: 10.11992/tis.202004026http://dx.doi.org/10.11992/tis.202004026]

Alert me when the article has been cited

提交

New building extraction method based on semantic segmentation

Freshly-opened swidden mapping using Support Vector Machine （SVM） and spatial characteristics in Phongsaly Province， Laos

Progress of the study on coral reef remote sensing

Detection and analysis of television frequency interference from an FY-3C microwave radiation imager