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

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Airborne LiDAR bathymetry sediment classification considering the optimal features by using seabed point cloud
Vol. 27, Issue 9, Pages: 2219-2228(2023)
Received：22 June 2022，

Published：07 September 2023
DOI： 10.11834/jrs.20222283
稿件说明：

移动端阅览

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

Keywords

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