标题：Classification of Coral Reefs in the South China Sea by Combining Airborne LiDAR Bathymetry Bottom Waveforms and Bathymetric Features
作者：Su, Dianpeng; Yang, Fanlin; Ma, Yue; Zhang, Kai; Huang, Jue; Wang, Mingwei
作者机构：[Su, Dianpeng; Yang, Fanlin; Ma, Yue; Zhang, Kai; Huang, Jue; Wang, Mingwei] Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China 更多
通讯作者：Yang, Fanlin;Yang, FL;Ma, Y
通讯作者地址：[Yang, FL; Ma, Y]Shandong Univ Sci & Technol, Coll Geomat, Qingdao 266590, Peoples R China.
来源：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
关键词：Airborne LiDAR bathymetry (ALB); bathymetric features; bottom waveform; features; coral reef classification; support vector machine (SVM)
摘要：Geographic information describing coral reefs plays an important role in constructing electronic chart systems and protecting the ecological environment of the ocean. To derive geographic information of coral reefs more effectively, this paper proposes a methodology to detect coral reefs by combining airborne LiDAR bathymetry (ALB) bottom waveform and bathymetric feature data. A feature vector was established by deriving bottom waveform variables (the peak amplitude, pulsewidth, area, skewness, kurtosis, and backscatter cross section) and bathymetric variables (the depth standard deviation, slope, bathymetric position index, Gaussian curvature, mean curvature, and roughness). Using a support vector machine classifier, coral reefs were detected by distinguishing two classes (coral reefs and others) on the seafloor. To evaluate the classification performance of coral reefs, the developed method was applied to Yuanzhi Island, South China Sea surveys, and verified by field data (aerial digital camera images and underwater video images). The results showed that the classification overall accuracy of coral reefs can be greatly improved from 80.59%/90.31% when ALB bottom waveform or bathymetric variables features were used separately to 93.57% when using a combination of ALB bottom waveform and bathymetric features. In addition, the kappa coefficient can also be greatly improved from approximately 0.61/0.80 to 0.87. And the new proposed method performs better compared to the current classification method using ALB data to detect coral reefs with an overall accuracy of 90.92% and Kappa of 0.81. This highlights the potential of ALB data, combining waveform data and bathymetric data, for precisely detecting coral reefs in shallow water areas.