[1] ANDERSON J T, VAN HOLLIDAY D, KLOSER R, et al. Acoustic seabed classification: current practice and future directions[J]. ICES Journal of Marine Science, 2008, 65(6): 1004-1011. [2] 唐秋华, 纪雪, 丁继胜, 等. 多波束声学底质分类研究进展与展望[J]. 海洋科学进展, 2019, 37(1): 1-10. TANG Qiuhua, JI Xue, DING Jisheng, et al. Research progress and prospect of acoustic seabed classification using multibeam echo sounder[J]. Advances in Marine Science, 2019, 37(1): 1-10. [3] BROWN C J, MITCHELL A, LIMPENNY D S, et al. Mapping seabed habitats in the Firth of Lorn off the west coast of Scotland: evaluation and comparison of habitat maps produced using the acoustic ground-discrimination system, RoxAnn, and sidescan sonar[J]. ICES Journal of Marine Science, 2005, 62(4): 790-802. [4] LARK R M, DOVE D, GREEN S L, et al. Spatial prediction of seabed sediment texture classes by cokriging from a legacy database of point observations[J]. Sedimentary Geology, 2012, 281: 35-49. [5] 金绍华, 李家彪, 吴自银, 等. 海底底质分类反向散射强度三维概率密度法[J]. 测绘学报, 2019, 48(1): 124-131. DOI: 10.11947/j.AGCS.2019.20170631. JIN Shaohua, LI Jiabiao, WU Ziyin, et al. 3D histogram of backscatter strength for seafloor substrates classification[J]. Acta Geodaetica et Cartographica Sinica, 2019, 48(1): 124-131. DOI: 10.11947/j.AGCS.2019.20170631. [6] WILKEN D, FELDENS P, WUNDERLICH T, et al. Application of 2D Fourier filtering for elimination of stripe noise in side-scan sonar mosaics[J]. Geo-Marine Letters, 2012, 32(4): 337-347. [7] FAJARYANTI R, KANG M. A preliminary study on seabed classification using a scientific echosounder[J]. Bulletin of the Korean Society of Fisheries Technology, 2019, 55(1): 39-49. [8] OJEDAG Y, GAYES P T, VAN DOLAH R F, et al. Spatially quantitative seafloor habitat mapping: example from the northern South Carolina inner continental shelf[J]. Estuarine, Coastal and Shelf Science, 2004, 59(3): 399-416. [9] 唐秋华, 刘保华, 陈永奇, 等. 结合遗传算法的LVQ神经网络在声学底质分类中的应用[J]. 地球物理学报, 2007, 50(1): 313-319. TANG Qiuhua, LIU Baohua, CHEN Yongqi, et al. Application of LVQ neural network combined with the genetic algorithm in acoustic seafloor classification[J]. Chinese Journal of Geophysics, 2007, 50(1): 313-319. [10] MARSH I, BROWN C. Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV)[J]. Applied Acoustics, 2009, 70(10): 1269-1276. [11] ZHOU Xinghua, CHEN Yongqi. Seafloor sediment classification based on multibeam sonar data[J]. Geo-Spatial Information Science, 2004, 7(4): 290-296. [12] LI Jin, TRAN M, SIWABESSY J. Selecting optimal random forest predictive models: a case study on predicting the spatial distribution of seabed hardness[J]. PLoS ONE, 2016, 11(2):1490-1508. [13] HERKVL, PETERSON A, PAEKIVI S. Applying multibeam sonar and mathematical modeling for mapping seabed substrate and biota of offshore shallows[J]. Estuarine, Coastal and Shelf Science, 2017, 192(1): 57-71. [14] TURNER J A, BABCOCK R C, HOVEY R, et al. Can single classifiers be as useful as model ensembles to produce benthic seabed substratum maps[J]. Estuarine, Coastal and Shelf Science, 2018, 204(2): 149-163. [15] JI Xue, YANG Bisheng, TANG Qiuhua. Seabed sediment classification using multibeam backscatter data based on the selecting optimal random forest model[J]. Applied Acoustics, 2020, 167: 107387. [16] 纪雪. 基于多波束数据的海底底质及地形复杂度分类研究[D]. 青岛: 国家海洋局第一海洋研究所, 2017. JI Xue. Classification of seabed sediment and terrain complexity based on multibeam data[D]. Qingdao: The First Institute of Oceanography, State Oceanic Administra-tion, 2017. [17] BERTHOLD T, LEICHTER A, ROSENHAHN B, et al. Seabed sediment classification of side-scan sonar data using convolutional neural networks[C]//Proceedings of 2017 IEEE Symposium Series on Computational Intelligence. Honolulu, HI, USA: IEEE, 2017:1-8. [18] 阳凡林,朱正任, 李家彪, 等. 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[J]. 测绘学报, 2021, 50(1): 71-84. DOI:10.11947/j.AGCS.2021.20200065. YANG Fanlin, ZHU Zhengren, LI Jiabiao, et al. Seafloor classification based on combined multibeam bathymetry and backscatter using deep convolution neural network[J]. Acta Geodaetica et Cartographica Sinica, 2021, 50(1): 71-84. DOI:10.11947/j.AGCS.2021.20200065. [19] SCHAPIRE R E. A brief introduction to boosting[C]//Proceedings of the 16th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: Morgan Kaufmann Publishers Inc., 1999: 1401-1406. [20] FREUND Y, SCHAPIRE R E. A decision-theoretic generalization of on-line learning and an application to boosting[J]. Journal of Computer and System Sciences, 1997, 55(1): 119-139. [21] VAPNIK V N. The nature of statistical learning theory[M]. New York: Springer, 1995. [22] VAPNIK V N. An overview of statistical learning theory[J]. IEEE Transactions on Neural Networks, 1999, 10(5): 988-999. [23] BURGES C J C. A tutorial on support vector machines for pattern recognition[J]. Data Mining and Knowledge Discovery, 1998, 2(2): 121-167. [24] SMOLA A J, SCHÖLKOPF B. A tutorial on support vector regression[J]. Statistics and Computing, 2004, 14(3): 199-222. [25] HOLLAND J H. Genetic algorithms[J]. Scientific American, 1992, 267(1): 66-73. [26] MICHALOPOULOU Z H, ALEXANDROU D, DE MOU-STIER C. Application of neural and statistical classifiers to the problem of seafloor characterization[J]. IEEE Journal of Oceanic Engineering, 1995, 20(3): 190-197. [27] HELLEQUIN L, BOUCHER J M, LURTON X. Processing of high-frequency multibeam echo sounder data for seafloor characterization[J]. IEEE Journal of Oceanic Engineering, 2003, 28(1): 78-89. [28] FONSECA L, BROWN C, CALDER B, et al. Angular range analysis of acoustic themes from Stanton Banks Ireland: a link between visual interpretation and multibeamechosounder angular signatures[J]. Applied Acoustics, 2009, 70(10): 1298-1304. [29] 严俊, 张红梅, 赵建虎, 等. 多波束声呐后向散射数据角度响应模型的改进算法[J]. 测绘学报, 2016, 45(11): 1301-1307. DOI: 10.11947/j.AGCS.2016.20160169. YAN Jun, ZHANG Hongmei, ZHAO Jianhu, et al. Study on improvement of multibeam backscatter angular response model[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(11): 1301-1307. DOI: 10.11947/j.AGCS.2016.20160169. [30] LECOURS V, DOLAN M F J, MICALLEF A, et al. A review of marine geomorphometry, the quantitative study of the seafloor[J]. Hydrology and Earth System Sciences, 2016, 20(8): 3207-3244. [31] 何林帮. 基于多波束和浅剖的海底浅表层沉积物分类关键问题研究[J]. 测绘学报, 2016, 45(12): 1498. DOI: 10.11947/j.AGCS.2016.20160466. HE Linbang. Research on key issues of sediment classification for seabed and sub-bottom based on multi-beam and sub-bottom profile echo intensity[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(12): 1498. DOI: 10.11947/j.AGCS.2016.20160466. [32] 胡韦伟, 汪荣贵, 方帅, 等. 基于双边滤波的Retinex图像增强算法[J]. 工程图学学报, 2010, 31(2): 104-109. HU Weiwei, WANG Ronggui, FANG Shuai, et al. Retinex algorithm for image enhancement based on bilateral filtering[J]. Journal of Engineering Graphics, 2010, 31(2): 104-109. [33] RAHMAN Z, JOBSON D J, WOODELL G A. Retinex processing for automatic image enhancement[J]. Journal of Electronic Imaging, 2004, 13(1): 100-110. [34] YAN Ke, ZHANG D. Feature selection and analysis on correlated gas sensor data with recursive feature elimination[J]. Sensors and Actuators B: Chemical, 2015, 212: 353-363. [35] SCHAPIRE R E, SINGER Y. BoosTexter: A boosting-based system for text categorization[J]. Machine Learning, 2000, 39(2-3): 135-168. [36] ZHU Ji, ZOU Hui, ROSSET S, et al. Multi-class AdaBoost[J]. Statistics and its Interface, 2009, 2(3): 349-360. |