[1]赵玉新,赵廷.海底声呐图像智能底质分类技术研究综述[J].智能系统学报,2020,15(3):587-600.[doi:10.11992/tis.202004026]
 ZHAO Yuxin,ZHAO Ting.Survey of the intelligent seabed sediment classification technology based on sonar images[J].CAAI Transactions on Intelligent Systems,2020,15(3):587-600.[doi:10.11992/tis.202004026]
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海底声呐图像智能底质分类技术研究综述

参考文献/References:
[1] 金翔龙. 海洋地球物理研究与海底探测声学技术的发展[J]. 地球物理学进展, 2007, 22(4): 1243-1249
JIN Xianglong. The development of research in marine geophysics and acoustic technology for submarine exploration[J]. Progress in geophysics, 2007, 22(4): 1243-1249
[2] 路晓磊, 张丽婷, 王芳, 等. 海底声学探测技术装备综述[J]. 海洋开发与管理, 2018, 35(6): 91-94
LU Xiaolei, ZHANG Liting, WANG Fang, et al. Summary of submarine acoustic detection technology and equipment[J]. Ocean development and management, 2018, 35(6): 91-94
[3] 庞新明, 赵明辉, 刘思青, 等. 复杂地质结构OBS地震剖面震相识别方法[J]. 地球物理学报, 2019, 62(9): 3482-3491
PANG Xinming, ZHAO Minghui, LIU Siqing, et al. Seismic phases identification in OBS seismic record sections through the complex geological structure[J]. Chinese journal of geophysics, 2019, 62(9): 3482-3491
[4] BROWN C J, BEAUDOIN J, BRISSETTE M, et al. Multispectral multibeam echo sounder backscatter as a tool for improved seafloor characterization[J]. Geosciences, 2019, 9(3): 126.
[5] SHIH C C, HORNG M F, TSENG Y R, et al. An adaptive bottom tracking algorithm for side-scan sonar seabed mapping[C]//Proceedings of 2019 IEEE Underwater Technology. Taiwan, China, 2019: 1-7.
[6] TAN Cheng, ZHANG Xuebo, YANG Peixuan, et al. A novel sub-bottom profiler and signal processor[J]. Sensors, 2019, 19(22): 5052.
[7] 张炳生. 水下声学高精度定位算法的研究[D]. 西安: 长安大学, 2019.
ZHANG Bingsheng. Research on high precision positioning algorithm for underwater acoustics[D]. Xi’an: Chang’an University, 2019.
[8] 耿雪樵, 徐行, 刘方兰, 等. 我国海底取样设备的现状与发展趋势[J]. 地质装备, 2009, 10(4): 11-16
GENG Xueqiao, XU Xing, LIU Fanglan, et al. The current status and development trends of marine sampling equipment[J]. Equipment for geotechnical engineering, 2009, 10(4): 11-16
[9] 补家武, 鄢泰宁, 昌志军. 海底取样技术发展现状及工作原理概述——海底取样技术专题之一[J]. 探矿工程(岩土钻掘工程), 2001(2): 44-48
BU Jiawu, YAN Taining, CHANG Zhijun. Introduction to the status quo and operating principle of seabed samplers——Part Ⅰ of the subject on seabed sampling[J]. Exploration engineering (Drilling & tunneling), 2001(2): 44-48
[10] 侯正瑜, 郭常升, 王景强, 等. 一种新型海底沉积物声学原位测量系统的研制及应用[J]. 地球物理学报, 2015, 58(6): 1976-1984
HOU Zhengyu, GUO Changsheng, WANG Jingqiang, et al. Development and application of a new type in-situ acoustic measurement system of seafloor sediments[J]. Chinese journal of geophysics, 2015, 58(6): 1976-1984
[11] 陶春辉, 王东, 金翔龙. 海底沉积物声学特性和原位测试技术[M]. 北京: 海洋出版社, 2006.
[12] 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
[13] MANIK H M, NISHIMORI Y, NISHIYAMA Y, et al. Developing signal processing of echo sounder for measuring acoustic backscatter[J]. IOP conference series: earth and environmental science, 2020, 429: 012034.
[14] GAIDA T C, MOHAMMADLOO T H, SNELLEN M, et al. Mapping the seabed and shallow subsurface with multi-frequency multibeam echosounders[J]. Remote sensing, 2020, 12(1): 52.
[15] YAN Jun, MENG Junxia, ZHAO Jianhu. Real-time bottom tracking using side scan sonar data through one-dimensional convolutional neural networks[J]. Remote sensing, 2020, 12(1): 37.
[16] 周杨锐, 吴秋云, 董明明, 等. 深水工程勘察技术研究现状与展望[J]. 中国海上油气, 2017, 29(6): 158-166
ZHOU Yangrui, WU Qiuyun, DONG Mingming, et al. Current status and development outlook of deep water geotechnical investigation and survey technology[J]. China offshore oil and gas, 2017, 29(6): 158-166
[17] LI W N, SMITH D T. Identification of sea-bottom sediments by a ship underway[J]. Geophysical prospecting, 1966, 14(1): 45-47.
[18] SMITH D T, LI W N. Echo-sounding and sea-floor sediments[J]. Marine geology, 1966, 4(5): 353-364.
[19] HAMILTON E L. Geoacoustic models of the sea floor[M]//HAMPTON L. Physics of Sound in Marine Sediments. Boston: Springer, 1974: 181-221.
[20] HAMILTON E L. Prediction of deep-sea sediment properties: state-of-the-art[M]//INDERBITZEN A L. Deep-Sea Sediments: Physical and Mechanical Properties. Boston: Springer, 1974: 1-43.
[21] 郭永刚. 海底声参数反演研究与应用[D]. 青岛: 中国海洋大学, 2004.
GUO Yonggang. Research and application of seafloor parameters inversion[D]. Qingdao: Ocean University of China, 2004.
[22] 蒯多杰, 王长红, 冯雷, 等. 海底回波空间相关特性研究[J]. 声学学报, 2009, 34(5): 385-395
KUAI Duojie, WANG Changhong, FENG Lei, et al. Spatial correlation analysis of sea-bottom backscattering[J]. Acta acustica, 2009, 34(5): 385-395
[23] 杨词银, 许枫, 魏建江. 基于邻域灰阶共生矩阵的海底沉积物分类[J]. 哈尔滨工程大学学报, 2005, 26(5): 561-564
YANG Ciyin, XU Feng, WEI Jianjiang. Seafloor sediment classification using a neighborhood gray level co-occurrence matrix[J]. Journal of Harbin Engineering University, 2005, 26(5): 561-564
[24] 罗忠辉, 曾新红, 杜灿谊, 等. 海底沉积物声学特征定量分析及其智能分类研究[J]. 海洋技术学报, 2015, 34(1): 43-49
LUO Zhonghui, ZENG Xinhong, DU Canyi, et al. Quantitative analysis and intelligent classification for the acoustic characteristics of seafloor sediments[J]. Journal of ocean technology, 2015, 34(1): 43-49
[25] BIOT M A. Theory of propagation of elastic waves in a fluid-saturated porous solid. II. Higher frequency range[J]. The journal of the acoustical society of America, 1956, 28(2): 179-191.
[26] 乔文孝, 吴文虬, 王耀俊. 多孔介质声学研究进展[J]. 物理学进展, 1996, 16(3/4): 386-395
QIAO Wenxiao, WU Wenqiu, WANG Yaojun. Major progress in porous medium acoustics[J]. Progress in physics, 1996, 16(3/4): 386-395
[27] HAMILTON E L. Geoacoustic modeling of the sea floor[J]. The journal of the acoustical society of America, 1980, 68(5): 1313-1340.
[28] HAMILTON E L, BACHMAN R T. Sound velocity and related properties of marine sediments[J]. The journal of the acoustical society of America, 1982, 72(6): 1891-1904.
[29] HAMILTON L J. Acoustic seabed classification systems[R]. Defence science and technology organisation Victoria: DSTO Aeronautical and Maritime Research Laboratory, 2001.
[30] JACKSON D R, BAIRD A M, CRISP J J, et al. High-frequency bottom backscatter measurements in shallow water[J]. The journal of the acoustical society of America, 1986, 80(4): 1188-1199.
[31] 徐超. 海底散射模型与多波束混响信号统计特性研究[D]. 哈尔滨: 哈尔滨工程大学, 2009.
XU Chao. Research of seafloor scattering model and statistical characteristic of multibeam reverberation signal[D]. Harbin: Harbin Engineering University, 2009.
[32] 杨玉春. 基于Jackson模型的高频海底散射研究[J]. 声学与电子工程, 2015(4): 33-36
YANG Yuchuan. Research of high-frequency seabed scattering based on Jackson model[J]. Acoustics and electronics engineering, 2015(4): 33-36
[33] JIANG Yongmin, CHAPMAN N R, GERSTOFT P. Estimation of geoacoustic properties of marine sediment using a hybrid differential evolution inversion method[J]. IEEE journal of oceanic engineering, 2010, 35(1): 59-69.
[34] SCHOCK S G. A method for estimating the physical and acoustic properties of the sea bed using chirp sonar data[J]. IEEE journal of oceanic engineering, 2004, 29(4): 1200-1217.
[35] SIEMES K, SNELLEN M, AMIRI-SIMKOOEI A R, et al. Predicting spatial variability of sediment properties from hydrographic data for geoacoustic inversion[J]. IEEE journal of oceanic engineering, 2010, 35(4): 766-778.
[36] 周天, 李海森, 朱建军, 等. 利用多角度海底反向散射信号进行地声参数估计[J]. 物理学报, 2014, 63(8): 084302
ZHOU Tian, LI Haisen, ZHU Jianjun, et al. A geoacoustic estimation scheme based on bottom backscatter signals from multiple angles[J]. Acta physica sinica, 2014, 63(8): 084302
[37] 金国亮, 张仁和. 由浅海混响反演海底反射和散射系数[J]. 声学学报, 1996, 21(S1): 565-572
JIN Guoliang, ZHANG Renhe. Inversion of shallow water reverberation for bottom reflection and scattering coefficients[J]. Acta Acustica, 1996, 21(S1): 565-572
[38] BENNETT R H, LI H, RICHARDSON M D, et al. Geoacoustic and geotechnical characterization of surficial marine sediments by in situ probe and remote sensing techniques[M]//GEYER R A. CRC Handbook of Geophysical Exploration at Sea. 2nd ed. Boca Raton: CRC Press, 1992: 295-350.
[39] CLARKE J H. Toward remote seafloor classification using the angular response of acoustic backscattering: a case study from multiple overlapping GLORIA data[J]. IEEE journal of oceanic engineering, 1994, 19(1): 112-127.
[40] HANIOTIS S, CERVENKA P, NEGREIRA C, et al. Seafloor segmentation using angular backscatter responses obtained at sea with a forward-looking sonar system[J]. Applied acoustics, 2015, 89: 306-319.
[41] PACE N G, DYER C M. Machine classification of sedimentary sea bottoms[J]. IEEE transactions on geoscience electronics, 1979, 17(3): 52-56.
[42] REUT Z, PACE N, HEATON M J P. Computer classification of sea beds by sonar[J]. Nature, 1985, 314(6010): 426-428.
[43] HUSEBY R B, MILVANG O, SOLBERG A S, et al. Seabed classification from multibeam echosounder data using statistical methods[C]//Proceedings of OCEANS1993. Victoria, Canada, 1993: III/229-III/233.
[44] ALEXANDROU D, PANTZARTZIS D. Seafloor classification with neural networks[C]//Proceedings of OCEANS1990. Washington, USA, 1990: 18-23.
[45] GAO Wei. Sediment classification based on least-squares support vector machine and phase-plane analysis[C]//Proceedings of 2009 Fifth International Conference on Natural Computation. Tianjin, China, 2009: 560-564.
[46] DIESING M, GREEN S L, STEPHENS D, et al. Mapping seabed sediments: comparison of manual, geostatistical, object-based image analysis and machine learning approaches[J]. Continental shelf research, 2014, 84: 107-119.
[47] 郑红霞,张训华. 海底底质分类方法综述[C]//中国地球物理学会第二十九届年会. 昆明, 中国, 2013: 5.
ZHENG Hongxia, ZHANG Xxunhua.Review of seabed sediment classification[C]//The 29th annual conference of the Chinese geophysical society. Kunming, China, 2013: 5.
[48] 孟金生, 关定华. 正入射声脉冲法估测海底表层沉积物衰减系数[J]. 海洋学报, 1984, 6(6): 867-873
MENG Jinsheng, GUAN Dinghua. A normal incidence-based acoustic pulse method for the estimation of seafloor sediment attenuation coefficients[J]. Acta oceanologica sinica (Chinese version), 1984, 6(6): 867-873
[49] 周志愚, 孟金生. 由爆炸声反射波遥测海底表面稀软薄层的某些参数[J]. 热带海洋, 1985, 4(2): 38-43
ZHOU Zhiyu, MENG Jinsheng. Telemetering of some parameters of the soft and thin surface layer of the sea floor by using reflected explosive waves[J]. Tropic oceanology, 1985, 4(2): 38-43
[50] 孟金生, 关定华. 海底沉积物的声学方法分类[J]. 声学学报, 1982, 7(6): 337-343
MENG Jinsheng, GUAN Dinghua. Acoustical classification of sea floor sediments[J]. Acta acustica, 1982, 7(6): 337-343
[51] PRAGER B T, CAUGHEY D A, POECKERT R H. Bottom classification: operational results from QTC view[C]//Proceedings of OCEANS1995. MTS/IEEE. San Diego, USA, 1995: 1827-1835.
[52] 万宏俊. 基于声纳回波波形特征的海底底质类型分类方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2019.
WAN Hongjun. Research on classification method of submarine substrate type based on characteristics of sonar wave[D]. Harbin: Harbin Engineering University, 2019.
[53] 刘光宇. 基于声纳图像的目标识别技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2009.
LIU Guangyu. Target recognition technology research based on sonar image[D]. Harbin: Harbin Engineering University, 2009.
[54] 罗伟东, 郭军. 基于多波束背向散射数据的海底底质分类[J]. 海洋地质前沿, 2017, 33(8): 57-62
LUO Weidong, GUO Jun. Seabed sediment classification based on multibeam backscatter data[J]. Marine geology frontiers, 2017, 33(8): 57-62
[55] FAKIRIS E, PAPATHEODOROU G, GERAGA M, et al. An automatic target detection algorithm for swath sonar backscatter imagery, using image texture and independent component analysis[J]. Remote sensing, 2016, 8(5): 373.
[56] ABBAS Z, REHMAN M U, NAJAM S, et al. An efficient gray-level co-occurrence matrix (GLCM) based approach towards classification of skin lesion[C]//Proceedings of 2019 Amity International Conference on Artificial Intelligence. Dubai, United Arab Emirates, 2019: 317-320.
[57] 杨词银, 许枫. 基于分形维的底质分类[J]. 海洋测绘, 2004, 24(6): 5-8
YANG Ciyin, XU Feng. Seabed material classification based on fractal dimension[J]. Hydrographic surveying and charting, 2004, 24(6): 5-8
[58] NAYAK S R, MISHRA J, PALAI G. Analysing roughness of surface through fractal dimension: a review[J]. Image and vision computing, 2019, 89: 21-34.
[59] PACE N, GAO H. Swathe seabed classification[J]. IEEE journal of oceanic engineering, 1988, 13(2): 83-90.
[60] 杨蕊. 基于侧扫声呐图像的底质特征提取及分类技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
YANG Rui. Research on seabed sediment feature extraction and classification based on side scan sonar image[D]. Harbin: Harbin Engineering University, 2018.
[61] HATANAKA K, WADA M. An algorithm based on the wavelet transform for the classification of seabed textures[C]//Proceedings of OCEANS 2010 MTS/IEEE. Seattle, USA, 2010: 1-6.
[62] 石丹, 李庆武, 范新南, 等. 曲波变换域侧扫声呐图像海底底质分类[J]. 应用科学学报, 2009, 27(5): 498-501
SHI Dan, LI Qingwu, FAN Xinnan, et al. Seafloor sediments classification of side-scan sonar imagery in fast discrete curvelet transform domain[J]. Journal of applied sciences, 2009, 27(5): 498-501
[63] 李庆武, 石丹, 霍冠英. 基于Contourlet变换的海底声呐图像特征提取与分类[J]. 海洋学报, 2011, 33(5): 163-168
LI Qingwu, SHI Dan, HUO Guanying. Feature extraction and classification of seabed sonar images based on contourlet transform[J]. Acta oceanologica sinica, 2011, 33(5): 163-168
[64] 付楠. 基于声呐图像特征的海底底质类型分类方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2019.
FU Nan. Research on classification method of submarine substrate type based on characteristics of sonar image[D]. Harbin: Harbin Engineering University, 2019.
[65] ZHAO Ting, LAZENDI? S, ZHAO Yuxin, et al. Classification of Multibeam Sonar Image Using the Weyl Transform[C]//Proceedings of International Conference on Image Processing and Communications. Bydgoszcz, Poland, 2020: 206-213.
[66] PRESTON J. Automated acoustic seabed classification of multibeam images of Stanton Banks[J]. Applied acoustics, 2009, 70(10): 1277-1287.
[67] KOOP L, AMIRI-SIMKOOEI A, VAN DER REIJDEN K, et al. Seafloor Classification in a Sand Wave Environment on the Dutch Continental Shelf Using Multibeam Echosounder Backscatter Data[J]. Geosciences, 2019, 9(3): 142.
[68] 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: First Institute of Oceanography, Ministry of Natural Resources of China, 2017.
[69] PRESTON J M, CHRISTNEY A C, BLOOMER S F, et al. Seabed classification of multibeam sonar images[C]//Proceedings of MTS/IEEE Oceans 2001. Honolulu, USA, 2001: 2616-2623.
[70] 徐超. 多波束测深声呐海底底质分类技术研究[D]. 哈尔滨: 哈尔滨工程大学, 2014.
XU Chao. Study on seabed classification technology based on multibeam bathymetry sonar[D]. Harbin: Harbin Engineering University, 2014.
[71] ATALLAH L, SMITH P J P. Automatic seabed classification by the analysis of sidescan sonar and bathymetric imagery[J]. IEE proceedings-radar, sonar and navigation, 2004, 151(5): 327-336.
[72] JAVIDAN R, MASNADI-SHIRAZI M A, AZIMIFAR Z. Contourlet-based acoustic seabed ground discrimination system[C]//Proceedings of the 3rd International Conference on Information and Communication Technologies: From Theory to Applications. Damascus, Syria, 2008: 1-6.
[73] SOMVANSHI M, CHAVAN P, TAMBADE S, et al. A review of machine learning techniques using decision tree and support vector machine[C]//Proceedings of 2016 International Conference on Computing Communication Control and automation. Pune, India, 2016: 1-7.
[74] 徐超, 李海森, 王川, 等. 基于合成核SVM的多波束海底声图像底质分类研究[J]. 地球物理学进展, 2014, 29(5): 2437-2442
XU Chao, LI Haisen, WANG Chuan, et al. Seabed classification of multibeam seabed acoustic image based on composite kernel SVM[J]. Progress in geophysics, 2014, 29(5): 2437-2442
[75] LI Jin, HEAP A D, POTTER A, et al. Can we improve the spatial predictions of seabed sediments? A case study of spatial interpolation of mud content across the southwest Australian margin[J]. Continental shelf research, 2011, 31(13): 1365-1376.
[76] HASAN R C, IERODIACONOU D, MONK J. Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar[J]. Remote sensing, 2012, 4(11): 3427-3443.
[77] ISLAMSU, ABBAS A W, AHMAD A, et al. Parameter investigation of artificial neural network and support vector machine for image classification[C]//Proceedings of the 14th International Bhurban Conference on Applied Sciences and Technology. Islamabad, Pakistan, 2017: 795-798.
[78] 焦李成, 杨淑媛, 刘芳, 等. 神经网络七十年: 回顾与展望[J]. 计算机学报, 2016, 39(8): 1697-1716
JIAO Licheng, YANG Shuyuan, LIU Fang, et al. Seventy years beyond neural networks: retrospect and prospect[J]. Chinese journal of computers, 2016, 39(8): 1697-1716
[79] 阳凡林, 刘经南, 赵建虎, 等. 基于遗传算法的BP网络实现海底底质分类[J]. 测绘科学, 2006, 31(2): 111-114
YANG Fanlin, LIU Jingnan, ZHAO Jianhu, et al. Seabed texture classification using BP neural network based on GA[J]. Science of surveying and mapping, 2006, 31(2): 111-114
[80] 董骐瑞. k-均值聚类算法的改进与实现[D]. 长春: 吉林大学, 2015.
DONG Qirui. Improvements and implementation of k-means clustering algorithm[D]. Changchun: Jilin University, 2015.
[81] 吕良, 金绍华, 边刚, 等. K-均值聚类算法在多波束底质分类中的应用[J]. 海洋测绘, 2018, 38(3): 64-68
LV Liang, JIN Shaohua, BIAN Gang, et al. The application of K-means clustering analysis algorithm in multibeam seafloor classification[J]. Hydrographic surveying and charting, 2018, 38(3): 64-68
[82] 王晓燕. K-均值算法与自组织神经网络算法的改进研究及应用[D]. 太原: 中北大学, 2017.
WANG Xiaoyan. Research on improved K-means and self-organizing map neural networks and their applications[D]. Taiyuan: North University of China, 2017.
[83] 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.
[84] YIN T, THAN K M M, TUN W. Face recognition system using self-organizing feature map and appearance-based approach[J]. International journal of trend in scientific research and development, 2019, 3(5): 1598-1603.
[85] 唐秋华, 刘保华, 陈永奇, 等. 基于自组织神经网络的声学底质分类研究[J]. 声学技术, 2007, 26(3): 380-384
TANG Qiuhua, LIU Baohua, LIU Yongqi, et al. Acoustic seafloor classification using self-organizing map neural network[J]. Technical acoustics, 2007, 26(3): 380-384
[86] 郭军, 马金凤. 基于K-L变换的自组织竞争神经网络在海底底质分类中的应用[J]. 测绘工程, 2013, 22(1): 51-54
GUO Jun, MA Jinfeng. Self-organization competition neural network based on K-L transform in seafloor classification[J]. Engineering of surveying and mapping, 2013, 22(1): 51-54
[87] ZHAO Jianhu, ZHANG Hongmei. Seabed classification based on SOFM neural network[C]//2008 International Conference on Computer Science and Software Engineering. Wuhan, China, 2008: 902-905.
[88] 焦佳. 基于深度学习的水下声呐图像分类方法研究[D]. 哈尔滨: 哈尔滨工程大学, 2018.
JIAO Jia. Research on underwater sonar image classification method based on deep learning[D]. Harbin: Harbin Engineering University, 2018.
[89] WILLIAMS D P. On the use of tiny convolutional neural networks for human-expert-level classification performance in sonar imagery[J/OL]. IEEE journal of oceanic engineering (2020-02-05). https://ieeexplore.ieee.org/document/8984248.
[90] 刘韦伯. 基于深度学习的水下目标图像识别方法研究[D]. 成都: 电子科技大学, 2019.
LIU Weibo. Research on deep learning-based underwater target image recognition method[D]. Chengdu: University of Electronic Science and Technology of China, 2019.

备注/Memo

收稿日期:2020-04-24。
基金项目:国家重点基础研究发展计划(613317);国家自然科学基金面上项目(41676088)
作者简介:赵玉新,教授,博士生导师,工信部高技术船舶通信导航与智能系统专业组秘书长,中国航海学会理事,中国运筹学会决策科学分会常务理事,IET(英国工程技术学会)Fellow,IEEE高级会员,主要研究方向为水下导航技术及应用、业务化海洋学、智能航海技术。主持国防973课题、国家重大专项课题、国家自然科学基金等多个科研项目。发表学术论文100余篇。出版学术著作4部;赵廷,博士研究生,主要研究方向为海底探测、海洋遥感、图像处理、机器学习
通讯作者:赵廷.E-mail:zhaoting@hrbeu.edu.cn

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