一种顾及辐射畸变的多波束与侧扫声呐通用海底底质分类方法
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摘要
声学海底底质分类对于海底环境和生态系统的研究具有重要意义,而多波束与侧扫声呐是目前最常用于探测海底底质的声呐设备.然而,多波束与侧扫声呐都严重地受到辐射畸变的影响,导致声呐图像噪声较大且难以消除,进而造成对海底底质的误判,并且多波束与侧扫声呐通用底质分类方法目前仍然较为缺乏.为此,本文提出了一种顾及辐射畸变的多波束和侧扫声呐的通用海底底质分类方法.该方法包括了对声呐图像中辐射畸变的改正、针对两种声呐数据特点的不同角度数据归一化、分类数自适应的非监督底质分类以及形态学去除底质图像噪声的步骤,并给出了完整的多波束与侧扫声呐通用海底底质分类流程.将本文方法应用于福建沿海同水域下实测的多波束与侧扫声呐数据得到了该水域的底质分类图像.实验结果表明了同区域下的多波束与侧扫声呐数据通过本文方法得到的底质分类结果具有较高的一致性,证明了通用底质分类方法的有效性,同时通过相互验证也提高了底质分类结果的可靠性.
Acoustic seabed classification is significant for the study of the seabed environment and the ecosystem, in which multibeam and sidescan sonars are the most common sonar equipment. However, multibeam and sidescan sonars are both largely affected by radiometric distortion, which makes the noise of the sonar image too big to be eliminated and the seabed sediment misinterpreted. Moreover, the universal seabed classification methods for multibeam and sidescan sonars are still deficient. Therefore, this paper proposes a universal seabed classification method of multibeam and sidescan sonars in consideration of radiation distortion. Steps of the method include correction of radiometric distortion, normalization of both mulitbeam and sidescan sonars data, adaptive unsupervised seabed classification, and morphological image denoising. A complete universal seafloor classification procedure was also presented. The proposed method was applied to the same water area in Fujian Coast, where multibeam and sidescan sonars data were both measured and the seabed classification results of the water area were obtained. Experimental results show that the seabed classification results processed from multibeam and sidescan data in the same water area were in high consistency, which verifies the validity of the universal seabed classification method and improves the reliability of the classification results through mutual verification.
Acoustic seabed classification is significant for the study of the seabed environment and the ecosystem, in which multibeam and sidescan sonars are the most common sonar equipment. However, multibeam and sidescan sonars are both largely affected by radiometric distortion, which makes the noise of the sonar image too big to be eliminated and the seabed sediment misinterpreted. Moreover, the universal seabed classification methods for multibeam and sidescan sonars are still deficient. Therefore, this paper proposes a universal seabed classification method of multibeam and sidescan sonars in consideration of radiation distortion. Steps of the method include correction of radiometric distortion, normalization of both mulitbeam and sidescan sonars data, adaptive unsupervised seabed classification, and morphological image denoising. A complete universal seafloor classification procedure was also presented. The proposed method was applied to the same water area in Fujian Coast, where multibeam and sidescan sonars data were both measured and the seabed classification results of the water area were obtained. Experimental results show that the seabed classification results processed from multibeam and sidescan data in the same water area were in high consistency, which verifies the validity of the universal seabed classification method and improves the reliability of the classification results through mutual verification.
引文
[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. DOI:10.1093/icesjms/fsn061
[2]PRESTON J. Automated acoustic seabed classification of multibeam images of Stanton Banks[J]. Applied Acoustics, 2009, 70(10): 1277. DOI:10.1016/j.apacoust.2008.07.011
[3]INNANGI S, BARRA M, DI MARTINO G, et al. Reson SeaBat 8125 backscatter data as a tool for seabed characterization (Central Mediterranean, Southern Italy): Results from different processing approaches[J]. Applied Acoustics, 2015(87): 109. DOI:10.1016/j.apacoust.2014.06.014
[4]BUSCOMBE D, GRAMS P E, SMITH S M C. Automated riverbed sediment classification using low-cost sidescan sonar[J]. Journal of Hydraulic Engineering, 2016, 142(2): 06015019. DOI:10.1061/(ASCE)HY.1943-7900.0001079
[5]PERGENT G, MONNIER B, CLABAUT P, et al. Innovative method for optimizing side-scan sonar mapping: The blind band unveiled[J]. Estuarine, Coastal and Shelf Science, 2017, 194: 77. DOI:10.1016/j.ecss.2017.05.016
[6]VENTERIS E R, MAY C J. Cost-effective mapping of benthic habitats in inland reservoirs through split-beam sonar, indicator Kriging, and historical geologic data[J]. PLOS ONE, 2014, 9(4): e95940. DOI:10.1371/journal.pone.0095940
[7]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, 11: 1301. DOI:10.11947/j.AGCS.2016.20160169YAN 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. DOI:10.11947/j.AGCS.2016.20160169
[8]ZHAO J, YAN J, ZHANG H, et al. Two self-adaptive methods of improving multibeam backscatter image quality by removing angular response effect[J]. Journal of Marine Science and Technology, 2017, 22(2): 288. DOI:10.1007/s00773-016-0410-1
[9]FONSECA L, BROWN C, CALDER B, et al. Angular range analysis of acoustic themes from Stanton Banks Ireland: A link between visual interpretation and multibeam echosounder angular signatures[J]. Applied Acoustics, 2009, 70(10): 1298. DOI:10.1016/j.apacoust.2008.09.008
[10]TANG Q, LEI N, LI J, et al. Seabed mixed sediment classification with multi-beam echo sounder backscatter data in Jiaozhou Bay[J]. Marine Georesources & Geotechnology, 2015, 33(1): 1. DOI:10.1080/1064119X.2013.764557
[11]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. DOI:10.1016/j.apacoust.2008.07.012
[12]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.2016.20160466HE 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
[13]唐秋华, 周兴华, 丁继胜, 等. 学习向量量化神经网络在多波束底质分类中的应用研究[J]. 武汉大学学报·信息科学版, 2006, 31(3): 229TANG Qiuhua, ZHOU Xinghua, DING Jisheng, et al. Seafloor classification from multibeam backscatter data using learning vector quantization neural network[J]. Geomatics and Information Science of Wuhan University, 2006, 31(3): 229
[14]BERTHOLD T, LEICHTER A, ROSENHAHN B, et al. Seabed sediment classification of side-scan sonar data using convolutional neural networks[C]//2017 IEEE Symposium Series on Computational Intelligence (SSCI). Honolulu, HI, USA. IEEE, 2017: 1. DOI:10.1109/SSCI.2017.8285220
[15]郭军, 马金凤, 王爱学. 基于SVM算法和GLCM的侧扫声纳影像分类研究[J]. 测绘与空间地理信息, 2015, 38(3): 60GUO Jun, MA Jinfeng, WANG Aixue. Study of side scan sonar image classification based on SVM and gray level co-occurrence matrix[J]. Geomatics & Spatial Information Technology, 2015, 38(3): 60
[16]LE BAS T P, HUVENNE V A I. Acquisition and processing of backscatter data for habitat mapping-Comparison of multibeam and sidescan systems[J]. Applied Acoustics, 2009, 70(10): 1248. DOI:10.1016/j.apacoust.2008.07.010
[17]WAITE A D. Sonar for practising engineers, 3rd Edition[M]. 3rd ed. Chichester: Wiley, 2002: 20
[18]ZHAO J, WANG X, ZHANG H, et al. A comprehensive bottom-tracking method for sidescan sonar image influenced by complicated measuring environment[J]. IEEE Journal of Oceanic Engineering, 2017, 42(3): 619. DOI:10.1109/JOE.2016.2602642
[19]CAPUS C, RUIZ I T, PETILLOT Y. Compensation for changing beam pattern and residual TVG effects with sonar altitude variation for sidescan mosaicing and classification[C]//7th. Eur. Conf. Underwater Acoustics, Delft, The Netherlands. 2004: 1
[20]FONSECA L, CALDER B. Clustering acoustic backscatter in the angular response space[C]//Proceedings of the US hydrographic conference. Norfolk, VA, 2007: 1
[21]ARTHUR D, VASSILVITSKII S. К-means++: the advantages of careful seeding[C]//Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. New Orleans, Louisiana: Society for Industrial and Applied Mathematics. 2007: 1027
[22]ZHAO J, MENG J, ZHANG H, et al. Comprehensive detection of gas plumes from multibeam water column images with minimisation of noise interferences[J]. Sensors, 2017, 17(12): 2755. DOI:10.3390/s17122755
[2]PRESTON J. Automated acoustic seabed classification of multibeam images of Stanton Banks[J]. Applied Acoustics, 2009, 70(10): 1277. DOI:10.1016/j.apacoust.2008.07.011
[3]INNANGI S, BARRA M, DI MARTINO G, et al. Reson SeaBat 8125 backscatter data as a tool for seabed characterization (Central Mediterranean, Southern Italy): Results from different processing approaches[J]. Applied Acoustics, 2015(87): 109. DOI:10.1016/j.apacoust.2014.06.014
[4]BUSCOMBE D, GRAMS P E, SMITH S M C. Automated riverbed sediment classification using low-cost sidescan sonar[J]. Journal of Hydraulic Engineering, 2016, 142(2): 06015019. DOI:10.1061/(ASCE)HY.1943-7900.0001079
[5]PERGENT G, MONNIER B, CLABAUT P, et al. Innovative method for optimizing side-scan sonar mapping: The blind band unveiled[J]. Estuarine, Coastal and Shelf Science, 2017, 194: 77. DOI:10.1016/j.ecss.2017.05.016
[6]VENTERIS E R, MAY C J. Cost-effective mapping of benthic habitats in inland reservoirs through split-beam sonar, indicator Kriging, and historical geologic data[J]. PLOS ONE, 2014, 9(4): e95940. DOI:10.1371/journal.pone.0095940
[7]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, 11: 1301. DOI:10.11947/j.AGCS.2016.20160169YAN 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. DOI:10.11947/j.AGCS.2016.20160169
[8]ZHAO J, YAN J, ZHANG H, et al. Two self-adaptive methods of improving multibeam backscatter image quality by removing angular response effect[J]. Journal of Marine Science and Technology, 2017, 22(2): 288. DOI:10.1007/s00773-016-0410-1
[9]FONSECA L, BROWN C, CALDER B, et al. Angular range analysis of acoustic themes from Stanton Banks Ireland: A link between visual interpretation and multibeam echosounder angular signatures[J]. Applied Acoustics, 2009, 70(10): 1298. DOI:10.1016/j.apacoust.2008.09.008
[10]TANG Q, LEI N, LI J, et al. Seabed mixed sediment classification with multi-beam echo sounder backscatter data in Jiaozhou Bay[J]. Marine Georesources & Geotechnology, 2015, 33(1): 1. DOI:10.1080/1064119X.2013.764557
[11]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. DOI:10.1016/j.apacoust.2008.07.012
[12]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.2016.20160466HE 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
[13]唐秋华, 周兴华, 丁继胜, 等. 学习向量量化神经网络在多波束底质分类中的应用研究[J]. 武汉大学学报·信息科学版, 2006, 31(3): 229TANG Qiuhua, ZHOU Xinghua, DING Jisheng, et al. Seafloor classification from multibeam backscatter data using learning vector quantization neural network[J]. Geomatics and Information Science of Wuhan University, 2006, 31(3): 229
[14]BERTHOLD T, LEICHTER A, ROSENHAHN B, et al. Seabed sediment classification of side-scan sonar data using convolutional neural networks[C]//2017 IEEE Symposium Series on Computational Intelligence (SSCI). Honolulu, HI, USA. IEEE, 2017: 1. DOI:10.1109/SSCI.2017.8285220
[15]郭军, 马金凤, 王爱学. 基于SVM算法和GLCM的侧扫声纳影像分类研究[J]. 测绘与空间地理信息, 2015, 38(3): 60GUO Jun, MA Jinfeng, WANG Aixue. Study of side scan sonar image classification based on SVM and gray level co-occurrence matrix[J]. Geomatics & Spatial Information Technology, 2015, 38(3): 60
[16]LE BAS T P, HUVENNE V A I. Acquisition and processing of backscatter data for habitat mapping-Comparison of multibeam and sidescan systems[J]. Applied Acoustics, 2009, 70(10): 1248. DOI:10.1016/j.apacoust.2008.07.010
[17]WAITE A D. Sonar for practising engineers, 3rd Edition[M]. 3rd ed. Chichester: Wiley, 2002: 20
[18]ZHAO J, WANG X, ZHANG H, et al. A comprehensive bottom-tracking method for sidescan sonar image influenced by complicated measuring environment[J]. IEEE Journal of Oceanic Engineering, 2017, 42(3): 619. DOI:10.1109/JOE.2016.2602642
[19]CAPUS C, RUIZ I T, PETILLOT Y. Compensation for changing beam pattern and residual TVG effects with sonar altitude variation for sidescan mosaicing and classification[C]//7th. Eur. Conf. Underwater Acoustics, Delft, The Netherlands. 2004: 1
[20]FONSECA L, CALDER B. Clustering acoustic backscatter in the angular response space[C]//Proceedings of the US hydrographic conference. Norfolk, VA, 2007: 1
[21]ARTHUR D, VASSILVITSKII S. К-means++: the advantages of careful seeding[C]//Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. New Orleans, Louisiana: Society for Industrial and Applied Mathematics. 2007: 1027
[22]ZHAO J, MENG J, ZHANG H, et al. Comprehensive detection of gas plumes from multibeam water column images with minimisation of noise interferences[J]. Sensors, 2017, 17(12): 2755. DOI:10.3390/s17122755
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