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博碩士論文 etd-0609117-235215 詳細資訊
Title page for etd-0609117-235215
論文名稱
Title
空載測深光達資料作海底混合法分類之探討
A Hybrid Seabed Classification Method Using Airborne Laser Bathymetric Data
系所名稱
Department
畢業學年期
Year, semester
語文別
Language
學位類別
Degree
頁數
Number of pages
95
研究生
Author
指導教授
Advisor
召集委員
Convenor
口試委員
Advisory Committee
口試日期
Date of Exam
2017-05-22
繳交日期
Date of Submission
2017-07-18
關鍵字
Keywords
灰度共現矩陣、支持向量機、K平均分群演算法、測深光達資料、混合分類法
gray co-occurrence matrices (GLCM), Support Vector Machine (SVM), K-means, Hybrid method, bathymetric LiDAR data
統計
Statistics
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The thesis/dissertation has been browsed 5737 times, has been downloaded 19 times.
中文摘要
近年來,空載測深光達已逐漸應用在海床分類與近岸海圖方面有增多之趨勢。在本論文研究中,推薦了一種將K平均分群演算法(K-means)結合支持向量機(SVM)之混合分類法(簡稱作KSVM),是一種將光達水深當作原始資料來源,以二階的灰度共現矩陣(GLCM)作為紋理分析所導出的分類方法。首先,將計算之GLCM資料以K-means分成不同之群聚。接著,在應用SVM分類法將前面群聚部分以訓練樣本方式來融合。最後,以整體精度與Kappa指標值來評估混合分類演算法的好壞,並與單純以SVM分類方法作比較。實驗結果本論文所建議之混合式分類(KSVM)改善了單純之SVM方法提升24%精度,Kappa指標值也增加了0.31,在精度和目視檢查方面顯示了KSVM方法可提供了保證之結果。本篇所建議的分類方法應用之優點,在於應用無監督分類的K-means作為無法直接看見的海床沉積物類型提供先驗資訊,此方法極為有效,特別是當僅擁有水深資料來源之資訊時,或是當地當時缺乏光達強度/波形能做區分的屬性之時機。
Abstract
In recent years, Airborne Bathymetric Light Detection and Ranging (LiDAR) has been applied intensively to map coastal depth as well as for seabed classification. In this study, we proposed a hybrid K-means and Support Vector Machine (KSVM) algorithm based on depth-derived from bathymetric LiDAR, and texture analysis by second derivatives of gray-level co-occurrence matrices (GLCM). First, the calculated GLCM data set was used to sort K-means into various clusters. Second, training samples were selected on merged clusters before applying SVM classification. Finally, we evaluated the proposed hybrid algorithm in overall accuracy and the Kappa index. Compared to pure SVM, the proposed hybrid KSVM improved the overall accuracy by 24%, and the Kappa index by 0.31. The results showed that the proposed KSVM method provided promising results, in terms of accuracy and visual inspection. The benefits of the proposed classification method applied unsupervised classification of K-means as prior information for unseen seabed sediment types. This method was useful, particularly when only depth-derived information was available, or where the intensity/waveform had poor discrimination properties.
目次 Table of Contents
論文審定書 i
致謝 iii
摘要 iv
Abstract v
目錄 vi
圖次 x
表次 xii
第一章 緒論 1
1.1 前言 1
1.2 研究動機與背景 2
1.3 研究構想與流程 6
第二章 文獻回顧 9
2.1 資料來源 10
2.1.1聲學法(Acoustics) 10
2.1.1.1單音束聲納 10
2.1.1.2多音束聲納 12
2.1.1.3側掃聲納 16
2.1.2光學法(Optics) 18
2.1.2.1衛星影像 18
2.1.2.2空載測深光達 18
2.2紋理分析與統計 20
2.3影像分類技術 22
2.3.1監督式分類 23
2.3.1.1最大似然分類法 24
2.3.1.2支持向量機 24
2.3.1.3隨機決策森林 25
2.3.2非監督式分類 25
2.3.2.1 K平均分群演算法 26
第三章 研究方法 27
3.1第一階段:資料建立 28
3.1.1地理區塊法 28
3.1.2區塊索引法 29
3.2第二階段:原始資料網格化 30
3.2.1距離權重法 31
3.2.2最小二乘預估法 31
3.3第三階段:基於灰度共現矩陣之紋理分析 33
3.3.1均勻性 35
3.3.2對比度 35
3.3.3非相似度 35
3.3.4熵 35
3.3.5角二次矩 35
3.3.6相關性 36
3.4海底底質沉積物特徵 36
3.5第四階段:機器學習分類 37
3.5.1非監督式學習法--K平均分群演算法 38
3.5.2分類後處理 40
3.5.3訓練樣本及地真 41
3.5.4監督式學習法-最大似然分類法 42
3.5.5監督式學習法-支持向量機 43
3.5.6混合分類法 43
3.6 第五階段:分類結果精度評估 44
3.6.1生產者精度 45
3.6.2使用者精度 45
3.6.3整體精度 45
3.6.4 Kappa係數 46
第四章 實驗結果與討論 47
4.1實驗區底質 47
4.2實驗區空載測深光達資料 49
4.3第一階段資料建立結果 51
4.4第二階段資料網格化結果 53
4.5第三階段紋理分析結果 55
4.5.1 均勻性 56
4.5.2 對比度 56
4.5.3 非相似度 57
4.5.4 熵 57
4.5.5角二次矩 58
4.5.6相關性 59
4.5.7視窗精度比較 59
4.6第四階段機器學習分類結果 60
4.6.1非監督式分類(K-Means)結果 60
4.6.2分類後處理(主要分析)結果 61
4.6.3分類後處理(合併)結果 62
4.7訓練樣本及地真 63
4.8監督式分類(MLC & SVM ) 64
4.9混合分類法(KSVM)結果 64
4.10第五階段分類精度評估 65
第五章 結論 68
參考文獻 71
參考文獻 References
內政部,2009。98年度施政目標與重點,中華民國內政部全球資訊網, http://www.moi.gov.tw/。
經濟部中央地質調查所,2007。10萬分之1海洋地質圖測製及編製作業規範,http://twgeoref.moeacgs.gov.tw/GipOpenWeb/imgAction?f=/2007/20072011/ap04.pdf。
史天元,2005。測深光達原理與國外測試成果,內政部辦理 LIDAR 區之高精度及高解析度數值地形測繪、資料庫建置與應用推廣工作案,新竹縣,工業技術研究院。
田文敏,2003。溺水人員搜尋作業之探討-以苗栗縣鯉魚潭水庫搜尋作業為例,海下技術季刊,第十三卷第二期,14-18頁。
田文敏、阮建鈞,2011。海床地貌細部描繪技術之探討與應用-以澎湖望安海域為例,第三十三屆海洋工程研討會論文集,769-774頁。
李良輝,1994。遙測影像之幾何處理與數值地形模型之整合應用,中正理工學院測繪工程學系,桃園。
周明中,2005。紋理輔助高解析度衛星影像分析應用於偵測入侵性植物分布之研究,國立中央大學土木工程研究所碩士論文,共90頁。
林奕翔,2001。高解析衛星影像之空間定位精度分析及其在淺水深度量測之應用,國立中山大學海洋環境及工程學系研究所碩士論文,共132頁。
林志交、王弘基、張坤樹、李彥弘,2005。SHOALS透水光達系統於臺灣沿海水深測試報告,內政部「辦理LIDAR測區之高精度及高解析度數值地形測繪、資料庫建置與應用推廣工作案」成果發表暨應用研討會,新竹縣,工業技術研究院。
林暐尊、史天元、陳杰宗,2014。由測深光達反射率進行底質分類之研究,航測及遙測學刊,第十八卷 第3 期,145-152頁。
林暐尊、史天元、侯尚儒、陳杰宗,2014。以測深光達數據產製東沙塊礁分佈圖,航測及遙測學刊,第十九卷,第2期,147-154頁。
奚民偉,2002。海底面狀況分析與資料處理實踐,海洋測繪,第22 卷,第5 期,48-53頁。
康哲銓,2008。利用MODIS影像推估台灣沿岸海水透視度,國立成功大學測量及空間資訊學系碩士論文,共94頁。
郭發濱、杜德文,2003。「不同海底底質類型水深數據特徵分析」,海洋科學進展,第21 卷,第3 期,349-354頁。
黃明哲,2006。A Knowledge-Based Approach to Urban-feature Classification Using Aerial Imagery with Airborne LiDAR Data,國立中山大學海洋環境及工程學系博士論文,共204頁。
薛憲文、李良輝、林奕翔,2002。高解析衛星影像在淺水深度推估之研究,中華民國第二十四屆海洋工程研討會論文集,交通部港灣技術研究中心,602-608頁。
薛憲文、史天元、徐佳筠,2012。水域測深方法暨原理探討,航測及遙測學刊,第十六卷 第3期,203-217頁。
莊智瑋、陳正湘、林昭遠,2009。利用紋理因子改善影像分類準確度之研究,水土保持學報, 第41卷 第2期,153-168頁。
張功武、薛憲文,2012。以多音束測深資料之底質分類系統區分人工魚礁分佈,第34屆海洋工程研討會論文集,台南市國立成功大學,703-708頁。
黃宗仁,2002。利用統計方法進行地形資料的融合及變遷偵測,國立台灣大學土木工程研究所碩士論文,共102頁。
陳俋臣、林昭宏,2014。多尺度紋理特徵應用於遙測影像分類之研究,台灣地理資訊學會年會暨學術研討會。
陳重隆、徐誌國、李明安、陳義雄、蕭松山、方惠民,2010。海岸構造物與生態棲地多樣性關係研究,第32屆海洋工程研討會論文集,國立臺灣海洋大學,479-484頁。
謝東發、李佩珊、白敏思、蕭輔導,2006。臺灣西部沿岸海水透視度調查應用於測深光達測量先期研究,第四屆數位地球國際研討會,臺北市,文化大學,142-149 頁。
Abdulrahman Janobi, 2001. Performance evaluation of cross-diagonal texture matrix method of texture analysis, Pattern recognition 34, pp. 171-180.
Alsabti, K., Ranka, S., & Singh, V., 1997. An efficient k-means clustering algorithm.
Arivazhagan, S., and Ganesan, L., 2003. Texture classification using wavelet transform. Pattern recognition letters, 24(9), pp. 1513-1521.
Ball, Geoffrey H., and David J. Hall, 1965. ISODATA, a novel method of data analysis and pattern classification. Stanford research institute, Menlo Park CA.
Barnhardt, W.A., Kelley, J.T., Dickson, S.M., and Belknap, D.F., 1998. Mapping the Gulf of Maine with side-scan sonar:A new bottom-type classification for complex seafloors, Journal of Coastal Research, Volume 14, pp.646-659.
Blondel, P., Gomez Sichi, O., 2009. Textural analyses of multibeam sonar imagery from Stanton Banks, Northern Ireland continental shelf. Applied Acoustics 70, pp. 1288-1297.
Breiman, L., 2001. Random forests. Machine learning, 45(1), pp. 5-32.
Brown, C. J., & Blondel, P., 2009. Developments in the application of multibeam sonar backscatter for seafloor habitat mapping. Applied Acoustics, 70(10), pp. 1242-1247.
Brown, C. J., Smith, S. J., Lawton, P., & Anderson, J. T., 2011. Benthic habitat mapping: a review of progress towards improved understanding of the spatial ecology of the seafloor using acoustic techniques. Estuarine, Coastal and Shelf Science, 92(3), pp. 502-520.
Brown, C. J., Hewer, A. J., Meadows, W. J., Limpenny, D. S., Cooper, K. M., and Rees, H. L., 2004. Mapping seabed biotopes at hastings shingle bank, eastern English Channel. Part 1. Assessment using sidescan sonar. Journal of the Marine Biological Association of the UK, vol. 84, no. 03, pp. 481–488.
Brock, J. C., Wright, C. W., Clayton, T. D., & Nayegandhi, A., 2004. LIDAR optical rugosity of coral reefs in Biscayne National Park, Florida. Coral Reefs, 23(1), pp. 48-59.
Buscombe, D., 2017. Shallow water benthic imaging and substrate characterization using recreational-grade sidescan-sonar. Environmental Modelling & Software, 89, pp. 1-18.
Calvert, J., Strong, J. A., McGonigle, C., & Quinn, R., 2015. An evaluation of supervised and unsupervised classification techniques for marine benthic habitat mapping using multibeam echosounder data. ICES Journal of Marine Science: Journal du Conseil, 72(5), pp. 1498-1513.
C.-C. Chang and C.-J. Lin., 2016. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1–27:27, 2011. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm.
Chamidah, N., & Wasito, I., 2015. Fetal state classification from cardiotocography based on feature extraction using hybrid K-Means and support vector machine. In Advanced Computer Science and Information Systems (ICACSIS), 2015 International Conference on pp. 37-41.
Chakraborty, B., Mahale, V., de Sousa, C., & Das, P., 2004. Seafloor classification using echo-waveforms: a method employing hybrid neural network architecture. IEEE Geoscience and Remote Sensing Letters, 1(3), pp. 196-200.
Chust, G., Grande, M., Galparsoro, I., Uriarte, A., & Borja, Á., 2010. Capabilities of the bathymetric Hawk Eye LiDAR for coastal habitat mapping: a case study within a Basque estuary. Estuarine, Coastal and Shelf Science, 89(3), pp. 200-213.
Collier, J. S., & Brown, C. J., 2005. Correlation of sidescan backscatter with grain size distribution of surficial seabed sediments. Marine Geology, pp. 214(4), 431-449.
Collin, A. G., B. F. Long, and P. Archambault, 2011. Benthic classifications using bathymetric lidar waveforms and integration of local spatial statistics and textural features. Journal of Coastal Research, pp. 62:86-98.
Congalton, R.G., 1991. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment, pp. 37:35-46.
Costa, B. M., Battista, T. A., and Pittman, S. J., 2009. Comparative evaluation of airborne LiDAR and ship-based multi-beam SoNAR bathymetry and intensity for mapping coral reef ecosystems. Remote Sensing of Environment, vol. 113, no. 5, pp. 1082–1100.
Cottin, A. G., D. L. Forbes, and B. F. Long, 2009. Shallow seabed mapping and classification using waveform analysis and bathymetry from Shoals lidar data. Canadian Journal of Remote Sensing, pp. 35:422-434.
Cutter Jr., G.R., Y. Rzhanov, and L.A. Mayer, 2003. Automated segmentation of seafloor bathymetry from multibeam echo¬sounder data using local Fourier histogram texture features. J. Exp. Mar. Biol. Ecol. pp. 285-286:355-370.
Dartnell, P., & Gardner, J. V., 2004. Predicting seafloor facies from multibeam bathymetry and backscatter data. Photogrammetric Engineering & Remote Sensing, 70(9), pp. 1081-1091.
De, C., & Chakraborty, B., 2009. Acoustic characterization of seafloor sediment employing a hybrid method of neural network architecture and fuzzy algorithm. IEEE Geoscience and Remote Sensing Letters, 6(4), pp. 743-747.
Ebrite, S., Pope, R.W., and Lillycrop, W.J., 2001. A multi-agency solution for coastal surveys –SHOALS in the Pacific, Proc., MTS/IEEE, Nov. 5-8, Honolulu, Hawaii.
Eidem, E. J., & Landmark, K., 2013. Acoustic seabed classification using QTC IMPACT on single-beam echo sounder data from the Norwegian Channel, northern North Sea. Continental Shelf Research, pp. 68, 1-14.
ENVI User’s Guide, September, 2001 Edition.
Erdey-Heydorn, M. D., 2008. An ArcGIS seabed characterization toolbox developed for investigating benthic habitats, Marine Geodesy, pp. 318-358.
Fonseca, L. and Mayer, L., 2007. Remote estimation of surficial seafloor properties through the application Angular Range Analysis to multibeam sonar data. Marine Geophysical Researches, 28(2), pp. 119-126.
Fonseca, L., Brown, C., Calder, B., Mayer, L., Rzhanov, Y., 2009. Angular range analysis of acoustic themes from Stanton Banks Ireland: A link between visual interpretation and multibeam echosounder angular signatures. Applied Acoustics, 70(10), pp. 1298-1304.
Franklin, J., 2009. Mapping Species Distributions-Spatial Inference and Prediction; Cambridge University Press: Cambridge, UK.
Galloway, M.M., 1975. Texture Analysis Using Gray Level Run Lengths, Computer Graphics and Image Processing, Vol.4, pp. 172-179.
Gersho, A., & Gray, R., 1992. Vector quantization and signal compression. Boston: Kluwer.
Gavazzi, G. M., Madricardo, F., Janowski, L., Kruss, A., Blondel, P., Sigovini, M., & Foglini, F., 2016. Evaluation of seabed mapping methods for fine-scale classification of extremely shallow benthic habitats–Application to the Venice Lagoon, Italy. Estuarine, Coastal and Shelf Science, 170, pp. 45-60.
Gislason, P. O., Benediktsson, J. A., & Sveinsson, J. R., 2006. Random forests for land cover classification. Pattern Recognition Letters, 27(4), pp. 294-300.
Guan, H., Li, J., Chapman, M. A., Zhong, L., & Ren, Q., 2011. Support vector machine for urban land-use classification using lidar point clouds and aerial imagery. In International Symposium on LiDAR and RADAR Mapping: Technologies and Applications, Nanjing, China, May, pp. 26-29.
Guenther, G. C., Cunningham, A. G., LaRocque, P. E., Reid, D. J., 2000. Meeting the Accuracy Challenge in Airborne Lidar Bathymetry, EARSeL, Dresden.
Haralick, R.M., K. Shaunmmugam, and I. Dinstein, 1973. Textural Features for Image Classification, IEEE Trans. On Syst., Man, and Cybern., Vol. SMC-3, No.6, pp. 610-620.
Haris, K., Chakraborty, B., Ingole, B., Menezes, A., and Srivastava, R., 2012. Seabed habitat mapping employing single and multi-beam backscatter data: A case study from the western continental shelf of India. Continental Shelf Research, vol. 48, pp. 40–49.
Hasan, R. C., Ierodiaconou, D., & Monk, J., 2012. Evaluation of four supervised learning methods for benthic habitat mapping using backscatter from multi-beam sonar. Remote Sensing, 4(11), pp. 3427-3443.
Huseby, R. B., Milvang, O., Solberg, A. S., & Bjerde, K. W., 1993. Seabed classification from multibeam echosounder data using statistical methods. In OCEANS'93. Engineering in Harmony with Ocean. Proceedings, pp. 229-233.
Huang, M. J., Shyue, S. W., Lee, L. H., & Kao, C. C., 2008. A knowledge-based approach to urban feature classification using aerial imagery with lidar data. Photogrammetric Engineering & Remote Sensing, 74(12), pp. 1473-1485.
IHO Manual of Hydrography (IHO M-13), 2005. Chapter 3 : Seafloor Classification, Table 4.5, Sediment Grain Size, pp. 233.
Jackson, J. B., Kirby, M. X., Berger, W. H., Bjorndal, K. A., Botsford, L. W., Bourque, B. J., ... & Hughes, T. P., 2001. Historical overfishing and the recent collapse of coastal ecosystems. science, 293(5530), pp. 629-637.
Jain. A.K., Dubes, R.C., 1988. Algorithms for Clusting Data. Perntice Hall. Englewood Cliffs, NJ.
Jensen, J.R., 2006. Introductory Digital Image Processing, Pearson Eductation.
Jiang, B., 2004. Extraction of Spatial Objects from Laser-scanning Data Using a Clustering Technique. XXth ISPRS Congress, Geo-Imagery Bridging Continents, Istanbul, Turkey, pp. 12 -23.
John R. Smith and Shih-Fu Chang, 1996. Automated binary texture feature sets for image retrieval. In Proc. IEEE Int. Conf. Acoust., Speech, and Signal Proc., May.
Julesz, B., 1975. Experiments in the Visual Perception of Texture. Sci. Amer., Vol. 232, pp.34-43.
J. Zhou, M. Heckman, B. Reynolds, A. Carlson, and M. Bishop, 2007. Modeling network intrusion detection alerts for correlation, ACM Transactions on Information and System Security (TISSEC), vol. 10, no. 1.
Kaufman, L. and P.J. Rousseeuw, 1990. Finding Groups in Data. Wiley Online Library.
Kenny, A. J., Cato, I., Desprez, M., Fader, G., Schüttenhelm, R. T. E., & Side, J., 2003. An overview of seabed-mapping technologies in the context of marine habitat classification. ICES Journal of Marine Science: Journal du Conseil, 60(2), pp. 411-418.
Kumpumäki, T., Ruusuvuori, P., Kangasniemi, V., & Lipping, T., 2015. Data-driven approach to benthic Cover type classification using bathymetric LiDAR waveform analysis. Remote Sensing, 7(10), pp. 13390-13409.
Lee, Y.G., J.H. Lee., Y.C. Hsueh, 1998. Texture Classification Using Fuzzy Uncertainty Texture Spectrum, Neurocomputing ., Vol.20, pp. 115-122.
Lodha, S. K., Kreps, E. J., Helmbold, D. P., and Fitzpatrick, D., 2006. Aerial LiDAR data classification using support vector machines (SVM). Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission, pp. 567–574.
Lundblad, E., Wright D.J., Miller J., Larkin E.M., Rinehart R., Naar D.F., Donahue B.T., Anderson S.M., Battista T., 2006. A benthic terrain classification scheme for American Samoa, Marine Geodesy, pp. 89-111.
Lucieer, V., & Lamarche, G., 2011. Unsupervised fuzzy classification and object-based image analysis of multibeam data to map deep water substrates, Cook Strait, New Zealand. Continental Shelf Research, 31(11), pp. 1236-1247.
Mahmut, O., and Vedat, E., 1997. “Sea-floor sediment and bedforms around Turkey, revealed by side-scan imagery’, Oceanologica Acta, Volume 20, Number 5, pp.673-685.
Maune, D. F., 2007. Digital elevation model technologies and applications: the DEM user’s manual, 2nd ed. American Society for Photogrammetry and Remote Sensing, Bethesda, Md.
Marsh, I., & Brown, C., 2009. Neural network classification of multibeam backscatter and bathymetry data from Stanton Bank (Area IV). Applied Acoustics, 70(10), pp. 1269-1276.
McGonigle, C., Brown, C., Quinn, R., & Grabowski, J., 2009. Evaluation of image-based multibeam sonar backscatter classification for benthic habitat discrimination and mapping at Stanton Banks UK. Estuarine, Coastal and Shelf Science, vol. 81, no. 3, pp. 423–437.
Monteys, X., Harris, P., Caloca, S., & Cahalane, C., 2015. Spatial prediction of coastal bathymetry based on multispectral satellite imagery and multibeam data. Remote Sensing, 7(10), pp. 13782-13806.
Moritz, H., 1972. Advanced Least-Squares Methods, Reports of the Department of Geodetic Science, The Ohio State University, pp. 175.
Ng, R.T. and Han, J., 1994. Efficient and effective clustering methods for spatial data mining. Proceedings of the 20th VLDB Conference, Santiago, Chile, pp. 144–155.
Penrose, J. D., Siwabessy, P. J. W., Gavrilov, A., Parnum, I., Hamilton, L. J., Bickers, A., & Kennedy, P., 2005. Acoustic techniques for seabed classification. Cooperative Research Centre for Coastal Zone Estuary and Waterway Management, Technical Report 32.
Preston, J. M., Christney, A. C., Bloomer, S. F., & Beaudet, I. L., 2001. Seabed classification of multibeam sonar images. In Oceans. MTS/IEEE Conference and Exhibition Vol. 4, pp. 2616-2623.
Purkis, S. J., Graham, N. A. J., & Riegl, B. M., 2008. Predictability of reef fish diversity and abundance using remote sensing data in Diego Garcia (Chagos Archipelago). Coral reefs, 27(1), pp. 167-178.
Riegl, B. M., & Purkis, S. J., 2005. Detection of shallow subtidal corals from IKONOS satellite and QTC View (50, 200 kHz) single-beam sonar data (Arabian Gulf; Dubai, UAE). Remote Sensing of Environment, 95(1), pp. 96-114.
Richards, J. A., 2013. Remote sensing digital image analysis (Vol. 5). Berlin et al.: Springer.
Ryan, D. A., Brooke, B. P., Collins, L. B., Kendrick, G. A., Baxter, K. J., Bickers, A. N., and Pattiaratchi, C. B, 2007. The influence of geomorphology and sedimentary processes on shallow-water benthic habitat distribution: Esperance Bay, Western Australia. Estuarine, Coastal and Shelf Science, vol. 72, no. 1, pp. 379–386.
Saleh, M., 2010. Seabed classification using Sub-bottom profiler. Delft University of Technology, Delft, Thesis.
Saleh, M., & Rabah, M., 2016. Seabed sub-bottom sediment classification using parametric sub-bottom profiler. NRIAG Journal of Astronomy and Geophysics, 5(1), pp. 87-95.
Simons, Dick G., and Snellen, Mirjam, 2009. A Bayesian approach to seafloor classification using multi-beam echo-sounder backscatter data. Applied Acoustics, vol. 70, no. 10, pp. 1258–1268.
Stanic, S., Goodman, R. R., Briggs, K. B., Chotiros, N. P., & Kennedy, E. T. (1998). Shallow-water bottom reverberation measurements. IEEE Journal of Oceanic Engineering, 23(3), pp. 203-210.
Sun, C. J. and Wee, W. G., 1982. Neighboring Gray Level Dependence Matrix for Texture Classification,& quot; Computer Vision, Graphics, and Image Processing, vol. 23, pp. 341–352.
Tamura, H., Mori, S., & Yamawaki, T., 1978. Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8(6), pp. 460-473.
Tamsett, D., 1993. Sea-bed characterisation and classification from the power spectra of side-scan sonar data. Marine Geophysical Research, 15(1), pp. 43-64.
Tęgowski, J., 2005. Acoustical classification of the bottom sediments in the southern Baltic Sea. Quaternary International, 130(1), pp. 153-161.
Tulldahl, H. M., & Wikström, S. A., 2012. Classification of aquatic macrovegetation and substrates with airborne lidar. Remote Sensing of Environment, pp. 347-357.
T. Kanungo, D. M. Mount, N. S. Netanyahu, C. D. Piatko, R. Silverman, and A. Y.
Wu, 2002. An efficient k-means clustering algorithm: analysis and implementation, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, pp. 881-892.
USGS SEATAR, 2017. https://www.ngdc.noaa.gov/mgg/fliers/81mgg04.html。
van Walree, P. A., Tęgowski, J., Laban, C., & Simons, D. G., 2005. Acoustic seafloor discrimination with echo shape parameters: A comparison with the ground truth. Continental Shelf Research, 25(18), pp. 2273-2293.
van Overmeeren, R., Craeymeersch, J., van Dalfsen, J., Fey, F., van Heteren, S., & Meesters, E., 2009. Acoustic habitat and shellfish mapping and monitoring in shallow coastal water–Sidescan sonar experiences in The Netherlands. Estuarine, Coastal and Shelf Science, 85(3), pp. 437-448.
Vapnik, V., 1979. Estimation of Dependences Based on Empirical Data (in Russian). Nauka Moscow.
Waske, B. and Benediktsson, J. A., 2007. Fusion of support vector machine for classification of multisensory data. IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 12, pp. 3858–3866.
Watson, D. F. and Philip, G. M., 1985. A Refinement of Inverse Distance Weighted Interpolation, Geo-processing, vol.2, pp. 315~327.
Weszka J, Dyer C & Rosenfeld A, 1976. A comparative study of texture measures for terrain classification. IEEE Transactions on Systems, Man, and Cybernetics 6: 269–285.
Wienberg, C., and Bartholomä, A., 2005. Acoustic seabed classification in a coastal environment (outer Weser Estuary, German Bight)—a new approach to monitor dredging and dredge spoil disposal. Continental Shelf Research, 25(9), pp. 1143-1156.
Wilson, M. F., O’Connell, B., Brown, C., Guinan, J. C., and Grehan, A. J., 2007. Multiscale terrain analysis of multibeam bathymetry data for habitat mapping on the continental slope. Marine Geodesy, vol. 30, no. 1–2, pp. 3–35.
Worm B., Barbier E.B., Beaumont N., Duffy J. E., Folke C., Halpern B.S., Jackson J.B.C., et al., 2006. Impacts of biodiversity loss on ocean ecosystem services. Science, pp. 787–790.
Yang, X., 2011. Parameterizing support vector machines for land cover classification. Photogrammetric Engineering and Remote Sensing, vol. 77, no. 1, pp. 27–37.
Zavalas R., Ierodiaconou, D., Ryan, D., Rattray, A., and Monk, J., 2014. Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR. Remote Sensing, vol. 6, no. 3, pp. 2154–2175.
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