本文關鍵詞：多波束測深聲吶海底底質分類技術研究　出處：《哈爾濱工程大學》2014年博士論文　論文類型：學位論文

  更多相關文章： 多波束測深聲吶 相干成像 反向散射強度 對數(shù)域K分布 特征提取 海底底質分類

【摘要】：多波束測深聲吶是海底特征聲學遙感的主流設備之一,不僅能提供全海深、寬覆蓋、高精度的海底地形地貌信息,而且利用其接收的海底反向散射數(shù)據(jù)還可用于判別海底表層底質信息,從而能實現(xiàn)海底地形、地貌與底質類型多種海底特征信息的一體化探測,這有利于提高海洋調查的工作效率。而如何有效地利用多波束測深聲吶測量的聲學數(shù)據(jù)并可靠地判斷海底底質類型長期以來一直是人們廣泛關注地熱點與難點問題。本文結合國內外多波束測深聲吶海底底質分類技術的研究現(xiàn)狀與發(fā)展趨勢,重點圍繞具有完全自主知識產權的國產多波束測深聲吶海底反向散射成像,成像數(shù)據(jù)的統(tǒng)計特性,多源信息特征提取方法以及分類算法的實現(xiàn)等4個主要方面開展研究工作,并以理論分析、計算機仿真以及湖上與海上試驗數(shù)據(jù)處理等理論和實驗方法為研究手段,主要研究內容如下:1、研究基于多波束測深聲吶的海底反向散射成像技術。在總結分析現(xiàn)有3種基于多波束測深聲吶的海底成像方法原理及其各自優(yōu)缺點的基礎上,提出了一種基于多波束相干原理的海底聲學成像方法。首先理論研究多波束相干算法估計海底回波到達時間(Time of Arrival,TOA)與到達角度(Direction of Arrival,DOA)信息的基本方法,并在估計相位差序列時為抑制噪聲影響,從相干信號的幅度、相關性以及頻譜3個角度對相位差數(shù)據(jù)進行質量控制。在此基礎上,利用得到的TOA-DOA數(shù)據(jù)并考慮水中聲速對聲波傳播路徑的影響對海底檢測點的回波強度以及空間位置進行估計。由于該成像方法在對海底檢測點的回波強度及其位置進行測量時都利用共同的TOA-DOA信息,因此避免了 snippet法中對各波束內回波強度序列的位置及其入射角度計算時的近似處理,實現(xiàn)回波強度及其空間位置數(shù)據(jù)的準確融合,提高了成像質量,并且該方法具有良好的空間分辨率。然后,結合聲吶方程對回波強度數(shù)據(jù)進行修正,獲取與海底底質特征聯(lián)系更為緊密的反向散射強度數(shù)據(jù),并分析研究反向散射強度的角度關系修正模型,以剔除其對海底聲圖像顯示的不利影響。最后,通過試驗數(shù)據(jù)對基于多波束相干原理的海底成像方法性能進行檢驗,驗證了該方法的有效性。2、對多波束海底反向散射數(shù)據(jù)的統(tǒng)計特性進行研究。在理論分析海底反向散射信號幅度服從K分布模型的基礎上,推導得到海底反向散射強度數(shù)據(jù)的概率分布服從對數(shù)域K分布,并估計模型參數(shù)的表達式。通過對上述概率分布模型的進一步近似推導,分析高斯分布假設描述反向散射強度概率分布的適用條件與局限性。然后利用仿真數(shù)據(jù)以及多種底質類型、兩個不同頻率的多波束測深聲吶試驗數(shù)據(jù)對上述理論結果進行檢驗,驗證了理論分析的正確性和實用性。最后對不同底質、不同角度下反向散射強度數(shù)據(jù)的對數(shù)域K分布模型兩參數(shù)——尺度參數(shù)與形狀參數(shù)的一般變化規(guī)律進行分析,試驗結果表明兩參數(shù)與入射角度之間存在一定聯(lián)系,且不同底質下的形狀參數(shù)與尺度參數(shù)存在一定差別。3、對多源信息特征提取方法及其分類性能進行研究。首先以多波束海底聲圖像為數(shù)據(jù)源研究基于數(shù)據(jù)概率分布特性的特征提取方法,基于灰度共生矩陣的紋理特征提取方法以及基于功率譜比的Pace特征提取方法;其次,結合試驗數(shù)據(jù)分析圖像樣本窗大小對分類結果的影響,結果表明隨著樣本圖像尺度的增加,分類正確率越高,而當增大到一定程度時分類正確率趨于恒定,不再受樣本窗尺度的影響;并且通過利用Fisher判別比對上述特征提取方法得到各特征量的分類性能進行比對分析;其中,將對數(shù)域K分布兩參數(shù)用作分類特征量,且分類性能較好;然后,將各特征提取方法得到的特征量構成3組特征向量,并利用支持向量機(Support Vector Machine,SVM)分類器對各特征向量整體的分類能力進行綜合分析,最后,以反向散射強度數(shù)據(jù)的角度響應曲線為數(shù)據(jù)源提取分類特征,并對其性能進行仿真分析以及試驗數(shù)據(jù)的檢驗。從整體上看利用角度響應曲線提取的特征向量與上述3種基于聲圖像的特征提取方法相比,分類正確率相對較低,其中基于數(shù)據(jù)概率分布特性得到的特征向量分類性能最好,對砂、礫質砂、砂質礫、泥質砂質礫以及巖石5種類型樣本數(shù)據(jù)的總體分類正確率可達到91.95%。4、研究多源特征合成核SVM的多波束底質分類方法。為使多種特征信息聯(lián)合使用后充分發(fā)揮各自特點,提高分類性能,本文在理論分析傳統(tǒng)單核SVM分類器分類原理的基礎上提出利用合成核SVM進行多波束海底底質分類的方法,即將不同特征信息數(shù)據(jù)以加權加法形式構成合成核以代替?zhèn)鹘y(tǒng)的單核形式,并用SVM分類器進行海底底質分類。討論了分類算法中最優(yōu)參數(shù)的交叉驗證搜索方法以及總體樣本正確率、Kappa系數(shù)等分類正確率評價方法。并在此基礎上,結合試驗數(shù)據(jù)對本文研究方法的有效性進行檢驗。試驗結果表明,基于合成核SVM的海底底質分類可得到比傳統(tǒng)單核SVM更高的分類正確率,驗證了利用合成核SVM在該分類問題中的有效性;并且試驗結果表明,不同特征信息聯(lián)合使用后,如果直接合成一個向量進行分類,其分類正確率并不一定能比單獨一種特征信息獲得的分類正確率高,而經合成核SVM處理后可有效解決此問題。
[Abstract]:Multibeam sonar is one of the main characteristics of the acoustic remote sensing equipment, can not only provide the deep sea, wide coverage, seabed topography information with high accuracy, and the use of the seafloor backscatter data received can be used for distinguishing seabed material information, which can realize the integration of the seabed topography, landform and sediment detection the characteristics of the various types of information, which is conducive to improve work efficiency. The marine survey and how to effectively use of acoustic data in multi beam bathymetry sonar measurement and reliably determine the seabed types has long been widespread attention to hot and difficult problems. Combining with the present situation and development trend of domestic multi beam bathymetry sonar seabed classification technology, it has completely independent intellectual property rights of the domestic multi beam bathymetry sonar seafloor backscatter imaging, imaging data system Project characteristics, to carry out research work in 4 aspects of multi-source information extraction method and classification algorithm, and based on theoretical analysis, computer simulation and lake and sea test data processing theory and experimental method as the research method, the main research contents are as follows: 1. Research on the seafloor backscatter imaging sonar multibeam sounding based on. Based on summarizing and analyzing the existing 3 kinds of principle of multi beam bathymetry sonar seafloor imaging method based on its advantages and disadvantages, proposes a method of multi beam coherent underwater acoustic imaging. Based on the principle of the first theoretical study of multi beam coherent algorithm to estimate seabed echo arrival time (Time of, Arrival, TOA) and angle of arrival (Direction of Arrival, DOA) the basic methods of information, and in the estimation of phase difference sequence for noise suppression, the coherent signal amplitude, correlation and spectrum 3 A view of the phase difference data quality control. On this basis, using TOA-DOA data and considering the influence of water velocity on the wave propagation path of the echo intensity of submarine detection point and location estimation. Due to the imaging method for measuring the seabed detection point back wave intensity and position are the common TOA-DOA information, so as to avoid the approximate position and angle of incidence on the echo intensity of each beam in sequence by snippet method, to achieve accurate fusion of echo intensity and spatial data, the image quality is improved, and the method has good spatial resolution. Then, combined with the sonar equation on the echo data are corrected. The acquisition and seabed features more backscatter data closely, and analyze the relationship between backscattering intensity correction angle In order to eliminate the model of acoustic image shows adverse effects. Finally, through the test on the performance of underwater multi beam coherent imaging method based on the principle of test data, verified the effectiveness of the method was.2, the statistical characteristics of multibeam seafloor backscatter data research. In the theoretical analysis of seafloor backscattering amplitude obey the basic K distribution model, derived the probability distribution of the seafloor backscatter strength data obey logarithmic K distribution, and the expression to estimate model parameters. Through further approximation of the probability distribution model is derived, analysis of the Gauss distribution describes the backscatter intensity probability distribution of the applicable conditions and limitations. Then using the simulation data as well as a variety of sediment types, to test the above theoretical results are two different frequency multibeam sonar test data, verify the theoretical analysis. The correctness and practicability. The different substrate, different angles backscatter data of the log domain K distribution model of two - parameter scale parameter and shape parameter changes in general analysis, test results show that there is a certain relation between the two parameters and incident angle, there are some differences and.3 under different substrate shape parameter and scale parameter, the multi-source feature extraction method and its classification performance is studied. Firstly, through multi beam acoustic image feature extraction method based on probability distribution characteristics of data on the data source, the texture feature of gray level co-occurrence matrix extraction method and extraction method of power spectrum based on Pace characteristic ratio; secondly, combined with the analysis of the test data effect of window size on the sample image classification results, the results show that with the increase of sample image scale, the correct classification rate is higher, and when increased to To a certain extent when the correct classification rate tends to be constant, is no longer affected by sample window scale; and the classification performance by using Fisher discriminant feature extraction method comparing the various characteristic parameters were compared and analyzed; the logarithmic domain K distribution of two parameters for the classification features, and better classification performance; then, 3 feature vectors constitute characteristic quantity method will extract the feature, and the use of support vector machine (Support Vector Machine, SVM) classifier to each feature vector of the overall classification ability of comprehensive analysis, finally, the backscatter data point response curve extracted features as the data source, and tested the simulation analysis and test data of the look at the response performance. The feature vector extraction curve and above-mentioned 3 kinds of extraction methods compared based on features of acoustic images using the angle from the whole, the correct classification rate is relatively low The classification, performance data distribution characteristics based on the best of sand, gravel, sand, sandy gravel, sandy gravel and mud in the overall classification of 5 types of rock sample data accuracy can reach 91.95%.4, the multi beam seafloor classification method of multi-source feature synthesis of nuclear SVM. Give full play to their respective characteristics the combined use of multiple features, improve the classification performance, this paper based classification principle of traditional single kernel SVM classifier is proposed on the use of synthetic nuclear SVM multi beam seafloor classification method in the theoretical analysis, the different forms of single core feature information data in the form of weighted addition to replace the traditional nuclear synthesis, and seabed classification using SVM classifier. The optimal parameters of cross validation classification algorithm in the search method and overall sample accuracy, Kappa coefficient classification accuracy evaluation method. And on this basis, verify with the test data of the research methods of this paper. The experimental results show that the synthesis of nuclear SVM seabed classification can get higher classification than traditional single core SVM accuracy based on validation of the use of nuclear SVM synthesis in the classification of the validity of the test results; and show that the combined use of different feature information, if the direct synthesis of a vector classification, the classification accuracy is not necessarily than a single kind of feature information to obtain the correct rate, and the synthesis of nuclear SVM treatment can effectively solve this problem.

【學位授予單位】：哈爾濱工程大學
【學位級別】：博士
【學位授予年份】：2014
【分類號】：P714

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