本文關(guān)鍵詞：基于SVM的ASAR波模式海浪波高與周期提取研究　出處：《內(nèi)蒙古大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： ASAR 波模式數(shù)據(jù) SVM 有效波高 平均波周期

【摘要】：星載合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)作為一種微波遙感探測(cè)手段,可以不受日照、云霧等外界環(huán)境因素影響實(shí)現(xiàn)對(duì)地觀測(cè)。ENVISAT衛(wèi)星搭載的先進(jìn)合成孔徑雷達(dá)(Advanced Synthetic Aperture Radar,ASAR)工作于C波段,具有5種工作模式,其中波模式全天時(shí)開通,數(shù)據(jù)量豐富。本文提出了基于支持向量機(jī)(Support Vector Machine,SVM)回歸模型的ASAR波模式數(shù)據(jù)有效波高(Significant Wave Height,SWH)和平均波周期(Mean Wave Period,MWP)提取新方法。論文詳細(xì)闡述了 SAR數(shù)據(jù)反演海浪參數(shù)的背景及意義,并對(duì)現(xiàn)有的海浪參數(shù)反演技術(shù)的進(jìn)展進(jìn)行了扼要介紹。分析了 SAR海表面成像機(jī)制并指出了現(xiàn)有MPI方法、參數(shù)化方法(PARSA)、半?yún)?shù)化方法(SPRA)等海浪譜反演存在的不足,進(jìn)而提出使用SVM提取海浪參數(shù)的新方法。本文完成工作如下:1.SVM模型的建立。詳細(xì)介紹了 ASAR波模式圖像預(yù)處理步驟、ASAR圖像譜分解過程及特征參數(shù)提取過程,并且從空間域和頻域分析了特征參數(shù)與SWH和MWP之間的關(guān)系。然后,給出了 SVM回歸機(jī)模型的理論推導(dǎo)過程,使用30771景與全球氣候再分析數(shù)據(jù)(ERA-Interim)匹配的ASAR波模式樣本進(jìn)行SVM模型訓(xùn)練。2.SVM模型驗(yàn)證與分析。本文分別采用浮標(biāo)和ERA-Interim數(shù)據(jù)對(duì)SVM方法提取的SWH和MWP進(jìn)行了印證,SWH均方誤差分別為0.49米和0.4米,相關(guān)度分別為0.8和0.93。MWP均方誤差分別為1.08秒和0.62秒,相關(guān)度為0.76和0.87。由此說明,基于SVM回歸模型的ASAR SWH和MWP提取是一種有效的方法。3.SVM模型進(jìn)一步評(píng)估,對(duì)臺(tái)風(fēng)案例和2011年全球海況季節(jié)性變化特征進(jìn)行了分析。論文結(jié)果表明,本文提出的SVM模型對(duì)高海況區(qū)域的海洋風(fēng)暴預(yù)警、監(jiān)測(cè)及海浪參數(shù)的業(yè)務(wù)化運(yùn)行具有重要的意義。
[Abstract]:Spaceborne synthetic Aperture radar (SAR) as a microwave remote sensing method, it can not be exposed to sunlight. The advanced synthetic Aperture Radar (ASAR) carried by the Earth observation. ENVISAT satellite is affected by external environmental factors such as clouds and fog. Advanced Synthetic Aperture Radar. ASAR) operates in C-band and has five modes of operation, in which the wave mode is switched on all day. This paper presents support Vector Machine based on support vector machine. The effective wave height of ASAR wave model data is significant Wave Height. In this paper, the background and significance of wave parameters inversion from SAR data are described in detail. The development of ocean wave parameter inversion technology is briefly introduced, the imaging mechanism of SAR sea surface is analyzed and the existing MPI method, parameterized method, is pointed out. The demerit of the semi-parameterization method, such as SPRA, in the inversion of ocean wave spectrum. Then a new method of extracting ocean wave parameters using SVM is proposed. The main work of this paper is as follows: 1. The establishment of ASAR model is completed. The preprocessing steps of ASAR wave mode image are introduced in detail. ASAR image spectral decomposition process and feature parameter extraction process, and from the spatial and frequency domain analysis of the relationship between feature parameters and SWH and MWP. Then. The theoretical derivation process of SVM regression model is given. ERA-Interim using 30771 View and Global Climate Reanalysis data. The matching ASAR wave pattern samples are trained by SVM model. 2.SVM model verification and analysis. This paper uses buoy and ERA-Interim data to extract SWH and SVM from SVM method, respectively. MWP confirmed it. The mean square error (MSE) of SWH was 0.49m and 0.4m, and the correlation degree was 0.8,0.93.MWP mean square error was 1.08s and 0.62s, respectively. The correlation is 0.76 and 0.87. It shows that ASAR SWH and MWP extraction based on SVM regression model is an effective method for further evaluation. 3. The characteristics of seasonal variation of global sea conditions in 2011 are analyzed. The results show that the SVM model proposed in this paper is an early warning method for ocean storms in high sea conditions. Monitoring and operational operation of wave parameters are of great significance.
【學(xué)位授予單位】：內(nèi)蒙古大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TN957.52

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