本文選題：全極化合成孔徑雷達(dá) + 極化特征分解��； 參考：《大連海事大學(xué)》2015年碩士論文

【摘要】：利用全極化合成孔徑雷達(dá)(Polarimetric Synthetic Aperture Radar,簡稱PolSAR)數(shù)據(jù)進(jìn)行海面溢油監(jiān)測是海洋遙感的新領(lǐng)域之一,全極化數(shù)據(jù)相對單極化數(shù)據(jù),包含豐富的極化特征信息和紋理信息,并且具有高效性、實(shí)時(shí)性、不受時(shí)間、氣候限制等優(yōu)勢,因此全極化SAR海面溢油厚度估算方法的研究具有重要意義。與海冰及其他地物信息相比,由于海風(fēng)、海浪、及其自身的化學(xué)反應(yīng),海面溢油的變化具有很大的動(dòng)態(tài)性,這都增加了研究的難度。在海面溢油中,輪廓與厚度信息是溢油量的體現(xiàn)。本文采用多特征融合策略設(shè)計(jì)分類器,考慮到極化特征間的相關(guān)性,使用馬氏距離對模糊C均值聚類算法改進(jìn),進(jìn)行油膜厚度估算。本文的研究思路主要包含以下幾方面：首先,分析了海面溢油的極化散射特性,研究并比較能夠用于海面溢油的油膜厚度估算的單極化特征。其次,根據(jù)實(shí)驗(yàn)室的海面溢油數(shù)據(jù),以及分類研究,提出了基于多特征融合策略的分類器,用于油膜厚度估算。根據(jù)各特征向量在油膜厚度估算中占的比重不同,分配不同的特征權(quán)值,進(jìn)行多特征融合；分類器的設(shè)計(jì)是在模糊C均值聚類算法的基礎(chǔ)上進(jìn)行的改進(jìn),在算法的預(yù)處理階段,加入馬氏距離,自動(dòng)計(jì)算初始聚類中心,進(jìn)行油膜厚度估算。最后,在本文的實(shí)驗(yàn)中使用的數(shù)據(jù)是RADARSAT-2全極化數(shù)據(jù),都采集的是發(fā)生在墨西哥灣地區(qū)的不同時(shí)間段與時(shí)間點(diǎn)的石油泄漏事故的三景海面溢油數(shù)據(jù)。對這三景數(shù)據(jù)做測試,得出部分結(jié)果。
[Abstract]:Sea surface oil spill monitoring based on Polarimetric Synthetic Aperture Radar, data is one of the new fields of ocean remote sensing. And it has the advantages of high efficiency, real time, not limited by time and climate, so it is of great significance to study the method of estimating the oil spill thickness of fully polarized SAR sea surface. Compared with the information of sea ice and other features, because of the sea wind, wave and its own chemical reaction, the oil spill on the sea surface has a great dynamic change, which makes it more difficult to study. In the oil spill, the information of profile and thickness is the embodiment of oil spill. In this paper, multi-feature fusion strategy is used to design classifier. Considering the correlation between polarization features, Markov distance is used to improve fuzzy C-means clustering algorithm to estimate oil film thickness. The main research ideas of this paper are as follows: firstly, the polarimetric scattering characteristics of oil spills on the sea surface are analyzed, and the one-polarization characteristics which can be used to estimate the oil film thickness on the sea surface are studied and compared. Secondly, a classifier based on multi-feature fusion strategy is proposed to estimate the oil film thickness according to the oil spill data of the laboratory and the classification research. According to the different proportion of each feature vector in the estimation of oil film thickness, different characteristic weights are assigned to carry out multi-feature fusion. The classifier is designed on the basis of fuzzy C-means clustering algorithm, and in the preprocessing stage of the algorithm, The initial cluster center is calculated automatically by adding Markov distance to estimate the oil film thickness. Finally, the data used in this experiment are RADARSAT-2 full polarization data, which are collected from three sea spills of oil spill accidents in different time periods and time points in the Gulf of Mexico region. The data of the three scenes are tested and some results are obtained.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：X87

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本文編號(hào)：1837772