本文選題：SAR + 8-bit轉(zhuǎn)換��； 參考：《中國地質(zhì)大學(xué)(北京)》2015年碩士論文

【摘要】：近年來,隨著人們對石油的需求不斷增加,海上運輸業(yè)、石油開采業(yè)取得了快速發(fā)展,致使海上溢油事故發(fā)生的頻率也呈現(xiàn)出不斷增加的趨勢�；谌鞎r、全天候的SAR技術(shù)能夠穿透云和霧等優(yōu)點,其目前已被廣泛應(yīng)用于海上溢油監(jiān)測。本文采用面向?qū)ο蟮男畔⑻崛〖夹g(shù)對SAR溢油圖像進行了研究,成功提取出海上油膜信息,取得的主要成果歸納如下:(1)介紹了SAR系統(tǒng)工作原理和監(jiān)測海面溢油原理,以及溢油現(xiàn)象在SAR圖像上具有的表現(xiàn)特征。對實驗數(shù)據(jù)進行去噪處理時,本文在不同的窗口大小下采用了七種常用的濾波方法對其進行斑點濾波并進行效果評價,最終選擇了7×7窗口大小的增強Lee濾波方法。(2)在對SAR圖像預(yù)處理過程中,引入了8-bit轉(zhuǎn)換及灰度降級操作,保證了在不損失溢油信息的前提下壓縮了數(shù)據(jù)量,從而提高了SAR圖像處理的速度和效率。(3)在對SAR圖像進行多尺度分割時,經(jīng)過多次試驗嘗試,通過分析每種分割參數(shù)的影響并結(jié)合目視判讀效果,最終選擇了一組尺度參數(shù)為20、形狀因子為0.2、緊致度因子為0.5的多尺度分割結(jié)果圖像用于后續(xù)對溢油信息的進一步提取。(4)在提取溢油信息時,利用了能夠區(qū)分油膜與疑似油膜目標的光譜特征、幾何特征、紋理特征和物理特征,通過對這些特征做進一步的綜合分析,構(gòu)建了適用于溢油信息提取的模糊規(guī)則庫,最后結(jié)合軟件中提供的相應(yīng)隸屬度函數(shù)完成了對溢油信息的提取。(5)將基于像元的監(jiān)督分類結(jié)果分別與面向?qū)ο蟮淖钹徑诸惡湍：诸惤Y(jié)果進行精度比較,其總體分類精度和Kappa系數(shù)分別為90.43%和0.7920、90.72%和0.8607、93.02%和0.8610。結(jié)果證明,采用面向?qū)ο蟮哪：诸惙椒ǹ梢愿鼫蚀_的完整的提取出海上溢油信息。
[Abstract]:In recent years, with the increasing demand for oil, the marine transportation industry and the oil mining industry have made rapid development, resulting in the frequency of oil spills on the sea also showing an increasing trend. Based on the advantages of all-day, all-weather SAR technology, which can penetrate clouds and fog, it has been widely used in offshore oil spill monitoring. In this paper, the object oriented information extraction technique is used to study the SAR oil spill image, and the oil film information is extracted successfully. The main results are summarized as follows: 1) the working principle of the SAR system and the principle of monitoring the oil spill on the sea surface are introduced. And the characteristics of oil spill on SAR images. When the experimental data is de-noised, seven common filtering methods are used in this paper under different window sizes to carry out speckle filtering and evaluate its effect. Finally, the 7 脳 7 window size enhanced Lee filtering method is chosen. In the process of SAR image preprocessing, the operation of 8-bit conversion and gray scale degradation is introduced to ensure that the data is compressed without losing the oil spill information. Thus, the speed and efficiency of SAR image processing are improved. When the SAR image is segmented at multiple scales, after many experiments, the influence of each segmentation parameter is analyzed and the effect of visual interpretation is combined. Finally, a group of multiscale segmentation images with scale parameters of 20, shape factor of 0.2 and tightness factor of 0.5 are selected for further extraction of oil spill information. The spectral features, geometric features, texture features and physical features are used to distinguish oil film from suspected oil film targets. Through further comprehensive analysis of these features, a fuzzy rule base suitable for oil spill information extraction is constructed. Finally, combined with the corresponding membership function provided in the software, the extraction of oil spill information is completed. Finally, the results of supervised classification based on pixel are compared with the results of object-oriented nearest neighbor classification and fuzzy classification, respectively. The overall classification accuracy and Kappa coefficient were 90.43% and 0.7920% 90.72% and 0.8607 793% and 0.8610%, respectively. The results show that the object-oriented fuzzy classification method can extract the oil spill information more accurately and completely.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：X55;X87

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