【摘要】：由于海上環(huán)境復(fù)雜,溢油事故發(fā)生后,很快發(fā)生風(fēng)化、擴(kuò)散,如果不能進(jìn)行及時監(jiān)測,采取應(yīng)急措施,其對海洋環(huán)境和資源的影響將會非常嚴(yán)重。星載合成孔徑雷達(dá)(Synthetic Aperture Radar, SAR)進(jìn)行海面溢油探測,主要是利用海面對微波波段的后向散射強(qiáng)度差別來識別溢油。其具有受天氣影響小、探測精度高、覆蓋范圍廣等特點,能夠保證對海面溢油目標(biāo)準(zhǔn)確識別的要求。 目前星載合成孔徑雷達(dá)監(jiān)測溢油的研究,主要面臨的問題：一是溢油“假目標(biāo)”的影響,極大降低了溢油目標(biāo)的識別精度；二是對于溢油目標(biāo)空間信息分析不足,缺乏從空間相似性角度考慮溢油目標(biāo)識別。 本文提出的海面溢油信息提取方法,將專家知識同面向?qū)ο蠓诸惙椒ㄏ嘟Y(jié)合,較好地解決了“假目標(biāo)”的影響；同時,以紋理特征作為分類對象輸入,利用面向?qū)ο蟮姆诸惙椒?進(jìn)一步挖掘了溢油目標(biāo)的二維空間特征。本研究的創(chuàng)新點在于：根據(jù)星載SAR海面溢油圖像“假目標(biāo)”的成因、特點及發(fā)展趨勢建立星載SAR海面溢油“假目標(biāo)”分類規(guī)則,利用分類規(guī)則將“假目標(biāo)”歸類,與海面溢油的圖像特征和溢油事件發(fā)生地的背景信息結(jié)合,作為專家知識庫,由此實現(xiàn)對假目標(biāo)的剔除。另一方面,研究認(rèn)識到現(xiàn)有圖像信息提取技術(shù)的局限性,通過將“假目標(biāo)”識別與面向?qū)ο蠓诸惙椒ńY(jié)合,進(jìn)一步考慮目標(biāo)的二維空間特征,建立了星載SAR圖像海面溢油信息提取監(jiān)測方案,獲得了較好的溢油目標(biāo)識別效果,這也是本研究的另一創(chuàng)新點。 研究以2006年黎巴嫩戰(zhàn)爭導(dǎo)致的海面溢油事故為例,利用ENVISAT-ASAR數(shù)據(jù),對本研究提出的技術(shù)方法進(jìn)行了實驗和應(yīng)用,結(jié)果表明：“假目標(biāo)”專家知識庫可以很好地剔除溢油“假目標(biāo)”,與面向?qū)ο蟮姆诸惙椒ㄏ嘟Y(jié)合對星載SAR圖像海面溢油信息進(jìn)行分類,相比不進(jìn)行“假目標(biāo)”剔除的方法,大大提高了分類算法的效率和精度。 星載SAR衛(wèi)星作為環(huán)境災(zāi)害監(jiān)測的重要工具,得到了世界各國的重視,其發(fā)展逐漸以星座化、多波段、多極化為特征,從而為本文提出的星載SAR海面溢油信息提取監(jiān)測方案提供更好的數(shù)據(jù)支持,其溢油目標(biāo)識別精度也會進(jìn)一步提高。
[Abstract]:Because of the complexity of marine environment, the oil spill accidents occur soon after weathering, diffusion, if not timely monitoring, emergency measures, its impact on the marine environment and resources will be very serious. Spaceborne synthetic Aperture Radar (Synthetic Aperture Radar, SAR) is used to detect oil spills, which is mainly based on the difference of the backscattering intensity of sea surface to microwave wave band. It has the characteristics of small weather influence, high detection precision and wide coverage, which can ensure the accurate identification of oil spill targets on the sea surface. At present, the main problems in the research of Spaceborne synthetic Aperture Radar (SAR) monitoring oil spill are as follows: first, the effect of oil spill "false target" greatly reduces the recognition accuracy of oil spill target; second, the spatial information analysis of oil spill target is insufficient. Lack of spatial similarity to consider oil spill target recognition. In this paper, the method of extracting oil spill information from sea surface combines expert knowledge with object oriented classification method, which solves the problem of "false target", and takes texture feature as the input of classification object. The object-oriented classification method is used to further excavate the two-dimensional spatial features of oil spill targets. The innovation of this study lies in: according to the causes, characteristics and development trend of the "false target" in the spaceborne SAR sea surface oil spill image, the classification rules of "false target" are established, and the "false target" is classified by using the classification rule. Combined with the image feature of the oil spill and the background information of the oil spill event, it can be used as the expert knowledge base to eliminate the false target. On the other hand, in recognition of the limitations of the existing image information extraction techniques, we further consider the two-dimensional spatial features of the target by combining the "false target" recognition with the object-oriented classification method. An oil spill information extraction and monitoring scheme based on spaceborne SAR images was established, and a better target recognition effect was obtained, which is another innovation of this study. Taking the oil spill accident caused by the Lebanon War in 2006 as an example, using the ENVISAT-ASAR data, the technical methods proposed in this study were tested and applied. The results show that the expert knowledge base of "false target" can eliminate the oil spill "false target" well, and combine with the object-oriented classification method to classify the oil spill information on the surface of spaceborne SAR image, compared with the method without "false target" elimination. The efficiency and accuracy of the classification algorithm are greatly improved. Spaceborne SAR satellite, as an important tool of environmental disaster monitoring, has been paid attention to by many countries all over the world. Its development is characterized by constellation, multi-band and multi-polarization. Therefore, it can provide better data support for the oil spill information extraction and monitoring scheme proposed in this paper, and the accuracy of oil spill target recognition will be further improved.
【學(xué)位授予單位】：大連海事大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：U698.7

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