發(fā)布時(shí)間：2018-06-05 03:15

  本文選題：合成孔徑雷達(dá) + 艦船探測(cè)實(shí)驗(yàn)��； 參考：《合肥工業(yè)大學(xué)》2016年碩士論文

【摘要】：作為沿海大國(guó),我國(guó)領(lǐng)海廣闊,海洋資源豐富,對(duì)高效、實(shí)時(shí)的海洋監(jiān)測(cè)手段需求迫切。合成孔徑雷達(dá)(synthetic aperture radar, SAR)作為一種先進(jìn)的主動(dòng)式微波遙感器,具有全天時(shí)、全天候、多極化等諸多優(yōu)點(diǎn),并且具有一定穿云透霧的能力,已被廣泛應(yīng)用于大地遙感、測(cè)繪以及資源勘探等領(lǐng)域。利用SAR圖像對(duì)海洋艦船目標(biāo)進(jìn)行檢測(cè),為海洋交通運(yùn)輸、漁業(yè)活動(dòng)控制、海洋環(huán)境監(jiān)測(cè)提供了有效手段,對(duì)我國(guó)領(lǐng)土安全有著重要意義。本文立足于SAR圖像艦船檢測(cè)技術(shù)的發(fā)展,在分析現(xiàn)有SAR圖像艦船檢測(cè)算法不足的基礎(chǔ)上,重點(diǎn)研究了服從Rice分布的恒虛警率(Constant False Alarm Rate, CFAR)艦船檢測(cè)方法、SAR目標(biāo)特性數(shù)據(jù)庫(kù)搭建等問(wèn)題。本文的主要研究?jī)?nèi)容如下：(1)針對(duì)SAR圖像中高噪聲引起的艦船目標(biāo)檢測(cè)性能下降的問(wèn)題,研究了一種基于Rice分布的CFAR艦船檢測(cè)方法。首先,研究了不同波段、不同極化方式、不同分辨率以及不同噪聲情況下的海雜波分布模型,并利用大量實(shí)測(cè)數(shù)據(jù)驗(yàn)證了Rice分布對(duì)海況特性描述的有效性。最后,提出了一種適用于高噪聲情況下的Rice-CFAR艦船監(jiān)測(cè)算法,對(duì)ENVISAT和TERRASAR-X等實(shí)測(cè)艦船數(shù)據(jù)的檢測(cè)結(jié)果表明,本文算法在高噪聲情況下較之已有算法具有較高的檢測(cè)準(zhǔn)確率。(2)海上艦船目標(biāo)探測(cè)實(shí)驗(yàn)。開(kāi)展了兩次海上艦船目標(biāo)探測(cè)實(shí)驗(yàn),實(shí)驗(yàn)海域?yàn)椴澈：Ｓ�。利用Radarsat-2星載SAR與AIS協(xié)同探測(cè)的方法,獲取了星載SAR與AIS的同步數(shù)據(jù),為艦船目標(biāo)探測(cè)的應(yīng)用研究提供重要的實(shí)測(cè)驗(yàn)證數(shù)據(jù),同時(shí)獲取了大量的數(shù)據(jù)也為SAR目標(biāo)特性數(shù)據(jù)庫(kù)的構(gòu)建提供了數(shù)據(jù)基礎(chǔ)。(3)SAR目標(biāo)特性數(shù)據(jù)庫(kù)軟件系統(tǒng)研發(fā)。利用當(dāng)前現(xiàn)有的SAR衛(wèi)星數(shù)據(jù),從海洋目標(biāo)和陸地目標(biāo)的角度出發(fā),構(gòu)建了不同地物目標(biāo)類型、不同極化方式、不同入射角的SAR目標(biāo)散射特性數(shù)據(jù),建立SAR目標(biāo)特性數(shù)據(jù)庫(kù)軟件系統(tǒng),填補(bǔ)了目前國(guó)內(nèi)該領(lǐng)域的空白。
[Abstract]:As a large coastal country, China has a vast territorial sea and abundant marine resources. Synthetic aperture radar, SAR), as an advanced active microwave remote sensor, has many advantages, such as all-day, all-weather, multi-polarization and so on. Surveying and mapping and resource exploration and other fields. The detection of marine ship targets by SAR images provides an effective means for ocean transportation, fishery activity control and marine environment monitoring, which is of great significance to the territorial security of our country. Based on the development of ship detection technology in SAR images, this paper analyzes the shortage of existing ship detection algorithms in SAR images. The constant false alarm rate (CFAR) and constant false alarm rate (CFAR) are studied in detail. The main contents of this paper are as follows: (1) aiming at the problem of the degradation of ship target detection performance caused by high noise in SAR images, a CFAR ship detection method based on Rice distribution is studied. Firstly, the model of sea clutter distribution in different wave bands, different polarization modes, different resolution and different noise is studied, and the validity of Rice distribution in describing sea conditions is verified by a large number of measured data. Finally, a Rice-CFAR ship monitoring algorithm is proposed, which is suitable for high noise condition. The detection results of ENVISAT and TERRASAR-X data show that, In this paper, the detection accuracy of the algorithm is higher than that of the existing algorithms in the case of high noise. Two experiments of ship target detection were carried out in Bohai Sea. Using the method of cooperative detection of Radarsat-2 spaceborne SAR and AIS, the synchronous data of spaceborne SAR and AIS are obtained, which provide important verification data for the application of ship target detection. At the same time, a large amount of data is obtained, which also provides a data base for the construction of SAR target characteristic database. Based on the existing SAR satellite data, the scattering characteristic data of SAR targets with different object types, different polarization modes and different incident angles are constructed from the point of view of ocean and land targets. The software system of SAR target characteristic database is established, which fills up the blank in this field at present.
【學(xué)位授予單位】：合肥工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TN957.52;TP311.52

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本文編號(hào)：1980214