本文關(guān)鍵詞： 極化合成孔徑雷達(dá) 極化散射特征 極化目標(biāo)分解 機(jī)場(chǎng)跑道檢測(cè) 飛機(jī)檢測(cè)　出處：《中國民航大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：極化合成孔徑雷達(dá)(Polarimetric Synthetic Aperture Radar,PolSAR)可對(duì)感興趣區(qū)域(Region of Interest,ROI)實(shí)施全天時(shí)、全天候的偵察。同時(shí),通過不同的天線組合方式獲取反映目標(biāo)振幅、相位等信息的極化散射矩陣,從而比傳統(tǒng)的單極化SAR獲得更為豐富的地物信息。已被廣泛應(yīng)用于農(nóng)業(yè)、林業(yè)、地質(zhì)學(xué)、海洋學(xué)、軍事探測(cè)等眾多領(lǐng)域。機(jī)場(chǎng)作為軍用和民用的重要設(shè)施,其自動(dòng)檢測(cè)在軍事偵察、精確打擊、緊急救援和飛機(jī)導(dǎo)航等眾多領(lǐng)域有著重要的實(shí)用價(jià)值。飛機(jī)作為一種典型的人造目標(biāo),是軍事偵察的主要打擊目標(biāo)之一,針對(duì)飛機(jī)的檢測(cè)研究具有重要的意義。本文針對(duì)復(fù)雜場(chǎng)景下的PolSAR圖像機(jī)場(chǎng)跑道檢測(cè)和飛機(jī)目標(biāo)檢測(cè)分別展開研究。對(duì)于機(jī)場(chǎng)跑道檢測(cè),本文在研究了PolSAR圖像分類和機(jī)場(chǎng)跑道特征的基礎(chǔ)上,給出了兩種復(fù)雜場(chǎng)景下的機(jī)場(chǎng)跑道檢測(cè)算法。第一種算法首先利用先驗(yàn)信息結(jié)合H/α分類提取模板;然后利用PolSAR圖像極化相干矩陣的統(tǒng)計(jì)特性進(jìn)行分類;最后利用跑道尺寸和結(jié)構(gòu)特征進(jìn)行判別,確定機(jī)場(chǎng)跑道區(qū)域。第二種算法作為改進(jìn)算法,采用h/q分類提取地物模板,并加入極化總功率檢測(cè)器判別跑道。利用美國UAVSAR系統(tǒng)采集的多組全極化實(shí)測(cè)數(shù)據(jù)對(duì)兩算法進(jìn)行實(shí)驗(yàn),結(jié)果表明,后一種算法在繼承了前一種算法能正確檢測(cè)出跑道的優(yōu)點(diǎn)的同時(shí),降低了運(yùn)算量,虛警更少,跑道輪廓更清晰,細(xì)節(jié)保持更好。從兩個(gè)方面對(duì)PolSAR圖像中飛機(jī)目標(biāo)檢測(cè)展開了研究。一方面,針對(duì)復(fù)雜大場(chǎng)景,根據(jù)飛機(jī)通常�？吭跈C(jī)場(chǎng)停機(jī)坪、滑行道等區(qū)域的特點(diǎn),給出一種基于先驗(yàn)知識(shí)的檢測(cè)算法。該算法在檢測(cè)出機(jī)場(chǎng)跑道區(qū)域的基礎(chǔ)上,利用Shannon熵對(duì)飛機(jī)和跑道加以判別。另一方面,針對(duì)復(fù)雜小場(chǎng)景,在已經(jīng)定位了機(jī)場(chǎng)區(qū)域的前提下,給出一種基于條件熵和Shannon熵的檢測(cè)算法。該算法利用條件熵和Shannon熵以及飛機(jī)的結(jié)構(gòu)特征進(jìn)行多目標(biāo)檢測(cè)。采用美國UAVSAR系統(tǒng)、AIRSAR系統(tǒng)采集的多組全極化實(shí)測(cè)數(shù)據(jù)對(duì)兩算法進(jìn)行實(shí)驗(yàn),并驗(yàn)證了該算法的有效性。
[Abstract]:Polarimetric Synthetic Aperture Radar. PolSAR can carry out round-the-clock reconnaissance on the area of interest region of InterestROI. At the same time. The polarimetric scattering matrix reflecting the amplitude and phase of the target is obtained by different antenna combinations, which is more abundant than the traditional single-polarization SAR. It has been widely used in agriculture and forestry. Geology oceanography military exploration and many other fields. Airport as an important military and civilian facilities its automatic detection in military reconnaissance accurate strike. As a typical artificial target, aircraft is one of the main targets of military reconnaissance. It is of great significance to study the detection of aircraft. In this paper, the airport runway detection and aircraft target detection based on PolSAR images under complex scenes are studied, respectively. In this paper, PolSAR image classification and airport runway features are studied. In this paper, two algorithms for airport runway detection in complex scenarios are presented. Firstly, the template is extracted by using prior information and H / 偽 classification. Then the statistical properties of polarimetric coherence matrix of PolSAR image are used to classify. Finally, the size and structure of the runway are used to determine the airport runway area. The second algorithm is used as the improved algorithm to extract the ground object template by using h / Q classification. The two algorithms are tested by using multi-sets of fully polarimetric measured data collected by American UAVSAR system, and the results show that the proposed algorithm can be used to identify the runway with a polarimetric total power detector. The latter algorithm not only inherits the advantages of the former algorithm, but also reduces the amount of computation, less false alarm and clearer contour of the runway. The study of aircraft target detection in PolSAR image is carried out from two aspects. On the one hand, according to the complex large scene, the aircraft is usually parked at the airport apron. Based on the characteristics of taxiway and other areas, a priori knowledge based detection algorithm is presented. On the basis of detecting the airport runway area, Shannon entropy is used to distinguish the aircraft from the runway. For complex small scenarios, the airport area has been located under the premise. This paper presents a detection algorithm based on conditional entropy and Shannon entropy. The algorithm uses conditional entropy and Shannon entropy as well as structural features of aircraft to detect multi-target. American UAVSAR system is adopted. . The two algorithms are tested by multi-sets of full polarization data collected by AIRSAR system and the validity of the algorithm is verified.
【學(xué)位授予單位】：中國民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：V351;TN957.52

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