【摘要】：統(tǒng)計(jì)建模是合成孔徑雷達(dá)(SAR)圖像解譯必不可少的東西,不同的SAR統(tǒng)計(jì)模型(如K分布,高斯分布,伽馬分布,對數(shù)正態(tài)分布,混合分布等)對不同的SAR地物類型(如農(nóng)田,森林,草地,河流等)的建模能力各不相同。本文首先介紹了不同的統(tǒng)計(jì)分布及其特點(diǎn),對不同的SAR圖像地物類型進(jìn)行統(tǒng)計(jì)建模,找出各種統(tǒng)計(jì)模型所適合的地物類型。目標(biāo)檢測更是SAR圖像應(yīng)用的重中之重,文章針對機(jī)載SAR圖像中車輛檢測問題結(jié)合CFAR算法提出了針對角反射器散射特點(diǎn)的目標(biāo)檢測算法,并采用真實(shí)的機(jī)載P波段和L波段SAR圖像數(shù)據(jù)對算法進(jìn)行了驗(yàn)證。超高分辨率SAR圖像具有數(shù)據(jù)量大,傳統(tǒng)CFAR算法處理時(shí)間復(fù)雜度高,目標(biāo)具有一定的形態(tài)及細(xì)節(jié)的特征。針對這些特點(diǎn)我們提出了多層CFAR算法。算法中采用對數(shù)正態(tài)分布作為圖像的統(tǒng)計(jì)分布模型。我們通過對整幅SAR圖像采用基于對數(shù)正態(tài)分布的全局CFAR算法濾除強(qiáng)散射點(diǎn)來找出SAR圖像背景區(qū)域。然后依據(jù)提取出的SAR圖像背景來進(jìn)一步檢測艦船目標(biāo)。盡管多層CFAR算法提取出較準(zhǔn)確的艦船目標(biāo),但是依然存在很多虛警目標(biāo)。我們根據(jù)先驗(yàn)艦船尺寸大小,對多層CFAR算法處理后的圖像濾除虛警目標(biāo)。由于超高SAR圖像特點(diǎn),濾除虛警后的目標(biāo)有著不完整或者船體出現(xiàn)空洞的現(xiàn)象,我們提出了提取目標(biāo)輪廓算法,并對目標(biāo)輪廓進(jìn)行填充來得到完整的目標(biāo)。實(shí)驗(yàn)中使用兩幅TerraSAR-X圖像真實(shí)數(shù)據(jù),分辨率為1米,分別采用多層CFAR算法及傳統(tǒng)CFAR算法進(jìn)行實(shí)驗(yàn)比較,結(jié)果證明我們的算法有較好的檢測結(jié)果。論文得到了國家自然科學(xué)基金(No.61072106,61271302)的資助和國家“973”計(jì)劃(No.2013CB329402)的支持。
[Abstract]:Statistical modeling is essential for the interpretation of synthetic aperture radar (SAR) images. Different SAR statistical models (such as K distribution, Gao Si distribution, gamma distribution, lognormal distribution, mixed distribution, etc.) relate to different types of SAR features (such as farmland, etc.). The modeling capabilities of forests, grasslands, rivers, etc., vary. In this paper, we first introduce the different statistical distribution and its characteristics, and make statistical modeling for different SAR image feature types, and find out the suitable feature types for various statistical models. Target detection is the most important in the application of SAR image. In this paper, a target detection algorithm based on the scattering characteristics of corner reflector is proposed to solve the problem of vehicle detection in airborne SAR images combined with CFAR algorithm. The real airborne P-band and L-band SAR image data are used to verify the algorithm. Ultra-high resolution SAR images have a large amount of data, the traditional CFAR algorithm processing time complexity is high, the target has a certain shape and detail characteristics. In view of these characteristics, we propose a multi-layer CFAR algorithm. The lognormal distribution is used as the statistical distribution model of the image in the algorithm. We use the global CFAR algorithm based on lognormal distribution to filter the strong scattering points for the whole SAR image to find out the background region of the SAR image. Then the background of the extracted SAR image is used to detect the ship target. Although the multi-layer CFAR algorithm can extract more accurate ship targets, there are still many false alarm targets. According to the size of a priori ship, we filter the false alarm target from the image processed by multi-layer CFAR algorithm. Due to the characteristics of ultra-high SAR images, the target after filtering false alarm is incomplete or empty in the hull. We propose an algorithm to extract the contour of the target and fill the contour to get the complete target. In the experiment, the real data of two TerraSAR-X images are used, and the resolution is 1 meter. Compared with the traditional CFAR algorithm and the multi-layer CFAR algorithm, the results show that our algorithm has better detection results. The thesis is supported by the National Natural Science Foundation (No.61072106,61271302) and the National 973 Program (No.2013CB329402).
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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