本文選題：SAR圖像　切入點：圖像分割　出處：《哈爾濱工程大學》2014年碩士論文　論文類型：學位論文

【摘要】：合成孔徑雷達(synthetic aperture radar, SAR)是一種二維成像微波探測雷達,具有全天時、全天候、全方位對地球表面進行探測觀測的能力。因其方便、較強的觀測能力,目前已廣泛應用于軍事和民用領(lǐng)域。因此,SAR技術(shù)成為各國爭相研究的熱點,其成像分辨率越來越高,但與其不相符的是對SAR圖像極低的圖像處理率和解譯能力,這極大地限制了 SAR圖像的利用率。而圖像分割是對圖像進行理解和解譯的前提,因此研究能正確分割SAR圖像的算法尤為重要。本文的主要工作是研究基于馬爾可夫隨機場(markov random field, MRF)模型的SAR圖像分割算法,包括空域單尺度MRF模型和小波域多尺度MRF模型兩大類。本文首先對SAR系統(tǒng)的成像原理和固有相干斑噪聲的產(chǎn)生機理進行了研究,然后對SAR圖像灰度的統(tǒng)計分布進行分析,并通過實驗仿真得到不同分布模型對SAR圖像灰度的擬合度,總結(jié)了 SAR圖像的特點。最后介紹了圖像分割質(zhì)量的評價準則為后續(xù)對SAR圖像分割算法的研究作準備。其次,系統(tǒng)且詳細的研究了空域單尺度MRF模型的理論知識,包括標號場建模、觀測場建模、參數(shù)估計方法以及后驗分割方法。通過對多幅SAR圖像實驗仿真,比較了傳統(tǒng)的k-均值聚類和空域MRF模型分割方法的優(yōu)劣,并分析總結(jié)了空域MRF模型對SAR圖像產(chǎn)生誤分割的因素。再次,基于SAR圖像紋理信息豐富的特點,引入紋理特征改進空域MRF模型。介紹了紋理的定義、紋理特征的提取方法以及對SAR圖像紋理特征進行了分析。另外,由于SAR圖像通常呈現(xiàn)模糊性,將模糊理論引入標號場,改善分割結(jié)果。實驗證明改進的MRF模型分割結(jié)果在區(qū)域一致性和邊緣保持方面表現(xiàn)較好。最后,研究了多尺度MRF模型,包括經(jīng)典的MSRF模型和小波域分層Markov模型分割方法。實驗仿真結(jié)果證明,小波域分層由于同時引入了尺度間因果模型和尺度內(nèi)非因果模型,對圖像全局信息和細節(jié)信息都給出了較好的描述,其分割結(jié)果在區(qū)域一致性和邊緣保持性均優(yōu)于空域MRF模型和MSRF模型。
[Abstract]:Synthetic aperture radar (sar) is a two-dimensional imaging microwave detection radar, which has the ability to detect and observe the earth's surface all day, all weather and all directions because of its convenience and strong observation ability. At present, it has been widely used in military and civil fields. Therefore, Sar technology has become a hot topic in many countries, and its imaging resolution is getting higher and higher. However, it is inconsistent with the very low image processing rate and interpretation ability of SAR images. This greatly limits the utilization of SAR images, and image segmentation is a prerequisite for image understanding and interpretation. Therefore, it is very important to study the algorithm to segment SAR image correctly. The main work of this paper is to study the SAR image segmentation algorithm based on Markov random field random (MRF) model. In this paper, the imaging principle of SAR system and the generating mechanism of inherent speckle noise are studied firstly, and then the statistical distribution of gray level of SAR image is analyzed. Through experimental simulation, the fitting degree of different distribution models to SAR image grayscale is obtained, and the characteristics of SAR image are summarized. Finally, the evaluation criterion of image segmentation quality is introduced to prepare for the further research on SAR image segmentation algorithm. The theoretical knowledge of single scale MRF model in spatial domain is studied systematically and in detail, including label field modeling, observation field modeling, parameter estimation and posterior segmentation. In this paper, the advantages and disadvantages of traditional K-means clustering and spatial MRF model segmentation methods are compared, and the factors that cause missegmentation of SAR image by spatial MRF model are analyzed and summarized. Thirdly, based on the rich texture information of SAR image, The definition of texture, the extraction method of texture feature and the analysis of texture feature of SAR image are introduced. In addition, fuzzy theory is introduced into label field because SAR image usually presents fuzziness. The experimental results show that the improved MRF model performs well in terms of regional consistency and edge preservation. Finally, the multi-scale MRF model is studied. The experimental results show that both the inter-scale causality model and the in-scale non-causal model are introduced in the wavelet domain hierarchy, and the simulation results show that the inter-scale causality model and the in-scale non-causality model are introduced in the wavelet domain delamination. The global information and detail information of the image are well described. The segmentation results are better than the spatial MRF model and the MSRF model in regional consistency and edge preservation.
【學位授予單位】：哈爾濱工程大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52

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