本文關(guān)鍵詞： SAR 相干斑抑制 核回歸 非局部均值濾波 迭代估計(jì)　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)作為一種等效天線孔徑的雷達(dá),它根據(jù)雷達(dá)與目標(biāo)的相對(duì)運(yùn)動(dòng)來(lái)把較小尺寸的真實(shí)天線孔徑用數(shù)據(jù)處理的方法進(jìn)行合成的。SAR圖像不僅具有全天候、全天時(shí)、分辨率高、可側(cè)視成像等諸多優(yōu)點(diǎn),而且包含了豐富的特征信號(hào),其中包括了多種信息,如幅度、相位和極化等,近幾年來(lái)進(jìn)行SAR圖像的處理(去噪,分割,目標(biāo)識(shí)別等)也越來(lái)越受到廣泛的關(guān)注。但是由于SAR圖像的特殊成像原理,會(huì)產(chǎn)生相干的散射回波,造成了得到的SAR圖像中含有隨機(jī)的斑點(diǎn)噪聲,并且這些噪聲是乘性的,使得SAR圖像的去噪處理與一般圖像不同。這種相干斑噪聲影響了SAR圖像的質(zhì)量和后續(xù)的處理。因此對(duì)SAR圖像的相干斑抑制是非常有必要的,并且要盡可能的保留圖像的細(xì)節(jié)信息。SAR圖像去噪問(wèn)題主要就是在去除斑點(diǎn)噪聲和保留SAR圖像細(xì)節(jié)這兩個(gè)方面做到一種好的平衡。本文主要是在更好的保留SAR圖像細(xì)節(jié)信息方向上對(duì)相干斑抑制方法做出了一些改進(jìn),主要工作和貢獻(xiàn)如下:1.提出基于核回歸特征聚類和改進(jìn)非局部均值濾波的SAR圖像相干斑抑制方法。主要是通過(guò)自適應(yīng)核回歸自身的核函數(shù)特性,可以通過(guò)權(quán)值表示得到SAR圖像的一些細(xì)節(jié)特征(邊緣,紋理等)。為了更好的處理這些特征,本文采用聚類的方式,將這些提取的特征作為初始聚類中心,然后利用K-means聚類的方法將相似的特征聚合在一起,這樣就可以得到多個(gè)具有相似特征的聚類的,接下來(lái)通過(guò)改進(jìn)相似性度量的方式進(jìn)行優(yōu)化非局部均值濾波,這樣有效的保證了在對(duì)每一類進(jìn)行一個(gè)非局部均值去噪處理時(shí)能夠盡可能保留SAR圖像的細(xì)節(jié)部分。2.提出了基于diffusion和boosting的自適應(yīng)迭代估計(jì)的SAR圖像相干斑抑制方法。主要是引入了基于最小均方誤差(MSE)的一種風(fēng)險(xiǎn)估計(jì),針對(duì)diffusion和boosting這兩種迭代機(jī)制,它們各有優(yōu)缺點(diǎn),后者可以彌補(bǔ)前者的缺點(diǎn),因此本文結(jié)合這兩種迭代方法的優(yōu)點(diǎn),進(jìn)行自適應(yīng)選擇,得到最優(yōu)的迭代方法和迭代次數(shù),然后基于這個(gè)最優(yōu)選擇得到的結(jié)果進(jìn)行非局部均值濾波,本方法可以在很好的保留圖像的細(xì)節(jié)信息的同時(shí)達(dá)到去噪的目的。
[Abstract]:Synthetic Aperture radar (SAR) as an equivalent antenna aperture radar. According to the relative motion of radar and target, the synthetic. SAR image with smaller real antenna aperture is not only all-weather, all-day, but also has high resolution. Side view imaging and many other advantages, but also contains a wealth of characteristic signals, including a variety of information, such as amplitude, phase and polarization, SAR image processing in recent years (denoising, segmentation). But because of the special imaging principle of SAR image, coherent scattering echo will be produced, resulting in random speckle noise in the obtained SAR image. And these noises are multiplicative. This kind of speckle noise affects the quality of SAR image and the subsequent processing. So speckle suppression for SAR image is very necessary. The problem of image denoising is to achieve a good balance between removing speckle noise and preserving the details of SAR image. Some improvements have been made to the speckle suppression method in the direction of preserving the details of SAR images. The main work and contributions are as follows: 1. A new method of speckle suppression for SAR images based on kernel regression feature clustering and improved non-local mean filter is proposed, which is mainly based on adaptive kernel regression. We can get some detail features (edge, texture, etc.) of SAR image by weight. In order to deal with these features better, we use clustering method to take these extracted features as the initial clustering center. Then the K-means clustering method is used to aggregate the similar features together, so that we can get multiple clusters with similar features. Then the non-local mean filter is optimized by improving the similarity measure. In this way, the detail part of SAR image can be preserved as much as possible when each class is de-noised with a non-local mean value. (2) based on diffusion and boosting, this paper puts forward a new method based on diffusion and boosting. Adaptive iterative estimation method for speckle suppression in SAR images based on the least mean square error (MMSE) is introduced. A risk estimate for MSE. For diffusion and boosting, they have their own advantages and disadvantages, the latter can make up for the shortcomings of the former, so this paper combines the advantages of these two iterative methods. Adaptive selection is carried out to obtain the optimal iterative method and the number of iterations, and then the non-local mean filtering is carried out based on the results obtained from the optimal selection. The method can preserve the details of the image and achieve the purpose of denoising at the same time.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TN957.52

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相關(guān)期刊論文 前1條

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本文編號(hào)：1493854