一種基于非參數(shù)密度估計(jì)和馬爾可夫上下文的SAR圖像分割算法
A Segmentation Method of SAR Images Based on Non-parametric Density Estimate and Markovian Contexture
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摘要: 在研究傳統(tǒng)的基于參數(shù)的合成孔徑雷達(dá)(SAR)圖像統(tǒng)計(jì)模型基礎(chǔ)上，為了精確估計(jì)高分辨率SAR圖像的統(tǒng)計(jì)分布，該文提出了一種結(jié)合基于核函數(shù)的非參數(shù)估計(jì)和馬爾可夫上下文的SAR圖像分割算法。該算法首先采用基于核函數(shù)的非參數(shù)方法估計(jì)SAR圖像的統(tǒng)計(jì)分布，然后將此統(tǒng)計(jì)量作為圖像分割的似然函數(shù)，利用馬爾可夫上下文約束進(jìn)行SAR圖像分割。該文通過軟件仿真對新算法和基于參數(shù)的統(tǒng)計(jì)模型的算法的效果進(jìn)行了比較。研究發(fā)現(xiàn)，基于核函數(shù)的非參數(shù)估計(jì)方法僅僅依賴實(shí)際數(shù)據(jù)，在無法準(zhǔn)確獲取分布函數(shù)解析式的情況下往往具有更好的效果。實(shí)驗(yàn)證明，基于核函數(shù)的非參數(shù)估計(jì)方法對高分辨率SAR圖像中較為復(fù)雜的場景如城區(qū)的提取取得了更為滿意的結(jié)果。Abstract: Aiming at giving a precise estimation of the statistic distribution of high-resolution Synthetic Aperture Radar (SAR) images, a segmentation method of SAR images using technique of non-parametric density estimate with kernel method and Markovian contexture is proposed in this paper, after studying the traditional models based on parametric technique. First, a non-parametric density estimate method based on kernel function is adopted to estimate the statistic distribution of the SAR images, and then, the SAR images is segmented with Markovian contexture by maximizing a MAP estimator, taking the former estimation as its likelihood term. And the results of the new proposed method and methods based on parametric statistical models are compared by software simulation. It shows that non-parametric density estimate technique based on kernel function can provide better results by just depending on real data, when there is no available analytical distribution function. Experiments on real SAR images also show that the non-parametric method can model the complex scenes of high-resolution SAR images such as urban areas well and get better results of segmentation.
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