【摘要】：針對(duì)極化合成孔徑雷達(dá)分類過程中較難同時(shí)獲得精確的邊緣和光滑的同質(zhì)區(qū)域的問題,提出了一種基于Wishart距離的高概率選擇分類器與自適應(yīng)馬爾科夫隨機(jī)場(chǎng)相結(jié)合的分類方法,對(duì)極化合成孔徑雷達(dá)圖像分類.首先,將Wishart分類器應(yīng)用于概率輸出的支撐矢量機(jī)中,根據(jù)高概率選擇得到一個(gè)基于像素的初始分類結(jié)果,并將此結(jié)果結(jié)合不同的邊緣檢測(cè)方法得到一個(gè)精確的邊緣;其次,采用自適應(yīng)窗口的馬爾科夫隨機(jī)場(chǎng)對(duì)上一步的分類結(jié)果進(jìn)行修正,該過程在得到平滑區(qū)域的同時(shí),也保持了上一步分類結(jié)果的邊緣.實(shí)驗(yàn)結(jié)果表明,該算法提高了極化合成孔徑雷達(dá)圖像分類的精度,并保持了圖像的細(xì)節(jié)信息.
[Abstract]:In order to solve the problem that it is difficult to obtain accurate edges and smooth homogeneous regions at the same time in the process of polarimetric synthetic aperture radar (SAR) classification, a classification method based on Wishart distance is proposed, which combines high probability selection classifiers with adaptive Markov random fields. Classification of polarimetric synthetic aperture radar (SAR) images. Firstly, the Wishart classifier is applied to the support vector machine with probabilistic output, and an initial classification result based on pixels is obtained according to the high probability selection, and an accurate edge is obtained by combining the results with different edge detection methods. Secondly, the Markov random field of the adaptive window is used to modify the classification results of the previous step. The process not only obtains the smooth region, but also maintains the edge of the previous classification results. The experimental results show that the algorithm improves the accuracy of polarimetric synthetic aperture radar (SAR) image classification and maintains the details of the image.
【作者單位】： 西安電子科技大學(xué)智能感知與圖像理解教育部重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家自然科學(xué)基金資助項(xiàng)目(61671350)
【分類號(hào)】：TN957.52
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本文編號(hào)：2486803