本文選題：PolSAR + 集成學習��； 參考：《西安電子科技大學》2014年碩士論文

【摘要】：極化合成孔徑雷達(Polarimetric Synthetic Aperture Radar,PolSAR)是一種多參數(shù)、多通道的成像雷達系統(tǒng),因其全天時,全天候,高分辨的優(yōu)勢而得到廣泛的應用�；跈C器學習的PolSAR圖像分類方法取得了很高的分類精度,但是當有標簽噪聲存在時,分類結果會受到很大的影響。本文基于集成學習,針對PolSAR圖像分類中標簽噪聲的問題,進行了深入的研究,主要包括以下三方面的內(nèi)容:1.結合PolSAR圖像的偏振參數(shù)、散射、紋理特征,提出了一種基于AdaBoost的PolSAR圖像監(jiān)督分類算法(Knn.Ada Boost)。此方法利用PolSAR圖像的偏振參數(shù)、極化散射特征和圖像的紋理特征,作為Ada Boost的輸入特征,Knn.Ada Boost算法預先通過K nn計算PolSAR圖像中每個像素的抗噪因子,根據(jù)抗噪因子修改Ada Boost算法中的樣本權值更新策略。實驗采用了一組模擬PolSAR數(shù)據(jù)和五組真實PolSAR數(shù)據(jù),實驗結果表明,K nn.Ada Boost算法提高了AdaBoost的分類精度,具有很好的抗噪性能。2.在Knn.Ada Boost的工作基礎上,提出了一種基于Ada Boost的PolSAR圖像半監(jiān)督分類算法(Semi.Knn.AdaBoost)。在Knn.Ada Boost的框架下,引入Wishart距離度量,在每一次迭代結束時,根據(jù)有標記樣本計算獲得Wishart聚類中心,從預測標記中選擇距離Wishart聚類中心最近的若干個樣本,分別加入對應的類別進入下一次迭代。實驗采用一組模擬PolSAR數(shù)據(jù)和五組真實PolSAR數(shù)據(jù),結果表明,Semi.Knn.Ada Boost豐富了訓練樣本,分類正確率有一定的提升。3.在PolSAR圖像分類問題中,提出了一種基于集成學習的標簽噪聲水平預測方法EEL(Estimated by Ensemble Learning)。采用PolSAR圖像的相干矩陣中九個元素作為特征,利用不同的分類算法,學習得到相互獨立的分類器,用這些分類器分別對標記樣本分類,然后用多數(shù)投票和全投票的策略判定一個已標記樣本是否是噪聲,多數(shù)投票策略即對一個樣本的預測,如果超過半數(shù)分類器的分類結果是相同的,則認為這個已標記樣本不是噪聲,否則是噪聲;全投票策略只認定所有分類器投票結果相同時,此樣本才不是噪聲,否則是噪聲。實驗采用三組UCI數(shù)據(jù)和四組模擬的PolSAR數(shù)據(jù),結果表明,在標簽噪聲水平比較低時,此方法能夠正確的預測,而標簽噪聲水平比較高時,預測出的標簽噪聲水平則不是很準確。本文工作得到了國家自然科學基金(No.61173092)、新世紀優(yōu)秀人才支持計劃(No.66ZY110)和陜西省科學技術研究發(fā)展計劃項目(No.2013KJXX-64)資助。
[Abstract]:Polarimetric Synthetic Aperture Radarr (PolSAR) is a multi-parameter, multi-channel imaging radar system, which is widely used because of its advantages of all-weather, all-weather and high-resolution.The PolSAR image classification method based on machine learning has achieved high classification accuracy, but when there is label noise, the classification results will be greatly affected.Based on ensemble learning, this paper focuses on the problem of label noise in PolSAR image classification, including the following three aspects: 1.Based on the polarization parameters, scattering and texture features of PolSAR images, a supervised classification algorithm for PolSAR images based on AdaBoost is proposed.Using polarization parameters, polarization scattering features and texture features of PolSAR images, the Knn.Ada Boost algorithm is used as the input feature of Ada Boost to calculate the anti-noise factor of each pixel in PolSAR image.The sample weight updating strategy in Ada Boost algorithm is modified according to the anti-noise factor.A set of simulated PolSAR data and five groups of real PolSAR data are used in the experiment. The experimental results show that the K nn.Ada Boost algorithm improves the classification accuracy of AdaBoost and has a good anti-noise performance.Based on the work of Knn.Ada Boost, a semi-supervised PolSAR image classification algorithm based on Ada Boost is proposed.In the framework of Knn.Ada Boost, the Wishart distance metric is introduced. At the end of each iteration, the Wishart cluster center is obtained according to the calculation of labeled samples, and several samples closest to the Wishart cluster center are selected from the prediction markers.Add corresponding categories to the next iteration.A set of simulated PolSAR data and five groups of real PolSAR data are used in the experiment. The results show that Semi.Knn.Ada Boost enriches the training samples and improves the classification accuracy. 3.In the problem of PolSAR image classification, an ensemble learning based label noise prediction method, EEL(Estimated by Ensemble learning, is proposed.Using nine elements in the coherent matrix of PolSAR image as features and using different classification algorithms, independent classifiers are obtained, and these classifiers are used to classify the labeled samples respectively.Then the majority voting strategy is used to determine whether a marked sample is noisy or not, and the majority voting strategy is the prediction of a sample, if the classification results of more than half of the classifiers are the same.It is considered that the labeled sample is not noise, otherwise it is noise; if the voting result of all classifiers is the same, the sample is not noise, otherwise it is noise.Three groups of UCI data and four groups of simulated PolSAR data are used in the experiment. The results show that this method can correctly predict the label noise level when the label noise level is low, but the predicted label noise level is not very accurate when the label noise level is high.This work is supported by the National Natural Science Foundation No. 61173092, the New Century Talent support Program No. 66ZY110) and the Shaanxi Provincial Science and Technology Research and Development Program Project No. 2013KJXX-64).
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52

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