【摘要】：極化合成孔徑雷達(Polarimetric Synthetic Aperture Radar,PolSAR)由于能夠提供更多豐富的目標散射信息,近年來成為遙感領(lǐng)域的新熱點。其地物分類問題作為極化SAR圖像解譯的重要研究內(nèi)容和關(guān)鍵技術(shù),在民用和軍事領(lǐng)域具有重大的理論意義和應(yīng)用價值。支持向量機(Support Vector Machine,SVM)是統(tǒng)計學中一種有效的監(jiān)督分類方法,目前已廣泛應(yīng)用于多個領(lǐng)域,該方法在機器學習領(lǐng)域受到了重視和長足的發(fā)展。本文采用SVM算法對極化SAR地物分類問題進行研究,所取得的主要研究成果為:1.本文著重研究了支持向量機算法中的最小二乘支持向量機(LSSVM)算法,考慮到在利用LSSVM模型解決分類問題時,采用傳統(tǒng)的求解算法有如下弊端:計算復雜度高、求出的解不稀疏,模型受樣本噪聲的影響較大的問題,本文將模糊支持向量機與LSSVM的稀疏求解算法相結(jié)合,提出了模糊稀疏LSSVM算法,考慮到模糊隸屬度對模糊LSSVM的重要性,本文采取了兩種基于樣本到所屬類別類中心距的計算方法來度量模糊隸屬度,即基于歐式距離的度量方法和基于核距離的度量方法。并將提出的算法對極化SAR數(shù)據(jù)進行分類,從分類結(jié)果和對比實驗上看,提出的算法性能更強。2.對LSSVM的核函數(shù)進行了研究。由于在求解非線性分類問題時,首要的任務(wù)是選擇符合條件的核函數(shù),將樣本映射到高維空間,從而實現(xiàn)高維空間線性可分,因而核函數(shù)的選取是關(guān)鍵。常用的核函數(shù)為徑向基核函數(shù),但其在擬合較為復雜的函數(shù)時,得到的效果并不十分滿意,本文首先分析了支持向量機核函數(shù)的條件,然后將稀疏LSSVM分類器的核函數(shù)改為了符合核函數(shù)條件的Morlet小波核函數(shù),提出了一種小波核稀疏LSSVM算法,經(jīng)過對極化SAR數(shù)據(jù)分類的對比實驗證明,Morlet小波核稀疏LSSVM模型對極化SAR數(shù)據(jù)的分類精度更高。3.對提出的小波核稀疏LSSVM算法進行擴展。將該算法與深度SVM的網(wǎng)絡(luò)架構(gòu)相結(jié)合,提出了一種深度小波核稀疏LSSVM模型,模型主要設(shè)計了2個隱層單元,將低層的支持向量對應(yīng)的激活值作為高層的訓練樣本,依此訓練出深度小波核稀疏LSSVM分類器,考慮到算法的時間復雜度,將第二層的求解采用Lasso算法,依此構(gòu)建出深度Lasso模型,將深度Lasso算法和深度小波核稀疏LSSVM算法分別采用了UCI數(shù)據(jù)和極化SAR數(shù)據(jù)進行驗證。
[Abstract]:Polarimetric synthetic Aperture Radar (Polarimetric Synthetic Aperture) has become a new hotspot in the field of remote sensing due to its ability to provide more abundant target scattering information. As an important research content and key technology of polarimetric SAR image interpretation, the classification of ground objects has great theoretical significance and application value in civil and military fields. Support Vector Machine (SVM) is an effective supervised classification method in statistics, which has been widely used in many fields. In this paper, SVM algorithm is used to study the classification of polarimetric SAR ground objects. The main research results are as follows: 1. In this paper, the least squares support vector machine (LSSVM) algorithm in support vector machine (SVM) algorithm is studied. Considering the disadvantages of the traditional algorithm when using LSSVM model to solve the classification problem, the computational complexity is high, and the solution is not sparse. The model is greatly affected by the sample noise. In this paper, the fuzzy sparse LSSVM algorithm is proposed by combining the fuzzy support vector machine (FSVM) with the sparse solution algorithm of LSSVM, considering the importance of fuzzy membership to fuzzy LSSVM. In this paper, we adopt two methods to measure fuzzy membership based on the distance between the sample and the center of the class, that is, the Euclidean distance based metric method and the kernel distance based measurement method. The proposed algorithm is used to classify the polarimetric SAR data. The proposed algorithm has better performance from the classification results and comparative experiments. The kernel function of LSSVM is studied. When solving nonlinear classification problem, the most important task is to select the kernel function that meets the conditions, and map the sample to the high-dimensional space, thus realizing the linear separability of the high-dimensional space, so the selection of kernel function is the key. The commonly used kernel function is radial basis kernel function, but the result is not very satisfactory when fitting the more complex function. In this paper, the condition of kernel function of support vector machine is analyzed. Then, the kernel function of the sparse LSSVM classifier is changed to the Morlet wavelet kernel function which accords with the kernel function condition, and a wavelet kernel sparse LSSVM algorithm is proposed. The contrast experiment of polarized SAR data classification shows that the sparse LSSVM model based on Morlet wavelet kernel has higher classification accuracy for polarimetric SAR data. The proposed wavelet kernel sparse LSSVM algorithm is extended. Combining the algorithm with the network architecture of deep SVM, a sparse LSSVM model with deep wavelet kernel is proposed. Two hidden layer units are designed in the model, and the activation values corresponding to the support vectors in the lower layer are used as the training samples at the higher level. Based on this, the sparse LSSVM classifier with depth wavelet kernel is trained. Considering the time complexity of the algorithm, the Lasso algorithm is used to solve the second layer, and then the depth Lasso model is constructed. The deep Lasso algorithm and the deep wavelet kernel sparse LSSVM algorithm are validated by UCI data and polarized SAR data, respectively.
【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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