【摘要】：論文通過對(duì)ALOSPALSAR雙極化SAR數(shù)據(jù)特征及其地物散射機(jī)理研究，利用極化目標(biāo)分解方法提取的特征參數(shù)進(jìn)行雙極化SAR影像分類處理，以達(dá)到提高極化SAR影像分類精度的目的。較光學(xué)數(shù)據(jù)和傳統(tǒng)雷達(dá)數(shù)據(jù)而言，極化SAR數(shù)據(jù)不僅包括了幅度信息，也帶有相位信息，因此該數(shù)據(jù)以每個(gè)分辨率單元不同極化組合狀態(tài)記錄了豐富的地物后向散射信息�；跇O化雷達(dá)具有不受晝夜云層影響、能穿透植被和淺層地表、多波段和多極化、高分辨率有源成像的特點(diǎn)，極化SAR雷達(dá)在城市規(guī)劃與變遷、農(nóng)作物生長(zhǎng)、地質(zhì)體與地質(zhì)現(xiàn)象(隱伏)、地質(zhì)災(zāi)害等監(jiān)測(cè)與制圖方面具有獨(dú)特的優(yōu)勢(shì)。 由于地物目標(biāo)所在的位置、表面幾何結(jié)構(gòu)和介電性不同，極化SAR接收的回波具有復(fù)雜的散射過程，在分析極化SAR成像機(jī)理時(shí),一些表征地物屬性的參數(shù)必須從這些復(fù)雜的散射回波中提取出來，目標(biāo)分解方法由此應(yīng)運(yùn)而生。論文著眼于揭示極化SAR目標(biāo)分解提取各參數(shù)所代表的地物散射機(jī)理，以及提高雙極化SAR圖像分類精度兩方面，對(duì)長(zhǎng)白山地區(qū)進(jìn)行地物分類研究，取得了以下成果： 1．本研究選用的雙極化SAR影像存在數(shù)據(jù)壓縮和斑點(diǎn)噪聲現(xiàn)象，為保證地物信息提取的精度，對(duì)該影像數(shù)據(jù)進(jìn)行了一系列預(yù)處理。通過分析相干斑統(tǒng)計(jì)特性和噪聲模型，，結(jié)合雙極化SAR影像特點(diǎn)，首先對(duì)ALOSPALSAR雙極化數(shù)據(jù)進(jìn)行多視處理，提高了極化SAR圖像的輻射分辨率；然后分別采用Boxcar、Lee-sigma和增強(qiáng)型Lee三種濾波算法對(duì)多視處理后的圖像做降噪對(duì)比分析，各濾波算法均能起到降噪作用，而增強(qiáng)型Lee濾波最能有效抑制斑點(diǎn)噪聲、保持空間分辨率和極化信息，是一種高性能、優(yōu)質(zhì)的濾波方法。 2．通過對(duì)常規(guī)雷達(dá)數(shù)據(jù)分別采用ML和SVM兩種具有代表性的算法進(jìn)行影像分類對(duì)比研究。與ML的分類算法相比，SVM法提高了地物分類的準(zhǔn)確率，驗(yàn)證了分類器的選擇直接影響著極化SAR圖像分類質(zhì)量。 3．極化特征參數(shù)提取。通過對(duì)雙極化SAR數(shù)據(jù)的相干矩陣進(jìn)行Cloude目標(biāo)分解，提取了反映目標(biāo)散射機(jī)理的4個(gè)特征參數(shù)，分析表明4個(gè)參數(shù)表征了不同散射機(jī)制下的地物散射信息和物理意義，為后續(xù)基于目標(biāo)分解的極化SAR影像分類提供了有效的特征參數(shù)集。這是本論文的特色。 4．基于Cloude目標(biāo)分解雙極化SAR影像分類算法的實(shí)現(xiàn)。由于目標(biāo)分解后得到的特征參數(shù)具有較為明確的物理意義，將目標(biāo)分解技術(shù)應(yīng)用到極化SAR圖像分類研究具有高效、可行性強(qiáng)的優(yōu)勢(shì)。論文利用目標(biāo)分解后得到的特征參數(shù)與高性能的SVM分類器相結(jié)合，對(duì)雙極化SAR影像進(jìn)行地物分類算法的實(shí)現(xiàn)。結(jié)果表明，較常規(guī)雷達(dá)數(shù)據(jù)的圖像分類而言，基于目標(biāo)分解的雙極化SAR影像分類精度更高，其各類地物均能準(zhǔn)確的分離出來。極化目標(biāo)分解方法可成為雙極化SAR圖像分類的一種有效的技術(shù)手段。
[Abstract]:Based on the study of the characteristics of ALOSPALSAR bipolar SAR data and the scattering mechanism of ground objects, the feature parameters extracted by the polarimetric target decomposition method are used to classify and process the dual-polarized SAR images in order to improve the classification accuracy of the polarimetric SAR images. Compared with optical data and traditional radar data, polarimetric SAR data not only includes amplitude information, but also has phase information, so the data record abundant backscattering information in different polarimetric states of each resolution unit. Based on the characteristics of polarimetric radar, which is not affected by day and night clouds, can penetrate vegetation and shallow surface, multi-band and multi-polarization, high-resolution active imaging, polarimetric SAR radar in urban planning and change, crop growth, Geological bodies and geological phenomena (hidden), geological hazards and other aspects of monitoring and mapping have unique advantages. Due to the location of the object, the surface geometry and dielectric properties, the echo received by polarized SAR has a complex scattering process. When analyzing the imaging mechanism of polarimetric SAR, Some parameters representing the properties of objects must be extracted from these complex scattering echoes, and the target decomposition method emerges as the times require. This paper focuses on revealing the scattering mechanism of ground objects represented by the extraction parameters of polarimetric SAR targets and improving the classification accuracy of dual-polarized SAR images, and studies the ground objects classification in Changbai Mountain area. The results are as follows: 1. The dual-polarization SAR image in this study has the phenomenon of data compression and speckle noise. In order to ensure the accuracy of information extraction, a series of preprocessing of the image data is carried out. By analyzing the statistical characteristics of speckle and the noise model, combining the characteristics of dual-polarized SAR images, the multi-view processing of ALOSPALSAR dual-polarization data is carried out, which improves the radiative resolution of polarized SAR images. Then, three filtering algorithms, Boxcar,Lee-sigma and enhanced Lee, are used to compare and analyze the noise reduction of the multi-view image. Each filtering algorithm can reduce the noise, and the enhanced Lee filter is the most effective to suppress speckle noise. Keeping spatial resolution and polarization information is a high performance and high quality filtering method. 2. The conventional radar data are classified by ML and SVM respectively. Compared with ML's classification algorithm, SVM improves the accuracy of ground object classification, and verifies that the choice of classifier directly affects the classification quality of polarimetric SAR images. 3. Extraction of polarization characteristic parameters. Through the Cloude target decomposition of the coherent matrix of dual-polarized SAR data, four characteristic parameters reflecting the scattering mechanism of the target are extracted. The analysis shows that the four parameters represent the scattering information and physical significance of the ground objects under different scattering mechanisms. It provides an effective feature parameter set for polarimetric SAR image classification based on target decomposition. This is the characteristic of this thesis. 4. Realization of dual-polarization SAR image classification algorithm based on Cloude target decomposition. Because the characteristic parameters obtained by target decomposition have definite physical significance, it is effective and feasible to apply target decomposition technology to the classification of polarimetric SAR images. In this paper, the feature parameters obtained from the target decomposition are combined with the high performance SVM classifier to realize the ground object classification algorithm for the dual-polarization SAR images. The results show that the classification accuracy of dual-polarization SAR images based on target decomposition is higher than that of conventional radar data, and all kinds of ground objects can be separated accurately. The polarimetric target decomposition method can be used as an effective technique for the classification of bipolar SAR images.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P225.1;TP391.41

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