全極化SAR地物分類與極化方位角補償
發(fā)布時間：2018-11-27 09:23
【摘要】：全極化合成孔徑雷達（Synthetic Aperture Radar，，SAR）作為一種先進的遙感信息獲取手段，更加完整的記錄了目標的回波散射信息。全極化SAR分類結果既可為目標檢測、邊緣提取等進一步的分析或解譯提供輔助信息,也可作為最終結果。與普通遙感圖像相比，全極化SAR分類技術對于揭示地物極化散射信息更具有研究價值。 本文以提高全極化SAR數(shù)據(jù)的分類精度為主要目的,研究了全極化SAR圖像分類方法。 目前使用全極化SAR進行地物分類的方法主要有兩種：非監(jiān)督分類和監(jiān)督分類。大部分非監(jiān)督分類方法的優(yōu)點在于提供了用于指派最終地物類型的輔助信息；但每個聚類對應某種單一散射機制，并不能代表實際地物，因此在對大規(guī)模的遙感數(shù)據(jù)處理時必須依靠人工專家的參與解譯。監(jiān)督分類方法通�；谙袼鼗蛐^(qū)域得到底層特征，利用這些底層特征對具有單一散射機制的地物進行分類非常有效，但對復雜地物進行分類時會遇到困難。針對上述兩種問題，本文提出一種使用中間層特征MLF(Middle-Level-Feature)的監(jiān)督分類方法。即統(tǒng)計以某像素為中心的一定區(qū)域（矩形窗口）內各“中間成分（基于底層極化特征得到的非監(jiān)督聚類類別）”的占比作為該像素的MLF，依此計算所有位置像素的MLF，然后利用支持向量機進行監(jiān)督分類。本文在覆蓋武漢地區(qū)的Radarsat-2全極化數(shù)據(jù)上，與基于經典全極化特征的SVM（Support Vector Machine）監(jiān)督分類方法進行了對比，研究了不同中間成分獲取方法以及特征支持窗口對于分類性能的影響，結果顯示本文方法有很好的性能并有進一步提升的空間。 對于全極化SAR數(shù)據(jù)中眾多的極化信息，極化方位角反映了散射目標相對于雷達視線的旋轉角度，即方位向坡度。在極化SAR圖像分類中，同一類別地物目標所處方位向坡度的差異，體現(xiàn)在極化SAR數(shù)據(jù)中是極化特性的不同，將導致被分為不同的類別。為了消除這種由于地形因素造成的分類誤差，本文進行了極化方位角的補償，以改善極化SAR數(shù)據(jù)的分類結果。 本文利用DEM（Digital Elevation Model）估計出極化方位角并做了極化方位角補償。實驗發(fā)現(xiàn)，極化方位角補償后，經過極化補償之后，F(xiàn)reeman分解中體散射功率會減小、二次散射功率均減小、絕大多數(shù)像素的面散射分量也會減小，但是減小值大部分都在0附近。對于Cloude分解，極化方位角補償后，極化數(shù)據(jù)區(qū)分兩個相對較弱的散射分量的能力增強，同時散射介質的隨機性增強，代表散射過程物理機制的alpha值約60%的像素值減小。
[Abstract]:Fully polarized synthetic aperture radar (Synthetic Aperture Radar,SAR) as an advanced remote sensing information acquisition method, more complete recording of the target echo scattering information. The results of fully polarized SAR classification can not only provide auxiliary information for further analysis or interpretation, such as target detection, edge extraction and so on, but also can be used as final results. Compared with conventional remote sensing images, the fully polarized SAR classification technique is more valuable to reveal the polarimetric scattering information of ground objects. In order to improve the classification accuracy of fully polarized SAR data, a method of full polarization SAR image classification is studied in this paper. At present, there are two main methods for ground object classification using fully polarized SAR: unsupervised classification and supervised classification. The advantage of most unsupervised classification methods is that they provide auxiliary information for assigning final feature types. However, each cluster corresponds to a single scattering mechanism, which can not represent the real objects. Therefore, the interpretation of large-scale remote sensing data must rely on the participation of artificial experts. Supervised classification methods are usually based on pixels or small regions to obtain bottom features. Using these underlying features to classify objects with a single scattering mechanism is very effective, but it will be difficult to classify complex objects. In order to solve the above two problems, a supervised classification method using MLF (Middle-Level-Feature) is proposed. That is, the percentage of the "intermediate components (unsupervised clustering categories based on the underlying polarization feature) in a certain region (rectangular window) centered on a pixel is counted as the MLF, of the pixel. The MLF, of all the pixels is calculated accordingly." Then support vector machine is used for supervised classification. In this paper, the Radarsat-2 full polarization data covering Wuhan area are compared with the SVM (Support Vector Machine) supervised classification method based on classical full polarization features. The effects of different intermediate component acquisition methods and feature support windows on classification performance are studied. The results show that the proposed method has good performance and further improvement. For all polarimetric SAR data, the polarization azimuth reflects the rotation angle of the scattering target relative to the radar line of sight, that is, the azimuth slope. In the classification of polarimetric SAR images, the difference of azimuth gradient of the same ground object is reflected in the polarization characteristics of the polarimetric SAR data, which will lead to the classification of different categories. In order to eliminate the classification error caused by topographic factors, the polarization azimuth compensation is carried out to improve the classification results of polarized SAR data. In this paper, the polarization azimuth angle is estimated by DEM (Digital Elevation Model) and the polarization azimuth compensation is made. The experimental results show that after polarization compensation and polarization compensation, the volume scattering power and secondary scattering power in Freeman decomposition will decrease, and the surface scattering components of most pixels will also decrease, but most of the decreases are near zero. For Cloude decomposition, after polarization azimuth compensation, the ability of polarization data to distinguish two relatively weak scattering components is enhanced, and the randomness of scattering medium is enhanced, and the pixel value of alpha representing the physical mechanism of scattering process is reduced by about 60%.
【學位授予單位】：貴州師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52
本文編號：2360208
[Abstract]:Fully polarized synthetic aperture radar (Synthetic Aperture Radar,SAR) as an advanced remote sensing information acquisition method, more complete recording of the target echo scattering information. The results of fully polarized SAR classification can not only provide auxiliary information for further analysis or interpretation, such as target detection, edge extraction and so on, but also can be used as final results. Compared with conventional remote sensing images, the fully polarized SAR classification technique is more valuable to reveal the polarimetric scattering information of ground objects. In order to improve the classification accuracy of fully polarized SAR data, a method of full polarization SAR image classification is studied in this paper. At present, there are two main methods for ground object classification using fully polarized SAR: unsupervised classification and supervised classification. The advantage of most unsupervised classification methods is that they provide auxiliary information for assigning final feature types. However, each cluster corresponds to a single scattering mechanism, which can not represent the real objects. Therefore, the interpretation of large-scale remote sensing data must rely on the participation of artificial experts. Supervised classification methods are usually based on pixels or small regions to obtain bottom features. Using these underlying features to classify objects with a single scattering mechanism is very effective, but it will be difficult to classify complex objects. In order to solve the above two problems, a supervised classification method using MLF (Middle-Level-Feature) is proposed. That is, the percentage of the "intermediate components (unsupervised clustering categories based on the underlying polarization feature) in a certain region (rectangular window) centered on a pixel is counted as the MLF, of the pixel. The MLF, of all the pixels is calculated accordingly." Then support vector machine is used for supervised classification. In this paper, the Radarsat-2 full polarization data covering Wuhan area are compared with the SVM (Support Vector Machine) supervised classification method based on classical full polarization features. The effects of different intermediate component acquisition methods and feature support windows on classification performance are studied. The results show that the proposed method has good performance and further improvement. For all polarimetric SAR data, the polarization azimuth reflects the rotation angle of the scattering target relative to the radar line of sight, that is, the azimuth slope. In the classification of polarimetric SAR images, the difference of azimuth gradient of the same ground object is reflected in the polarization characteristics of the polarimetric SAR data, which will lead to the classification of different categories. In order to eliminate the classification error caused by topographic factors, the polarization azimuth compensation is carried out to improve the classification results of polarized SAR data. In this paper, the polarization azimuth angle is estimated by DEM (Digital Elevation Model) and the polarization azimuth compensation is made. The experimental results show that after polarization compensation and polarization compensation, the volume scattering power and secondary scattering power in Freeman decomposition will decrease, and the surface scattering components of most pixels will also decrease, but most of the decreases are near zero. For Cloude decomposition, after polarization azimuth compensation, the ability of polarization data to distinguish two relatively weak scattering components is enhanced, and the randomness of scattering medium is enhanced, and the pixel value of alpha representing the physical mechanism of scattering process is reduced by about 60%.
【學位授予單位】：貴州師范大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52
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本文編號：2360208
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