【摘要】：極化合成孔徑雷達(Polarimetric Synthetic Aperture Radar,簡稱極化SAR)是一種先進的獲取遙感信息的手段,通過測量地面每一個分辨單元在四種不同的極化組合下的散射特性,從而得到目標對應的極化信息,極化SAR比傳統(tǒng)單極化SAR所記錄的地物目標電磁散射特征信息更完整。極化SAR分類的意義重要在于它既能作為一個中間步驟,為極化SAR的解譯提供幫助,協(xié)助極化SAR圖像提取邊緣信息、檢測目標、識別目標,也可能是用戶最終需求。傳統(tǒng)的極化SAR分類均是以單個像素為分類單元,而極化SAR特有的相干斑噪聲對以像素為分類單元的分類結(jié)果影響很大,因此本文構(gòu)造包含點和含權值的邊的全連圖,對極化SAR數(shù)據(jù)先進行過分割,減小相干斑噪聲對分類的影響,然后以分割后的區(qū)域為分類單元,結(jié)合數(shù)據(jù)的極化特征和結(jié)構(gòu)信息實現(xiàn)對極化SAR數(shù)據(jù)的分類研究,文章主要包含了以下三方面的內(nèi)容:1.提出一種基于圖方法的極化SAR分割方法。該方法中先提取像素點的極化特征,結(jié)合極化SAR數(shù)據(jù)的Wishart距離構(gòu)建圖,然后基于圖對極化SAR數(shù)據(jù)進行初始分割,最后對基于圖方法初始分割后的區(qū)域進行一個分層合并,按照區(qū)域大小的等級設定不同的合并策略,得到一個相對均勻的分割結(jié)果。該算法引入了應用在自然圖像上的分割算法圖方法,結(jié)合極化SAR數(shù)據(jù)的特點改進權值的計算方法,合并過程中考慮了像素的空間信息,思路簡明,便于理解。2.提出一種基于圖方法過分割的極化SAR有監(jiān)督分類方法。該方法利用了上面介紹的分割方法得到分割結(jié)果,以過分割后的區(qū)域為分類單元,利用Wishart距離計算每個區(qū)域與各個訓練類別之間的距離,對每個區(qū)域進行類別劃分。該分類方法為基于區(qū)域的有監(jiān)督分類,減小了傳統(tǒng)分類結(jié)果中出現(xiàn)雜點的情況,并且提高了分類結(jié)果的區(qū)域一致性,而且提高了極化SAR的分類精度。3.提出一種基于圖方法過分割以及改進的Wishart距離的極化SAR二分樹分類方法。該方法同上面提到的有監(jiān)督分類方法一樣,以圖方法分割得到的區(qū)域為分類單元,計算每兩個區(qū)域之間的不相似度構(gòu)建二分樹,最終得到的分類樹的個數(shù)即類別個數(shù)。該方法中用到的不相似度計算方式為改進的Wishart距離,考慮到區(qū)域的尺寸大小。該方法減少了分類結(jié)果中出現(xiàn)局部收斂的情況,減小了一般無監(jiān)督方法中出現(xiàn)雜點的現(xiàn)象,且提升了分類精度。
[Abstract]:Polarization synthetic Aperture Radar (Polarimetric Synthetic Aperture Radar,) is an advanced method to obtain remote sensing information. The scattering characteristics of each resolution unit on the ground under four different polarization combinations are measured. Thus, the polarization information corresponding to the target is obtained, and the electromagnetic scattering characteristic information of the ground object recorded by the polarization SAR is more complete than that recorded by the traditional unipolar SAR. The significance of polarization SAR classification is that it can be used as an intermediate step to help the interpretation of polarization SAR, assist polarization SAR images to extract edge information, detect targets, identify targets, and may also be the final requirements of users. The traditional polarization SAR classification takes a single pixel as the classification unit, and the speckle noise unique to the polarization SAR has a great influence on the classification results with pixels as the classification unit. Therefore, this paper constructs a fully connected graph containing points and edges with weights. Firstly, the polarimetric SAR data is segmented to reduce the influence of speckle noise on the classification, and then the polarimetric SAR data classification is realized by taking the segmented region as the classification unit and combining the polarization characteristics and structural information of the data. The article mainly contains the following three aspects: 1. A polarization SAR segmentation method based on graph method is proposed. In this method, the polarization features of pixels are extracted, and the Wishart distance of polarized SAR data is combined to construct the graph, and then the polarized SAR data is initially segmented based on the graph. Finally, a hierarchical merging of the regions after the initial segmentation based on the graph method is carried out. Different merging strategies are set according to the level of region size, and a relatively uniform segmentation result is obtained. The algorithm introduces the graph method of segmentation algorithm applied to natural images, and improves the calculation method of weights according to the characteristics of polarized SAR data. The spatial information of pixels is considered in the process of merging, and the train of thought is simple and easy to understand. 2. A polarization SAR supervised classification method based on graph method is proposed. In this method, the segmentation results are obtained by using the segmentation method described above. Taking the over-segmented region as the classification unit, the distance between each region and each training category is calculated by using Wishart distance, and each region is classified. The classification method is region-based supervised classification, which reduces the occurrence of miscellaneous points in the traditional classification results, improves the regional consistency of the classification results, and improves the classification accuracy of polarized SAR. 3. A polarization SAR binary tree classification method based on graph method oversegmentation and improved Wishart distance is proposed. This method is the same as the supervised classification method mentioned above, taking the region segmented by graph method as the classification unit, and calculating the dissimilarity between each two regions to construct the binary tree, and the number of classification trees is the number of categories. The dissimilarity calculation method used in this method is the improved Wishart distance, which takes into account the size of the region. This method reduces the local convergence in the classification results, reduces the phenomenon of miscellaneous points in the general unsupervised method, and improves the classification accuracy.
【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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