【摘要】：合成孔徑雷達(SAR)因為獨特的成像方式使得其具有全天時、全天候等特點。隨著近年來SAR傳感器的不斷發(fā)展,高分辨率SAR圖像處理逐漸成為了SAR領(lǐng)域的研究熱點。圖像的分割和分類一直以來都是圖像解譯的關(guān)鍵步驟,而SAR圖像因其固有的相干斑噪聲以及高分辨率下的同譜異質(zhì)現(xiàn)象使得SAR圖像自動解譯非常困難�；谙嗨贫认拗茀^(qū)域合并的SAR圖像分割算法為高分辨率下的SAR圖像分析提供了新思路�；趨^(qū)域的方法可以有效減少SAR圖像固有的相干斑噪聲對分割的影響,通過對相似度進行限制可以有效地實現(xiàn)SAR圖像的多層次分割,以滿足不同尺度的分割需求�；趨^(qū)域的分割結(jié)果進行分類,可以有效地減少相干斑噪聲的影響,將分割后的區(qū)域作為基本的分類單元,可以引入?yún)^(qū)域的紋理、結(jié)構(gòu)以及尺寸形狀等信息進行分析,有效的提高SAR圖像的分類精度。 本文對遙感圖像中的分割方法進行總結(jié)和研究,結(jié)合SAR圖像的特點,提出并設(shè)計了一種魯棒性高的適用于不同SAR圖像的分割算法,并且將其引入到全極化SAR圖像分割當(dāng)中,最后基于單極化和全極化的分割結(jié)果進行SAR圖像分類。本文的主要工作如下： (一)論文回顧和總結(jié)了SAR圖像的分割方法和分類方法,指出了基于區(qū)域合并分割算法的優(yōu)勢,以及其在SAR圖像分類中應(yīng)用的可行性。 (二)改進了基于區(qū)域合并的分割算法。首先利用形態(tài)學(xué)分水嶺算法獲得邊緣保持良好的過分割區(qū)域,在過分割區(qū)域的基礎(chǔ)上進行相鄰區(qū)域相似性度量,度量的方法在傳統(tǒng)灰度均值的基礎(chǔ)上引入了統(tǒng)計模型度量的方法。然后引入了最鄰近圖對SAR過分割區(qū)域進行表示,通過邊界像素進行各個區(qū)域鄰域的計算并存儲。區(qū)域合并采用的是全局最優(yōu)的合并方式,以保證獲得的最終結(jié)果是全局最佳的。在合并過程中每一次的區(qū)域合并都要將鄰接表進行一次更新。每次只對一個區(qū)域合并,無需對所有區(qū)域的鄰域通過邊界像素進行更新,從而加快區(qū)域合并速度。對相似度進行限制可實現(xiàn)SAR圖像的多尺度分割。將相似度限制合并完成的分割結(jié)果進行后處理,采用全局最優(yōu)的合并方式將尺寸小于某一限定區(qū)域的小區(qū)域進行合并,得到最終的分割結(jié)果。最后將分割結(jié)果進行分析,并與常用的SAR圖像分割方法進行比較,驗證算法的有效性。 (三)在區(qū)域合并的分割方法基礎(chǔ)上,實現(xiàn)基于SVM分類器的單極化和全極化的SAR圖像分類。有別于傳統(tǒng)的基于像素的SAR圖像分類方法,基于區(qū)域的分類算法可以有效降低相干斑噪聲的影響。利用SVM分類方法結(jié)合單極化和全極化的分類特征進行分類,在全極化SAR圖像分類中引入了多特征組合的方法來提高分類精度。最后將分類結(jié)果與基于像素的SAR圖像分類結(jié)果進行比較,可以發(fā)現(xiàn)基于區(qū)域合并分割結(jié)果的SAR圖像分類比傳統(tǒng)的基于像素的分類結(jié)果具有更高的分類精度。
[Abstract]:Synthetic Aperture Radar (SAR) (SAR) has the characteristics of all-day, all-weather and so on because of its unique imaging mode. With the development of SAR sensors, high resolution SAR image processing has become a hotspot in the field of SAR. Image segmentation and classification have always been the key steps of image interpretation, but SAR images are very difficult to interpret automatically because of their inherent speckle noise and homospectral heterogeneity at high resolution. The SAR image segmentation algorithm based on similarity constrained region merging provides a new idea for SAR image analysis with high resolution. The region-based method can effectively reduce the effect of the inherent speckle noise on the segmentation of SAR images. By limiting the similarity, the multi-level segmentation of SAR images can be realized effectively to meet the needs of different scales of segmentation. Classification based on the segmentation results can effectively reduce the effect of speckle noise. The segmented region can be used as the basic classification unit, and the texture, structure, size and shape of the region can be analyzed. The classification accuracy of SAR image is improved effectively. In this paper, the segmentation methods in remote sensing images are summarized and studied. According to the characteristics of SAR images, a robust segmentation algorithm suitable for different SAR images is proposed and designed, and it is introduced into the segmentation of fully polarized SAR images. Finally, SAR images are classified based on the segmentation results of single polarization and full polarization. The main work of this paper is as follows: (1) the paper reviews and summarizes the segmentation methods and classification methods of SAR images, points out the advantages of region merging segmentation algorithm and the feasibility of its application in SAR image classification. (2) the segmentation algorithm based on region merging is improved. Firstly, the morphological watershed algorithm is used to obtain a well-maintained edge over-segmented region, and the similarity of adjacent regions is measured on the basis of over-segmented region. The method of statistical model measurement is introduced based on the traditional gray mean. Then the nearest neighbor graph is introduced to represent the SAR over-segmented region, and the boundary pixels are used to calculate and store the neighborhood of each region. The region merging adopts the global optimal merging method to ensure the final result is the global optimal. In the process of merging each region merge must update the adjacent table once. Only one region is merged at a time without updating all the neighborhood pixels through the boundary so as to accelerate the speed of region merging. The multi-scale segmentation of SAR images can be realized by limiting the similarity. After processing the segmentation result which is completed by similarity restriction merging, the final segmentation result is obtained by using the global optimal merging method to merge the small area smaller than a certain limited region. Finally, the segmentation results are analyzed and compared with the common SAR image segmentation methods to verify the effectiveness of the algorithm. (3) based on the segmentation method of region merging, SAR image classification based on single polarization and full polarization is realized based on SVM classifier. Different from the traditional pixel based SAR image classification algorithm, the region-based classification algorithm can effectively reduce the effect of speckle noise. The SVM classification method is used to combine the single and full polarization classification features, and the multi-feature combination method is introduced to improve the classification accuracy in the full polarization SAR image classification. Finally, by comparing the classification results with the pixel based SAR image classification results, it can be found that the classification accuracy of the SAR image based on region merging segmentation is higher than that of the traditional pixel based classification results.
【學(xué)位授予單位】：北京交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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