本文選題：變化檢測　切入點：合成孔徑雷達(SAR)　出處：《西安電子科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：遙感圖像變化檢測是通過對不同時間獲得的覆蓋同一區(qū)域的兩幅或多幅遙感圖像進行觀測分析,對比得到圖像之間的差異,進而檢測出該地區(qū)的地物隨時間發(fā)生的變化信息。目前圖像變化檢測算法的研究方法一般流程是,首先生成兩時相遙感圖像的差異圖像,然后對差異圖像進行分析處理,將差異圖像分成變化類和未變化類兩類,得到最終的檢測結(jié)果。聚類就是其中對差異圖進行分類的方法之一。本文針對已有聚類技術(shù)在圖像分割中的不足,提出了一種無監(jiān)督的基于于非支配鄰域免疫算法的多目標(biāo)模糊聚類算法和選擇性集成策略的SAR圖像變化檢測算法:1.首先,提出了一種多目標(biāo)聚類的差異圖分析算法。算法設(shè)計兩個互補的聚類目標(biāo)函數(shù)評價聚類性能,目標(biāo)二中引入鄰域像素與中心點像素之間的灰度差和歐式空間距離加權(quán)作為聚類算法的相似性度量,在聚類過程中結(jié)合空間鄰域信息。算法相比于傳統(tǒng)的單目標(biāo)聚類算法,可以更好地去除斑點噪聲對聚類結(jié)果的影響,又不會造成細(xì)節(jié)的丟失。同時由于建立兩個目標(biāo),避免了參數(shù)選擇困難的問題。實現(xiàn)了在圖像分割或分類過程中既保持細(xì)節(jié)的完整增強了聚類性能,同時抑制斑點噪聲的目標(biāo)。使用的進化多目標(biāo)方法用隨機產(chǎn)生的初始抗體種群代替初始的聚類中心,降低了傳統(tǒng)聚類分割方法對初始聚類中心的敏感度,使用單一目標(biāo)進行聚類運行多次才能生成不同的解,利用多目標(biāo)優(yōu)化方法運行一代即可得到。2.本文引入選擇性集成策略,將多目標(biāo)聚類的結(jié)果視為不同權(quán)值的同態(tài)分類器分類的結(jié)果,對初步分類的結(jié)果進行選擇性集成,得到比單個聚類更好的結(jié)果。本文所提出的選擇性集成策略,首先將所有聚類結(jié)果進行簡單的投票集成;然后根據(jù)一次集成的結(jié)果作為評判標(biāo)準(zhǔn),對各基分類器進行排序;排序后,選擇前10%~30%進行集成,最終獲得一組整合的分割結(jié)果。使用多目標(biāo)優(yōu)化聚類產(chǎn)生的結(jié)果是一組非支配解集,得到的是一組聚類中心,由聚類中心可以得到不同的分割結(jié)果。從多目標(biāo)優(yōu)化角度來講,結(jié)果之間相互支配,沒有優(yōu)劣性可言。實際應(yīng)用時,可以根據(jù)實際需要或偏好來選擇其中一個解。其他大多數(shù)的多目標(biāo)聚類算法是采用第三方選解策略來進行選解,其實質(zhì)相當(dāng)增加聚類目標(biāo)。另外一些選解策略則需假設(shè)10%的實驗數(shù)據(jù)已知,這對于圖像聚類分割并不實際。而使用選擇性集成策略進行處理,無需先驗知識或第三方指標(biāo)。最后的實驗結(jié)果表明,相比現(xiàn)有的其他辦法,本文所采用的方法整體取得較好的變化檢測差異圖分割結(jié)果。
[Abstract]:Remote sensing image change detection is obtained through observation and Analysis on different time covering the same region of the two or more images of remote sensing images, comparing the difference between the images, and then detect the change information of the region features over time. The current image change detection method is the general process is to first generate two when the phase difference between remote sensing image, and then the difference of image analysis and processing, the difference image is divided into change and no change two classes, to get the final detection results. The difference of graph clustering is one way of classifying. Aiming at the shortcomings of the existing clustering techniques in image segmentation, proposed a SAR the change of image based on unsupervised multi target nondominated neighbor immune algorithm of fuzzy clustering algorithm and selective ensemble strategy detection algorithm: 1. first, this paper presents a multi-objective Algorithm analysis of the difference graph clustering algorithm design two clustering objective function evaluation of the complementary clustering performance, the second goal is introduced between the gray and neighborhood pixels in the center pixel difference and the Euclidean distance as the similarity weighted clustering algorithm with spatial neighborhood information measure, in the process of clustering. The single objective algorithm compared with the traditional clustering algorithm, can better remove the influence of speckle noise on the clustering results, and will not cause the loss of details. At the same time due to the establishment of two goals, to avoid the difficulty of parameter selection problem. The image segmentation and classification process can keep the full details to improve the clustering performance, while suppressing speckle noise. The evolution of multi target the target used by the initial cluster center instead of the initial antibody population randomly generated, reducing the traditional clustering segmentation method of initial clustering center sensitivity Sensitivity, using a single target clustering operation several times to generate different solutions, using multi-objective optimization method to run a generation of.2. can be obtained by selective integration strategy, will result in multi objective clustering results as the classification of different weights of the homomorphic classifiers, the preliminary classification results of selective integration than single clustering better results. Selective integration strategy proposed in this paper, firstly all clustering results are simple voting integration; then according to the results of integrated as a judgment standard, for each base classifier in order; after sorting, selection of 10%~30% before integration, eventually obtain a set of integration results using multi target segmentation results. The optimal clustering is a set of non dominated solutions, get a set of cluster centers, get different segmentation results by the clustering center. From the angle of multi-objective optimization The degree of speaking, mutual control between results, no merits at all. In practical application, can choose one of the solutions according to the actual needs or preferences. Most of the other multi-objective clustering algorithm is used to select third party solution strategies to select the solution, in fact a considerable increase in poly target class. In addition some experimental data in solution selection strategy you need to assume that the 10% known, this is not practical. For image clustering segmentation using selective ensemble method for processing, without prior knowledge or third indicators. The experimental results show that, compared with other existing methods, this method has better overall results to detect the changes of image segmentation.

【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

【參考文獻】

相關(guān)期刊論文 前1條

1 李金基;焦李成;張向榮;楊咚咚;;基于融合和T-分布的SAR圖像水災(zāi)變化檢測[J];計算機研究與發(fā)展;2011年02期

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本文編號：1613327