本文選題：變化檢測 + 協(xié)同分割��； 參考：《北京建筑大學(xué)》2017年碩士論文

【摘要】：地表覆蓋是指地球表面各種物質(zhì)類型及其自然屬性與特征的綜合體。航空航天技術(shù)的發(fā)展,實現(xiàn)了覆蓋全球的衛(wèi)星對地觀測。人們可以使用遙感衛(wèi)星數(shù)據(jù)來獲取大面積甚至全球的地表覆蓋信息,及時準(zhǔn)確的掌握地表覆蓋類型的分布及其變化情況。地表覆蓋遙感產(chǎn)品的研制成功,極大地方便了人們在氣候變化研究、生態(tài)環(huán)境監(jiān)測和可持續(xù)發(fā)展規(guī)劃等領(lǐng)域?qū)Φ乇砀采w信息的應(yīng)用。為了滿足人們對地表覆蓋信息使用的現(xiàn)勢性要求,學(xué)者們提出了多種變化檢測方法以從不同時相的遙感圖像中提取出變化信息來完成對地表覆蓋信息的更新。變化檢測方法的核心在于如何發(fā)現(xiàn)變化,測定變化范圍及變化屬性。傳統(tǒng)的變化檢測方法以圖像像元作為基本的處理單位,利用像元的光譜特征來構(gòu)建特征值,將閾值作為判斷變化與非變化的標(biāo)準(zhǔn),在這種情況下,即使同時使用多個特征值組合進(jìn)行判斷,也容易造成錯分誤差或者漏分誤差,影響變化檢測的精度。面向?qū)ο蟮淖兓瘷z測方法考慮了圖像的空間信息,將多個像元組成的同質(zhì)對象作為變化檢測的基本單位。如何確定不同地類的最佳分割尺度,如何確定不同時相的圖像對象的空間對應(yīng)性是變化檢測開始前需要解決的問題。面向?qū)ο蟮姆指罘椒ㄊ恰跋确指?后檢測”的模式,其對變化區(qū)域的判斷仍然主要依靠閾值來進(jìn)行。閾值選擇的準(zhǔn)確性是變化檢測精度的決定性因素。計算機(jī)視覺中的協(xié)同分割算法,能夠從同一場景的多視圖像中分割出相同或近似的目標(biāo)。該算法由于利用了圖像之間的聯(lián)系,因此能夠挖掘出更多的圖像信息。如果把土地覆蓋的變化過程看作自然界的運動,則土地覆蓋的變化檢測問題就可以看作運動圖像的協(xié)同分割問題�？紤]到基于像元和面向?qū)ο蟮淖兓瘷z測方法將閾值作為判斷變化/非變化的標(biāo)準(zhǔn),本文根據(jù)協(xié)同分割的思想,構(gòu)建了適用于變化檢測的能量函數(shù)。能量函數(shù)中包含的變化特征項以兩時相圖像共同的變化信息為基礎(chǔ),將基于像元和面向?qū)ο蠓椒ㄖ械拈撝禇l件作為判斷是否發(fā)生變化的先驗知識。同時為了更好的利用圖像的空間信息,能量函數(shù)中包含的圖像特征項能夠反映鄰域內(nèi)像元之間的相似性。本文利用基于圖割的方法來解決能量函數(shù)的最小化問題,將圖像映射為圖,將能量函數(shù)中的各項作為圖中不同類的邊的權(quán)重,使用最小割/最大流方法求得圖的最小割,從不同時相的圖像中分割出空間對應(yīng)的變化圖斑。本文創(chuàng)新點主要有:(1)根據(jù)變化檢測方法與計算機(jī)視覺中協(xié)同分割方法的共同特點,將變化信息特征值的閾值條件作為能量函數(shù)中變化特征項的先驗知識,改變了基于像元和面向?qū)ο蟮淖兓瘷z測方法中以閾值條件作為變化和非變化的唯一標(biāo)準(zhǔn)。(2)能量函數(shù)中的圖像特征項的構(gòu)建,綜合考慮鄰域內(nèi)像元對的光譜和紋理特征,通過對不同時相的圖像特征項賦予權(quán)重值,構(gòu)建綜合圖像特征項。根據(jù)不同圖像特征參與的分割結(jié)果,不僅能夠提取出變化位置和區(qū)域,也能夠?qū)缀螌傩赃M(jìn)行判斷。
[Abstract]:Surface coverage refers to a variety of material types and their natural properties and characteristics of the earth's surface. The development of Aeronautics and Astronautics has achieved global satellite observation. People can use remote sensing satellite data to obtain large area and global surface coverage information, and to accurately grasp the distribution of surface cover types. The application of surface coverage information in the fields of climate change research, ecological environment monitoring and sustainable development planning. In order to meet the potential requirements for people to use the surface coverage information, a variety of change detection methods have been put forward by scholars. The core of the change detection method is how to find the change, determine the change range and the change attribute. The traditional change detection method takes the image pixel as the basic processing unit, and uses the spectral characteristics of the pixel to construct the eigenvalues, and the threshold value is used. As a criterion for judging change and non change, in this case, even using multiple eigenvalues at the same time, it is easy to cause error or leakage error and affect the accuracy of change detection. The object oriented change detection method considers the spatial information of the image, and makes the homogeneous objects composed of multiple pixels as the change. The basic unit of detection. How to determine the best segmentation scale of different classes and how to determine the spatial correspondence of the image objects in different phases is a problem that needs to be solved before the beginning of change detection. The object oriented segmentation method is the model of "first segmentation and post detection", and the judgment of the changing region is still mainly based on the threshold. The accuracy of the threshold selection is the decisive factor of the change detection precision. The cooperative segmentation algorithm in computer vision can divide the same or approximate target from the multi view image of the same scene. The algorithm can excavate more image information because of the connection between the images. As the process is regarded as the movement of nature, the problem of change detection of land cover can be regarded as the problem of synergetic segmentation of motion images. Considering the change detection method based on pixel and object oriented change detection method as the criterion for judging change / non change, this paper constructs the energy function suitable for change detection according to the idea of cooperative segmentation. The variation feature contained in the energy function is based on the common change information of the two phase image. The threshold condition based on the pixel and the object oriented method is used as the prior knowledge to judge whether the change is occurring. In order to better use the spatial information of the image, the image feature included in the energy function can reflect the neighborhood. In this paper, a graph cut method is used to solve the minimization of the energy function, and the image is mapped into a graph. The minimum cut of the graph is obtained by using the minimum cut / maximum flow method in the energy function as the weight of the edges of the different classes in the graph. The spatial corresponding change map is segmented from the images of different phases. The main innovations of this paper are as follows: (1) according to the common characteristics of the change detection method and the cooperative segmentation method in the computer vision, the threshold condition of the change of the characteristic value of the information is taken as the prior knowledge of the change feature in the energy function, and the threshold condition is changed and non variable in the variational detection method based on the pixel and the object oriented. The only standard of the transformation. (2) the construction of the image feature item in the energy function, considering the spectral and texture features of the pixel pair in the neighborhood, and building a comprehensive image feature item by giving weight values to the image feature items of different phase. Enough to judge the geometric properties.
【學(xué)位授予單位】：北京建筑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP751

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