本文關(guān)鍵詞： 變化檢測(cè) 非負(fù)矩陣分解 聚類 圖像融合 紋理特征 灰度共生矩陣 主成分分析　出處：《西南交通大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：遙感圖像的變化檢測(cè)是通過(guò)對(duì)同一地區(qū)不同時(shí)期中兩幅或多幅遙感圖像進(jìn)行比較分析,得到圖像之間變化信息的一項(xiàng)遙感技術(shù)的應(yīng)用,它目前已廣泛地應(yīng)用于經(jīng)濟(jì)和國(guó)防建設(shè)等諸多領(lǐng)域。 非負(fù)矩陣分解(Nonnegative Matrix Factorization, NMF)算法是國(guó)際上新近提出的一種矩陣分解方法,是一種很重要的矩陣降維技術(shù)。NMF的應(yīng)用領(lǐng)域十分廣泛,如圖像處理,計(jì)算機(jī)視覺(jué),文本分析等。本文嘗試將NMF運(yùn)用于遙感圖像的變化檢測(cè)當(dāng)中。主要包括以下三個(gè)內(nèi)容： (1)NMF對(duì)差異圖的融合。一般的變化檢測(cè)常常以單一的差異圖作為研究對(duì)象,而一幅差異圖往往存在局限性,針對(duì)這個(gè)問(wèn)題本文提出一種使用NMF融合的變化檢測(cè)方法,將SAR圖像的對(duì)數(shù)比值圖與MRD算子圖融合,將光學(xué)圖像的差值圖與t檢驗(yàn)圖融合。通過(guò)仿真實(shí)驗(yàn)得到了較好的效果,并與現(xiàn)有的幾種變化檢測(cè)方法對(duì)比,驗(yàn)證了該方法的有效性。 (2)基于重點(diǎn)關(guān)注區(qū)域的變化檢測(cè)。為了降低噪聲干擾引起的虛警率,以及變化信息幅度弱引起的漏檢率,本文采用確定重點(diǎn)關(guān)注區(qū)域的方法來(lái)做變化檢測(cè)。首先運(yùn)用灰度共生矩陣產(chǎn)生差異圖的紋理圖像,由于方差紋理能凸顯變化區(qū)域邊界且可分性較強(qiáng),我們采用方差紋理圖來(lái)為重點(diǎn)關(guān)注區(qū)域的確定做鋪墊。利用NMF提取紋理圖的背景特征,通過(guò)計(jì)算特征圖與紋理圖中每個(gè)像素鄰域塊的歐氏距離,從而弱化紋理背景。然后將原差異圖與該圖像對(duì)應(yīng)像素相乘,得到較理想的變化輪廓顯著圖。將變化輪廓圖通過(guò)聚類,并膨脹填充內(nèi)部區(qū)域從而得到重點(diǎn)關(guān)注區(qū)域。最后,根據(jù)重點(diǎn)關(guān)注區(qū)域修正原差異圖,通過(guò)對(duì)修正后差異圖的處理得到了較理想的變化檢測(cè)結(jié)果。 (3)研究了NMF聚類的特性�？紤]到差異圖存有噪聲的問(wèn)題,運(yùn)用雙邊濾波對(duì)差異圖濾波,通過(guò)對(duì)像素的二階鄰域掃描得到每個(gè)像素的特征向量,然后采用PCA降維,最后利用Semi-NMF聚類得到最終變化檢測(cè)結(jié)果。通過(guò)與本文前面兩種算法的比較可以看到該算法具有很高的檢測(cè)精度,并且通過(guò)與K-means聚類結(jié)果的對(duì)比,體現(xiàn)出Semi-NMF聚類的準(zhǔn)確性。
[Abstract]:The change detection of remote sensing image is an application of remote sensing technology by comparing and analyzing two or more remote sensing images in different periods in the same area. It has been widely used in many fields, such as economy and national defense construction. Nonnegative Matrix factorization (NMF) algorithm is a recently proposed matrix decomposition method in the world. It is a very important matrix dimension reduction technology. NMF is widely used in many fields, such as image processing, computer vision, etc. Text analysis and so on. This paper tries to apply NMF in remote sensing image change detection. General change detection often takes a single difference map as the object of study, but a difference map often has some limitations. In this paper, a change detection method using NMF fusion is proposed in this paper. The logarithmic ratio graph of SAR image is fused with MRD operator graph, and the difference graph of optical image is fused with t-test graph. A good result is obtained by simulation experiment, and the validity of this method is verified by comparing it with several existing change detection methods. In order to reduce false alarm rate caused by noise interference and miss detection rate caused by weak amplitude of change information, In this paper, the method of determining the focus area is used to detect the change. Firstly, the grayscale co-occurrence matrix is used to produce the texture image of the difference map, because the variance texture can highlight the boundary of the changing region and has strong separability. We use variance texture map to lay the groundwork for determining the region of focus. We use NMF to extract the background features of texture map and calculate the Euclidean distance between each pixel neighborhood block in the feature map and texture map. In order to weaken the texture background, then multiply the original difference map with the corresponding pixels of the image, and obtain a more ideal contour salient map. The change contour map is clustered, and the inner region is filled in so as to get the focus area. Finally, According to the original difference map which focuses on the region correction, an ideal change detection result is obtained by processing the modified difference map. In this paper, the characteristics of NMF clustering are studied. Considering the existence of noise in the differential map, a two-sided filter is used to filter the difference map. The feature vector of each pixel is obtained by the second-order neighborhood scanning of the pixel, and then the dimension reduction of each pixel is achieved by using PCA. Finally, the final change detection results are obtained by using Semi-NMF clustering. By comparing with the two previous algorithms in this paper, we can see that the algorithm has a high detection accuracy, and by comparing with K-means clustering results, the accuracy of Semi-NMF clustering is reflected.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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