【摘要】：變化檢測(cè)問(wèn)題屬于圖像處理領(lǐng)域,通常是指“根據(jù)不同時(shí)間的多次觀測(cè)來(lái)確定一個(gè)物體的狀態(tài)變化或確定某種現(xiàn)象的變化的過(guò)程”。隨著遙感技術(shù)的發(fā)展,合成孔徑雷達(dá)(Synthetic Aperture Radar,SAR)圖像成為了圖像變化檢測(cè)問(wèn)題中的主要的數(shù)據(jù)來(lái)源。SAR圖像的獲得具有全天候、全天時(shí)的特點(diǎn),合成孔徑雷達(dá)在成像過(guò)程中對(duì)地物有一定的穿透能力,且不受大氣、氣候等隨機(jī)因素的影響,具有不可比擬的優(yōu)點(diǎn)。國(guó)內(nèi)外眾多學(xué)者對(duì)SAR圖像的變化檢測(cè)問(wèn)題進(jìn)行了大量的研究,變化檢測(cè)結(jié)果的精度也在不斷地提高。無(wú)監(jiān)督變化檢測(cè)算法為最常使用的變化檢測(cè)算法,該類(lèi)算法的主要步驟為SAR圖像的預(yù)處理、差異圖的構(gòu)造和差異圖的分析,本文的研究重點(diǎn)為SAR圖像變化檢測(cè)中的差異圖構(gòu)造和差異圖分析的問(wèn)題。本文在兩方面對(duì)SAR圖像變化檢測(cè)技術(shù)進(jìn)行了提高,具體如下所述:1.對(duì)于差異圖的構(gòu)造,提出了一種基于非局部小波信息的SAR圖像變化檢測(cè)方法。這種方法提出對(duì)同一地區(qū)的兩幅遙感圖像先用簡(jiǎn)單的代數(shù)方法產(chǎn)生差異圖,再對(duì)差異圖進(jìn)行小波分解,對(duì)于高頻部分,使用基于非局部均值的方法進(jìn)行去噪,將高頻圖像的每一個(gè)像素點(diǎn)結(jié)合鄰域信息轉(zhuǎn)變?yōu)橄蛄?對(duì)圖像進(jìn)行升維,再利用高斯核函數(shù)判斷全局信息對(duì)于這個(gè)點(diǎn)去噪的加權(quán)系數(shù),然后求加權(quán)均值得到這個(gè)點(diǎn)的真實(shí)灰度值,對(duì)高頻圖像的每一個(gè)點(diǎn)都進(jìn)行相同的操作,最后進(jìn)行小波逆變換,得到最終的差異圖。在高頻部分使用基于非局部均值的去噪方法,既能有效地保留圖像的結(jié)構(gòu)信息,又能去除噪聲,實(shí)現(xiàn)了保留細(xì)節(jié)信息和去除噪聲的平衡,將提出的方法與傳統(tǒng)的基于代數(shù)的方法和圖像融合的算法對(duì)比,實(shí)驗(yàn)結(jié)果表明,提出的方法無(wú)論是在視覺(jué)上還是定量的評(píng)價(jià)指標(biāo)上都能取得較好的結(jié)果;2.對(duì)于差異圖分析,提出了一種基于水平集的動(dòng)態(tài)輪廓模型,這種模型的提出是基于局部模糊C均值聚類(lèi)算法,對(duì)局部模糊C均值聚類(lèi)算法的目標(biāo)函數(shù)中加入水平集函數(shù)構(gòu)成局部能量函數(shù),使用高斯核函數(shù)使得局部信息對(duì)于目標(biāo)能量方程的貢獻(xiàn)是可控的,在目標(biāo)能量方程中加入使得水平集函數(shù)能夠保持良好的形狀和約束零水平集曲線光滑演化的正則項(xiàng),與全局能量項(xiàng)一起構(gòu)成新的目標(biāo)能量方程,最小化目標(biāo)能量方程,得到目標(biāo)區(qū)域的輪廓曲線。實(shí)驗(yàn)證明,相比于之前的經(jīng)典水平集方法和局部模糊C均值聚類(lèi)算法,所提出的方法在差異圖分割方面能夠取得較好的結(jié)果。
[Abstract]:The problem of change detection belongs to the field of image processing. It usually refers to the process of determining the state change of an object or determining the change of a phenomenon according to many observations at different times. With the development of remote sensing technology, synthetic Aperture Radar (Synthetic Aperture Radar,SAR) images have become the main data source in image change detection. Synthetic Aperture Radar (SAR) has a certain penetrating ability to surface objects in the imaging process, and is not affected by random factors such as atmosphere and climate, so it has unparalleled advantages. Many scholars at home and abroad have done a lot of research on the change detection of SAR images, and the accuracy of the change detection results is constantly improving. Unsupervised change detection algorithm is the most commonly used change detection algorithm. The main steps of this kind of algorithm are pre-processing of SAR image, construction of difference graph and analysis of difference graph. This paper focuses on the construction of difference graph and the analysis of difference graph in SAR image change detection. In this paper, the SAR image change detection technology has been improved in two aspects, as follows: 1. For the construction of difference graph, a SAR image change detection method based on nonlocal wavelet information is proposed. This method proposes that two remote sensing images in the same area are generated by simple algebraic method and then decomposed by wavelet transform. For the high frequency part, the method based on non-local mean is used to Denoise the image, and the difference image is decomposed by wavelet transform, and the method based on non-local mean is used to remove the noise. Every pixel of high frequency image is transformed into vector combined with neighborhood information, the dimension of the image is raised, and then the weighted coefficient of global information for denoising of this point is judged by Gao Si kernel function. Then the real gray value of the point is obtained by the weighted mean, and the same operation is performed on each point of the high-frequency image. Finally, the inverse wavelet transform is carried out to obtain the final difference graph. In the high frequency part, the denoising method based on the non-local mean can not only preserve the structure information of the image effectively, but also remove the noise, and achieve the balance between preserving the detail information and removing the noise. The proposed method is compared with the traditional algebra-based method and the image fusion algorithm. The experimental results show that the proposed method can achieve good results both visually and quantitatively. 2. For difference graph analysis, a dynamic contour model based on level set is proposed, which is based on local fuzzy C-means clustering algorithm. The local energy function is formed by adding the level set function to the objective function of the local fuzzy C-means clustering algorithm. The contribution of the local information to the target energy equation is controllable by using the Gao Si kernel function. A regular term is added to the objective energy equation, which makes the level set function maintain a good shape and constrains the smooth evolution of the zero level set curve. Together with the global energy term, a new objective energy equation is formed to minimize the objective energy equation. The contour curve of the target area is obtained. The experimental results show that compared with the classical level set method and the local fuzzy C-means clustering algorithm, the proposed method can achieve better results in the segmentation of difference graph than the classical level set method and the local fuzzy C-means clustering algorithm.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TN957.52

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