本文選題：高分辨遙感圖像　切入點(diǎn)：背景統(tǒng)計(jì)建模　出處：《深圳大學(xué)》2017年碩士論文

【摘要】：海洋遙感技術(shù)是全球變化偵測(cè)和軍事偵察等領(lǐng)域重要研究課題之一,如何有效精準(zhǔn)地從大量的大幅面海洋遙感圖像中提取出重要區(qū)域,是海洋遙感技術(shù)中的重要研究方向。其中,艦船目標(biāo)檢測(cè)是重點(diǎn)研究的內(nèi)容,具有重要的軍事和民用意義。但是由于遙感圖像的空間分辨率越來越高,其尺寸越來越大,如何從大尺度大幅面遙感圖像中進(jìn)行海上目標(biāo)檢測(cè)存在一定的困難。所以本文針對(duì)大幅面海洋遙感圖像目標(biāo)檢測(cè)中的一系列關(guān)鍵問題進(jìn)行了深入的研究,包括系統(tǒng)的分析了大幅面海洋遙感圖像的背景特性,提出了大范圍的背景統(tǒng)計(jì)建模和模型參數(shù)的雙層細(xì)化估計(jì)方法,以及提出了基于大范圍背景模型的高分辨海洋遙感圖像目標(biāo)檢測(cè)方法。論文的研究成果主要是以下三個(gè)方面:1)分析了衛(wèi)星高分辨海洋遙感圖像局部和大范圍的海洋背景統(tǒng)計(jì)特性。利用局部和大范圍特征統(tǒng)計(jì)量分析了衛(wèi)星高分辨遙感圖像中的局部和大范圍背景統(tǒng)計(jì)特性,得出了海洋背景的統(tǒng)計(jì)均值與方差具有局部相似性和大范圍連續(xù)變化性。2)建立了高分辨海洋遙感圖像背景的大范圍曲面高斯分布統(tǒng)計(jì)模型,提出了模型參數(shù)的雙層細(xì)化估計(jì)方法。利用K-S假設(shè)檢驗(yàn)對(duì)局部海洋遙感背景圖像的灰度分布特性進(jìn)行檢驗(yàn),得出了其局部海洋遙感背景圖像灰度分布特性最符合高斯分布,結(jié)合海洋背景統(tǒng)計(jì)均值與方差的局部相似性和大范圍連續(xù)變化性,采用曲面擬合的方法為海水背景空間中每個(gè)像素點(diǎn)建立統(tǒng)計(jì)分布模型,接著介紹了模型參數(shù)雙層估計(jì)方法中的子圖像參數(shù)粗估計(jì)和內(nèi)部塊參數(shù)細(xì)化插值,得到了大范圍的遙感圖像背景曲面高斯統(tǒng)計(jì)模型。3)設(shè)計(jì)了一種基于大范圍背景模型的高分辨海洋遙感圖像目標(biāo)檢測(cè)方法。首先將圖像子塊的均值和方差作為分類向量,利用模糊C均值聚類方法對(duì)純海洋和非純海洋的圖像子塊進(jìn)行粗略分類預(yù)處理;接著采用該大范圍的背景統(tǒng)計(jì)模型對(duì)大幅面海洋遙感圖像中非純海洋圖像子塊進(jìn)行目標(biāo)檢測(cè),得到大幅面遙感圖像候選目標(biāo)檢測(cè)結(jié)果圖;最后提取任意兩個(gè)候選目標(biāo)的質(zhì)心距離和重疊率特征,利用模糊推理對(duì)候選艦船目標(biāo)進(jìn)行融合后處理,得到最終檢測(cè)結(jié)果。
[Abstract]:Ocean remote sensing technology is one of the important research topics in the fields of global change detection and military reconnaissance. How to extract important areas from a large number of large format ocean remote sensing images effectively and accurately, It is an important research direction in ocean remote sensing technology. Among them, ship target detection is an important research content, which has important military and civil significance. However, because of the higher spatial resolution of remote sensing image, the size of ship target detection becomes larger and larger. There are some difficulties in the detection of large scale and large scale remote sensing images. Therefore, a series of key problems in large scale ocean remote sensing image detection are studied in this paper. The background characteristics of large format marine remote sensing images are systematically analyzed, and a large range of background statistical modeling and two-layer thinning estimation method for model parameters are proposed. And a high resolution ocean remote sensing image target detection method based on large range background model is proposed. The main research results of this paper are as follows: 1) analyze the local and large area sea of satellite high resolution ocean remote sensing image. Statistical characteristics of ocean background. Local and large-scale background statistical characteristics in satellite high-resolution remote sensing images are analyzed by using local and large-scale feature statistics, The statistical mean and variance of ocean background have local similarity and wide range continuous variation. 2) A statistical model of Gao Si distribution on large scale curved surface with high resolution ocean remote sensing image background is established. A two-layer thinning estimation method for model parameters is proposed. The gray distribution characteristics of local marine remote sensing background images are tested by K-S hypothesis test, and the results show that the gray distribution characteristics of local marine remote sensing background images are most consistent with Gao Si distribution. Combined with the local similarity between the statistical mean and variance of ocean background and the continuous variation in large range, a statistical distribution model for each pixel in sea water background space is established by using curved surface fitting method. Then, the rough estimation of sub-image parameters and the interpolation of internal block parameters in the two-level estimation of model parameters are introduced. In this paper, Gao Si's statistical model of large-scale remote sensing image background surface. 3) A high resolution ocean remote sensing image target detection method based on large-scale background model is designed. Firstly, the mean and variance of image subblocks are taken as classification vectors. The fuzzy C-means clustering method is used to preprocess the image subblocks of pure and non-pure ocean roughly, and then the large scale background statistical model is used to detect the sub-blocks of non-pure ocean images of large scale ocean remote sensing images. Finally, the centroid distance and overlap rate of any two candidate targets are extracted, and the final detection results are obtained by using fuzzy inference to fuse the candidate ship targets.
【學(xué)位授予單位】：深圳大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP751

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