本文選題：高分辨遙感圖像　切入點：背景統計建模　出處：《深圳大學》2017年碩士論文

【摘要】：海洋遙感技術是全球變化偵測和軍事偵察等領域重要研究課題之一,如何有效精準地從大量的大幅面海洋遙感圖像中提取出重要區(qū)域,是海洋遙感技術中的重要研究方向。其中,艦船目標檢測是重點研究的內容,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行海上目標檢測存在一定的困難。所以本文針對大幅面海洋遙感圖像目標檢測中的一系列關鍵問題進行了深入的研究,包括系統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計建模和模型參數的雙層細化估計方法,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檢測方法。論文的研究成果主要是以下三個方面: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計特性。利用局部和大范圍特征統計量分析了衛(wèi)星高分辨遙感圖像中的局部和大范圍背景統計特性,得出了海洋背景的統計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計模型,提出了模型參數的雙層細化估計方法。利用K-S假設檢驗對局部海洋遙感背景圖像的灰度分布特性進行檢驗,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,結合海洋背景統計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像素點建立統計分布模型,接著介紹了模型參數雙層估計方法中的子圖像參數粗估計和內部塊參數細化插值,得到了大范圍的遙感圖像背景曲面高斯統計模型。3)設計了一種基于大范圍背景模型的高分辨海洋遙感圖像目標檢測方法。首先將圖像子塊的均值和方差作為分類向量,利用模糊C均值聚類方法對純海洋和非純海洋的圖像子塊進行粗略分類預處理;接著采用該大范圍的背景統計模型對大幅面海洋遙感圖像中非純海洋圖像子塊進行目標檢測,得到大幅面遙感圖像候選目標檢測結果圖;最后提取任意兩個候選目標的質心距離和重疊率特征,利用模糊推理對候選艦船目標進行融合后處理,得到最終檢測結果。
[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.
【學位授予單位】：深圳大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

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