本文關(guān)鍵詞：SAR圖像顯著性檢測方法研究　出處：《國防科學(xué)技術(shù)大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 合成孔徑雷達 視覺注意機制 顯著性 顯著圖 目標檢測

【摘要】：人類視覺系統(tǒng)具備高效的圖像解譯能力,能夠快速地檢測顯著性區(qū)域,提取感興趣目標。本文旨在將視覺注意機制理論應(yīng)用于SAR圖像解譯中,提出適用于SAR圖像的顯著性檢測方法。針對這一問題,本文總結(jié)分析了現(xiàn)有典型的顯著性檢測算法,并結(jié)合SAR圖像的特性,提出基于顯著性的SAR圖像目標檢測算法和尺度自適應(yīng)的SAR圖像顯著性檢測方法。本文主要工作包含以下幾個方面:(1)總結(jié)分析了典型的顯著性檢測算法。首先重點分析了特征融合理論以及在此基礎(chǔ)上提出來的Koch視覺注意生物神經(jīng)學(xué)框架;討論了顯著性檢測算法的客觀評估指標;總結(jié)了四種典型的顯著性檢測算法的原理和計算方法,并使用上述算法分別對光學(xué)圖像和SAR圖像進行實驗和評價。(2)提出了基于顯著性的SAR圖像目標檢測算法。首先簡要介紹了SAR圖像背景雜波統(tǒng)計建模的方法,總結(jié)了SAR圖像常用的背景雜波統(tǒng)計分布模型以及最優(yōu)統(tǒng)計分布模型的選擇準則。其次,總結(jié)分析了基于統(tǒng)計分布模型的雙參數(shù)CFAR算法的算法流程和判決閾值的計算問題。再次,從視覺顯著性理論出發(fā),結(jié)合CFAR算法的窗口設(shè)計和SAR圖像雜波背景統(tǒng)計建模方法,運用假設(shè)檢驗方法和貝葉斯定理設(shè)計基于顯著性的SAR圖像目標檢測算法。最后,通過對比實驗驗證本文算法在虛警率、運算效率指標上優(yōu)于基于統(tǒng)計分布模型的雙參數(shù)CFAR算法。(3)提出了尺度自適應(yīng)的SAR圖像顯著性檢測方法。在Kadir顯著區(qū)域檢測算法基礎(chǔ)上,結(jié)合SAR圖像特性進行算法改進,提出了尺度自適應(yīng)的SAR圖像顯著性檢測方法。首先,重定義局部復(fù)雜度測度,解決信息熵度量方式不適合用于度量SAR圖像局部復(fù)雜度測度這一問題。通過比率距離度量方式替代差值距離度量方式,克服SAR圖像相干斑噪聲,進而考慮了像素之間的空間分布,構(gòu)造與空間分布相關(guān)的局部復(fù)雜度測度度量方法,該度量方法比信息熵度量方法更適用于SAR圖像。其次,重定義了自差異性測度,選取了一種對于顯著信息變化敏感的自差異性測度度量方法。再次,改進了顯著性尺度確定方法,優(yōu)化算法檢測的準確性;最后,根據(jù)顯著性測度和顯著性尺度提出了顯著圖生成方法。實驗結(jié)果驗證了該算法比Kadir顯著性檢測算法更適用于SAR圖像,并且,該算法的顯著性檢測效果優(yōu)于四種典型的顯著性檢測算法。
[Abstract]:Human visual system has the ability of efficient image interpretation, can quickly detect significant regions and extract objects of interest. This paper aims to apply the theory of visual attention mechanism to the interpretation of SAR images. To solve this problem, this paper summarizes and analyzes the existing typical salience detection algorithms, and combines the characteristics of SAR images. This paper proposes a salience based SAR image target detection algorithm and a scale-adaptive SAR image salience detection method. The main work of this paper includes the following aspects: 1). The typical salience detection algorithms are summarized and analyzed. Firstly, the feature fusion theory and the Koch visual attention biological neural framework are analyzed. The objective evaluation index of salience detection algorithm is discussed. The principle and calculation method of four typical salience detection algorithms are summarized. The above algorithms are used to test and evaluate the optical image and SAR image, respectively. In this paper, a salience based target detection algorithm for SAR images is proposed. Firstly, the statistical modeling method of background clutter in SAR images is briefly introduced. The selection criteria of background clutter statistical distribution model and optimal statistical distribution model for SAR images are summarized. Secondly. The algorithm flow of two-parameter CFAR algorithm based on statistical distribution model and the calculation of decision threshold are summarized and analyzed. Thirdly, based on the visual significance theory. Combining the window design of CFAR algorithm and the statistical modeling method of SAR image clutter background, using hypothesis test method and Bayesian theorem to design a salient based SAR image target detection algorithm. Finally. Through the contrast experiment, the algorithm is verified in the false alarm rate. The operational efficiency is better than the two-parameter CFAR algorithm based on statistical distribution model. A scale-adaptive SAR image salience detection method is proposed, which is based on the Kadir salient region detection algorithm. Based on the algorithm improvement of SAR image characteristics, a scale-adaptive SAR image salience detection method is proposed. Firstly, the local complexity measure is redefined. The information entropy measurement is not suitable for measuring the local complexity of SAR images. Instead of the difference distance measurement, the ratio distance measure is used to overcome the speckle noise of SAR images. Then the spatial distribution of pixels is considered, and the local complexity measurement method related to spatial distribution is constructed, which is more suitable for SAR images than information entropy measurement. Redefine the measure of self-diversity, select a measure of self-diversity that is sensitive to the change of significant information. Thirdly, improve the method of determining significant scale and optimize the accuracy of algorithm detection. Finally, according to the significance measure and significance scale, a significant map generation method is proposed. Experimental results show that the algorithm is more suitable for SAR images than the Kadir saliency detection algorithm. The significance detection effect of this algorithm is better than that of four typical salience detection algorithms.
【學(xué)位授予單位】：國防科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TN957.52

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本文編號：1402077