本文選題：SAR圖像 + 變化檢測(cè)��； 參考：《東北大學(xué)》2014年碩士論文

【摘要】：變化檢測(cè)是從同一地區(qū)、不同時(shí)相的圖像中分析和確定地物變化的特征、過(guò)程的技術(shù),是遙感圖像研究領(lǐng)域的重要分支。SAR (synthetic aperture radar)技術(shù)同可見(jiàn)光成像相比,不受天氣和光照條件影響,具有多極化、全天候、全天時(shí)和一定穿透性的特點(diǎn),十分適合變化檢測(cè)研究。但由于SAR圖像中包含相干斑噪聲,又使SAR圖像區(qū)別于其他遙感圖像,形成了一套獨(dú)特的變化檢測(cè)方法。本文以提高SAR圖像變化檢測(cè)方法的性能為目標(biāo),圍繞SAR圖像的變化檢測(cè)技術(shù)開(kāi)展研究,主要工作可以概括為以下幾個(gè)部分：(1)介紹SAR相關(guān)原理及變化檢測(cè)理論,概括SAR圖像變化檢測(cè)的一般流程及基本方法,分析相干斑噪聲產(chǎn)生的機(jī)理、模型和統(tǒng)計(jì)特性,針對(duì)SAR圖像變化檢測(cè)的結(jié)果給出精度評(píng)估的標(biāo)準(zhǔn)和依據(jù)。(2)詳細(xì)介紹Lee、Kuan、Frost和Gamma Map濾波算法原理,分析不同濾波算法的優(yōu)點(diǎn)、缺點(diǎn)以及適用條件,并通過(guò)實(shí)驗(yàn)比較,選取Frsot濾波作為本文變化檢測(cè)研究的濾波算法。(3)比較和分析差值法、比值法和對(duì)數(shù)比值法構(gòu)造差異圖像的特性,并在此基礎(chǔ)上,本文提出用增強(qiáng)的對(duì)數(shù)比值法來(lái)構(gòu)造差異圖像,以提高圖像中發(fā)生變化和未發(fā)生變化區(qū)域的可分性。(4)基于圖像分割的自動(dòng)閾值選取算法,對(duì)增強(qiáng)的對(duì)數(shù)比值法構(gòu)造的差異圖像應(yīng)用循環(huán)迭代法、最大類(lèi)間方差法和最佳直方圖熵法；基于貝葉斯決策的自動(dòng)閾值選取算法,對(duì)增強(qiáng)的對(duì)數(shù)比值法構(gòu)造的差異圖像應(yīng)用KI最佳閾值選取算法和EM迭代閾值選取算法。分析了傳統(tǒng)算法的優(yōu)缺點(diǎn)及限定條件,針對(duì)以上算法的不足之處,首次將基于最小Tsallis交叉熵的圖像分割算法應(yīng)用到SAR圖像的變化檢測(cè),平均正確檢測(cè)率達(dá)96.1916%,平均Kappa系數(shù)達(dá)0.58,提高了變化檢測(cè)性能。(5)提出改進(jìn)的基于最小Tsallis交叉熵閾值選取算法,充分利用圖像中每個(gè)像元與周?chē)裨目臻g關(guān)系,對(duì)基于最小Tsallis交叉熵選取的單一閾值進(jìn)行補(bǔ)償,用獲得的動(dòng)態(tài)閾值分割圖像,經(jīng)實(shí)驗(yàn)驗(yàn)證該改進(jìn)算法更加準(zhǔn)確和有效,平均正確檢測(cè)率達(dá)96.9125%,平均Kappa系數(shù)達(dá)0.6075。(6)總結(jié)本文的工作,展望未來(lái)SAR圖像變化檢測(cè)技術(shù)的發(fā)展趨勢(shì)。
[Abstract]:Change detection, which is an important branch of remote sensing image research field, is to analyze and determine the feature and process of ground object change from the same area and different phase image, which is compared with visible light imaging. It has the characteristics of multi-polarization, all-weather, all-day and certain penetration, so it is suitable for the research of change detection. However, because of the speckle noise in SAR images, SAR images are different from other remote sensing images, and a unique change detection method is formed. Aiming at improving the performance of the change detection method of SAR image, this paper focuses on the change detection technology of SAR image. The main work can be summarized as follows: (1) introduce the principle of SAR correlation and the theory of change detection. The general flow and basic methods of SAR image change detection are summarized, and the mechanism, model and statistical characteristics of speckle noise are analyzed. According to the results of SAR image change detection, the criterion and basis of accuracy evaluation are given. (2) the principle of Leehnian Frost and Gamma Map filtering algorithms is introduced in detail, the advantages, disadvantages and applicable conditions of different filtering algorithms are analyzed, and the experimental results are compared. Frsot filter is selected as the filtering algorithm in this paper. (3) the difference method, the ratio method and the logarithmic ratio method are compared and analyzed to construct the difference image. On this basis, an enhanced logarithmic ratio method is proposed to construct the difference image. In order to improve the separability of the changed and unchanged regions in the image. (4) an automatic threshold selection algorithm based on image segmentation is applied to the differential image constructed by the enhanced logarithmic ratio method. Based on the automatic threshold selection algorithm of Bayesian decision, Ki optimal threshold selection algorithm and EM iterative threshold selection algorithm are applied to the difference image constructed by enhanced logarithmic ratio method. This paper analyzes the advantages and disadvantages of the traditional algorithms and their limitations. In view of the shortcomings of the above algorithms, the image segmentation algorithm based on the minimum Tsallis cross-entropy is applied to the change detection of SAR images for the first time. The average correct detection rate is 96.1916 and the average Kappa coefficient is 0.58, which improves the performance of change detection. (5) an improved algorithm based on minimum Tsallis cross-entropy threshold selection is proposed to make full use of the spatial relationship between each pixel and the surrounding pixel in the image. This paper compensates a single threshold selected based on minimum Tsallis cross entropy, and uses the obtained dynamic threshold to segment the image. Experiments show that the improved algorithm is more accurate and effective, with an average correct detection rate of 96.9125 and an average Kappa coefficient of 0.6075. (6) the work of this paper is summarized. The development trend of SAR image change detection technology in the future is prospected.
【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類(lèi)號(hào)】：TP751

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本文編號(hào)：2084310