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  本文選題：航空遙感 + 陰影檢測��； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：航空遙感影像具有空間分辨率高、信息量大的特點,被廣泛應(yīng)用于地理信息產(chǎn)業(yè)、城市信息化建設(shè)、服務(wù)與旅游事業(yè)等城市經(jīng)濟(jì)和社會發(fā)展事業(yè)中。近年來,遙感影像分辨率隨遙感傳感技術(shù)的提高而呈百倍的增長,這更是突出了影像中陰影的存在。陰影削弱甚至消除了被遮擋區(qū)域物體的光學(xué)和物理信息,對后續(xù)的影像處理,包括目標(biāo)識別、地物分類等造成消極的影響。因此,面對航空遙感技術(shù)產(chǎn)出的海量遙感影像,陰影去除技術(shù)成為近年來研究的熱點與難點。國內(nèi)外學(xué)者針對航空遙感影像提出的陰影處理方法,能夠成功完成陰影檢測與補(bǔ)償。在此基礎(chǔ)上,如何提高陰影檢測與補(bǔ)償?shù)臏?zhǔn)確率與速度、如何擴(kuò)展其應(yīng)用范圍又是陰影處理的重要研究方向。本文研究了3c通道的不足,對基于3c通道的陰影檢測算法的各步驟提出改進(jìn)方案:第一,3c通道影像的非線性增強(qiáng)處理。為log變換公式設(shè)計可選參數(shù)擴(kuò)展像素值的動態(tài)變化范圍,然后利用閾值法修正增強(qiáng)結(jié)果。第二,平滑去噪處理。以鄰接像素點與中心點的距離為影響因子設(shè)計權(quán)重公式,并結(jié)合閾值法來修正邊緣細(xì)節(jié)處圖像模糊的效應(yīng)。第三,陰影邊緣檢測。針對改進(jìn)的Sobel邊緣檢測算法,利用高斯分布和抽樣方法估計邊緣閾值,從而實現(xiàn)閾值的自動選取。實驗結(jié)果證明,本文算法可有效降低噪聲對陰影提取的影響,尤其是在邊緣位置,改進(jìn)后的算法準(zhǔn)確率得到提高,適用于不同場景下的航空遙感影像的陰影檢測。本文重點分析了現(xiàn)有的基于顏色恒常的陰影補(bǔ)償算法對航空遙感影像進(jìn)行陰影補(bǔ)償時的優(yōu)缺點,針對校正增益計算的不準(zhǔn)確性以及原算法忽略半影區(qū)域在陰影補(bǔ)償所占的重要地位這兩個問題,提出基于顏色恒常的分區(qū)域陰影補(bǔ)償算法:第一,本影的補(bǔ)償。首先,確定同質(zhì)區(qū),根據(jù)陰影區(qū)域的大小確定同質(zhì)區(qū)的大小,然后通過“逐層選取”的方法標(biāo)記出陰影的同質(zhì)區(qū);其次,分別對陰影區(qū)和同質(zhì)區(qū)進(jìn)行顏色恒常計算得到顏色校正增益;最后,通過顏色恒常公式將本影區(qū)域的色彩變換到非陰影光照條件下。第二,半影的補(bǔ)償。首先,對陰影邊緣施行擴(kuò)展處理,獲得半影區(qū)域;然后,在半影區(qū)分段多項式模型的基礎(chǔ)上,設(shè)計半影光照補(bǔ)償公式;最后,根據(jù)光照補(bǔ)償公式對半影區(qū)域進(jìn)行陰影補(bǔ)償。航空遙感影像因其地物的多樣性與復(fù)雜性而在圖像處理領(lǐng)域擁有其特殊的地位,有針對性地對其進(jìn)行陰影檢測與補(bǔ)償始終是圖像處理的重點與難點。針對陰影檢測,本文分析了陰影區(qū)域的共有特征,沒有研究地物的獨有特征,因此,如何有針對性地分析地物的陰影相關(guān)特征,從而更有效地檢測出陰影也是今后的研究方向。針對陰影補(bǔ)償,本文使用同質(zhì)區(qū)的顏色恒常計算結(jié)果作為非陰影區(qū)的結(jié)果并增加半影補(bǔ)償步驟,如何在此基礎(chǔ)上進(jìn)一步提高陰影補(bǔ)償?shù)木葢?yīng)進(jìn)一步研究。
[Abstract]:Aerial remote sensing images are widely used in urban economic and social development, such as geographic information industry, urban information construction, service and tourism, because of their high spatial resolution and large amount of information. In recent years, the resolution of remote sensing image has increased by a hundred times with the improvement of remote sensing technology, which highlights the shadow in the image. Shadow weakens or even eliminates the optical and physical information of the occluded area, which has a negative effect on the subsequent image processing, including target recognition, ground object classification, and so on. Therefore, in the face of massive remote sensing images produced by aerial remote sensing technology, shadow removal technology has become a hot and difficult point in recent years. The shadow processing methods proposed by scholars at home and abroad for aerial remote sensing images can successfully complete shadow detection and compensation. On this basis, how to improve the accuracy and speed of shadow detection and compensation, and how to expand its application range is an important research direction of shadow processing. In this paper, the deficiency of 3c channel is studied, and an improved scheme is proposed for each step of shadow detection algorithm based on 3c channel: first, nonlinear enhancement processing of 3c channel image. For the log transform formula, the optional parameters are designed to extend the dynamic range of pixel values, and then the enhancement results are corrected by the threshold method. Second, smooth denoising. The distance between the adjacent pixel and the center is used as the influence factor to design the weight formula and the threshold method is combined to correct the image blur effect at the edge details. Third, shadow edge detection. For the improved Sobel edge detection algorithm, the Gao Si distribution and sampling methods are used to estimate the edge threshold, thus the automatic selection of the threshold is realized. The experimental results show that the proposed algorithm can effectively reduce the influence of noise on shadow extraction, especially in edge position, and the improved algorithm can be applied to shadow detection of aerial remote sensing images in different scenes. This paper focuses on analyzing the advantages and disadvantages of the existing shadow compensation algorithms based on color constant for aerial remote sensing image shadow compensation. Aiming at the inaccuracy of the correction gain calculation and the fact that the original algorithm neglects the important position of the penumbra region in shadow compensation, a sub-region shadow compensation algorithm based on color constant is proposed. First, the homogenous area is determined, the size of the homogeneous area is determined according to the size of the shadow area, and then the homogeneous area of the shadow is marked by the method of "layer by layer selection"; secondly, The color correction gain is obtained by calculating the color constant of the shadow region and the homogeneous region, respectively. Finally, the color of the shadow region is transformed to the non-shadow illumination condition by the color constant formula. Second, penumbra compensation. First, the shadow edge is extended to obtain the penumbra region; then, based on the polynomial model of the penumbra differentiation segment, the penumbra illumination compensation formula is designed. Finally, the shadow compensation is carried out according to the illumination compensation formula. Aerial remote sensing image has a special position in the field of image processing because of its diversity and complexity. It is always the focus and difficulty of image processing to detect and compensate its shadow pertinently. Aiming at shadow detection, this paper analyzes the common features of shadow area, and does not study the unique features of ground objects. Therefore, how to analyze the shadow correlation features of ground objects in order to detect shadows more effectively is also the research direction in the future. For shadow compensation, we use the result of color constant calculation of homogeneous region as the result of non-shadow region and add penumbra compensation steps. How to further improve the accuracy of shadow compensation on this basis should be further studied.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

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相關(guān)期刊論文 前1條

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本文編號：1942821