【摘要】：對地觀測技術(shù)的迅猛發(fā)展,迎來了衛(wèi)星遙感前所未有的發(fā)展階段。高分辨率遙感圖像的出現(xiàn)使得人們探索和認(rèn)識自然界步入了新的里程碑,高大建筑物、樹木等遮擋太陽光線難以避免在遙感圖像中形成大片陰影區(qū)。陰影的存在影響圖像信息的判讀與解譯,給后續(xù)遙感圖像處理帶來諸多困難,如目標(biāo)分類識別,圖像配準(zhǔn)等任務(wù)；為有效利用陰影數(shù)據(jù),遙感影像陰影處理成為研究熱點(diǎn),而陰影檢測與陰影去除是陰影處理中相互依存的兩個(gè)方面。 本文對現(xiàn)有陰影檢測算法-基于黑體輻射模型與自適應(yīng)特征選擇的陰影檢測算法進(jìn)行了學(xué)習(xí)與仿真。分析研究發(fā)現(xiàn),現(xiàn)有陰影檢測算法對亮度均勻且亮度低的陰影區(qū)域有較好的檢測性能。不過,由于地物復(fù)雜性,遙感圖像中存在許多非勻質(zhì)陰影和亮陰影,現(xiàn)有陰影檢測算法對非勻質(zhì)陰影和亮陰影存在漏檢問題。為提高對非勻質(zhì)陰影與亮陰影的檢測效果,本文提出了一種結(jié)合局部分類水平集與顏色特征的遙感影像陰影檢測方法,該方法首先結(jié)合陰影區(qū)域的亮度非均勻性,采用局部分類水平集分割遙感圖像的陰影區(qū)域,然后通過分析綠地與陰影顏色特征分量的差別以去除候選陰影區(qū)中被誤檢的綠地。實(shí)驗(yàn)結(jié)果表明所提出的方法優(yōu)于現(xiàn)有的黑體輻射模型與自適應(yīng)特征選擇的方法,有效克服了傳統(tǒng)方法對非勻質(zhì)陰影與亮陰影的漏檢問題,且整個(gè)檢測過程無需人工干預(yù)。 在陰影檢測算法能較好檢測定位出陰影的基礎(chǔ)上,論文進(jìn)行陰影去除算法的研究。仿真分析了現(xiàn)有的基于顏色恒常、基于樣本學(xué)習(xí)、自適應(yīng)非局部正則的陰影去除算法。分析研究發(fā)現(xiàn),現(xiàn)有陰影去除算法存在顏色失真、信息失真,或是需過多人工參與操作、陰影去除后紋理信息保持不夠好等問題。為此,論文設(shè)計(jì)了一種基于Curvelet紋理方向非局部正則的陰影去除算法。該算法對自適應(yīng)非局部正則的陰影去除算法從三個(gè)方面進(jìn)行了改進(jìn)。(1)采用論文提出的“結(jié)合局部分類水平集與顏色特征的遙感影像陰影檢測方法”提取陰影區(qū)；(2)為消除半影的影響,對陰影邊緣進(jìn)行弱化；(3)采用Curvelet小波提取陰影區(qū)方向因子用于正則化處理,增強(qiáng)陰影去除區(qū)域的紋理細(xì)節(jié)信息。實(shí)驗(yàn)結(jié)果表明所設(shè)計(jì)的陰影去除算法提高了算法可操作性、對陰影去除區(qū)域的紋理細(xì)節(jié)信息的保持優(yōu)于自適應(yīng)非局部正則的陰影去除算法。 最后,本文基于MATLAB平臺設(shè)計(jì)了一款遙感影像陰影檢測與去除算法可視化演示系統(tǒng),并給出了該系統(tǒng)的使用說明和演示結(jié)果。
[Abstract]:The rapid development of Earth observation technology ushered in an unprecedented development stage of satellite remote sensing. The appearance of high-resolution remote sensing images makes people explore and understand the nature into a new milestone. It is difficult to avoid the formation of large shadow areas in remote sensing images by blocking solar rays such as tall buildings and trees. The existence of shadow affects the interpretation and interpretation of image information, and brings many difficulties to the subsequent remote sensing image processing, such as target classification and recognition, image registration and other tasks. In order to utilize shadow data effectively, shadow processing of remote sensing images has become a hot topic, and shadow detection and shadow removal are two interdependent aspects in shadow processing. In this paper, shadow detection algorithms based on blackbody radiation model and adaptive feature selection are studied and simulated. It is found that the existing shadow detection algorithms have better detection performance for shadow regions with uniform brightness and low brightness. However, due to the complexity of ground objects, there are many non-homogeneous shadows and bright shadows in remote sensing images, and the existing shadow detection algorithms have the problem of missing detection of non-homogeneous shadows and bright shadows. In order to improve the detection effect of non-homogeneous shadow and bright shadow, this paper presents a method of shadow detection in remote sensing image combining local classification level set and color feature. This method first combines the brightness inhomogeneity of shadow region. The shadow region of remote sensing image is segmented by using local classification level set, and then the difference between green space and shadow color characteristic component is analyzed to remove the false detected green space in candidate shadow area. The experimental results show that the proposed method is superior to the existing blackbody radiation model and adaptive feature selection method, and effectively overcomes the problem of missing detection of non-homogeneous and bright shadows by traditional methods, and the whole detection process does not require manual intervention. On the basis of shadow detection algorithm which can detect and locate shadow, shadow removal algorithm is studied in this paper. The existing shadow removal algorithms based on color constant, sample learning and adaptive nonlocal regularization are simulated and analyzed. It is found that the existing shadow removal algorithms have some problems such as color distortion, information distortion, or too much manual participation, and the texture information is not good enough after shadow removal. Therefore, a non-local regular shadow removal algorithm based on Curvelet texture direction is designed. The algorithm improves the adaptive non-local regularization shadow removal algorithm from three aspects. (1) the shadow region is extracted by the "shadow detection method combining local classification level set and color features" proposed in this paper; (2) in order to eliminate the influence of penumbra, the shadow edge is weakened; (3) the direction factor of shadow region is extracted by Curvelet wavelet to enhance the texture details of shadow removal region. The experimental results show that the proposed shadow removal algorithm improves the maneuverability of the algorithm and maintains the texture details of the shadow removal region better than the adaptive non-local regular shadow removal algorithm. Finally, based on the MATLAB platform, a visual demonstration system of shadow detection and removal algorithm for remote sensing images is designed, and the application and demonstration results of the system are given.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張海霞;卞正富;;遙感影像植被信息提取方法研究及思考[J];地理空間信息;2007年06期

2 王潤生;熊盛青;聶洪峰;梁樹能;齊澤榮;楊金中;閆柏琨;趙福岳;范景輝;童立強(qiáng);林鍵;甘甫平;陳微;楊蘇明;張瑞江;葛大慶;張曉坤;張振華;王品清;郭小方;李麗;;遙感地質(zhì)勘查技術(shù)與應(yīng)用研究[J];地質(zhì)學(xué)報(bào);2011年11期

3 唐亮,謝維信,黃建軍,肖志級;城市航空影像中基于模糊Retinex的陰影消除[J];電子學(xué)報(bào);2005年03期

4 楊俊;趙忠明;;基于歸一化RGB色彩模型的陰影處理方法[J];光電工程;2007年12期

5 葉勤;徐秋紅;謝惠洪;;城市航空影像中基于顏色恒常性的陰影消除[J];光電子.激光;2010年11期

6 趙亮;和紅杰;尹忠科;;基于Curvelet的紋理方向自適應(yīng)圖像插值[J];光電子.激光;2012年04期

7 柳稼航;楊建峰;方濤;;彩色遙感影像陰影顏色特性分析[J];光子學(xué)報(bào);2009年02期

8 裴浩;敖艷紅;;衛(wèi)星遙感技術(shù)的應(yīng)用與發(fā)展[J];航天器工程;2008年06期

9 喻紅艷;李利軍;;彩色航空影像中的建筑物陰影提取[J];科學(xué)技術(shù)與工程;2007年07期

10 王彥情;馬雷;田原;;光學(xué)遙感圖像艦船目標(biāo)檢測與識別綜述[J];自動化學(xué)報(bào);2011年09期

，

本文編號：2429938