【摘要】：無(wú)人機(jī)影像重疊度較高,幅數(shù)過(guò)多,影像拼接需要耗費(fèi)大量時(shí)間,在不影響數(shù)據(jù)質(zhì)量的情況下,如何降低航片的冗余是本論文研究的主要問(wèn)題。論文以甘肅某縣土地確權(quán)項(xiàng)目為依托,以實(shí)測(cè)無(wú)人機(jī)影像為基礎(chǔ)數(shù)據(jù),對(duì)減小無(wú)人機(jī)影像重疊度,減少數(shù)據(jù)冗余進(jìn)行了分析研究,具體研究?jī)?nèi)容及結(jié)果如下:1.首先對(duì)研究區(qū)域無(wú)人機(jī)數(shù)據(jù)做了影像重疊度統(tǒng)計(jì),結(jié)合無(wú)人機(jī)最佳重疊度和規(guī)范要求,發(fā)現(xiàn)無(wú)人機(jī)影像存在大量冗余。針對(duì)無(wú)人機(jī)影像高重疊度的特點(diǎn),采用不同間隔的抽稀方式對(duì)影像稀釋。實(shí)驗(yàn)結(jié)果表明影像抽稀間隔越大,拼接影像所用時(shí)間越少,平面位置中誤差越大。間隔4張影像抽稀達(dá)到影像重疊度規(guī)范臨界值,拼接的時(shí)間比未抽稀影像拼接所用時(shí)間減少了一半多。2.通過(guò)構(gòu)建一個(gè)包含138幅航空遙感影像的樣本庫(kù),利用知識(shí)庫(kù)的方法,在10分制的基礎(chǔ)上,提出了利用加權(quán)平均標(biāo)準(zhǔn)差、平均梯度和信息熵三種評(píng)價(jià)指標(biāo)得到影像質(zhì)量的綜合評(píng)價(jià)值,建立一種基于綜合評(píng)價(jià)值的抽稀方式。其影像拼接比全部影像拼接短了近一半時(shí)間,融合影像平面位置中誤差得到的良好控制,曝光過(guò)度影像引起的“白斑”現(xiàn)象明顯變少,影像辨識(shí)度明顯提高。3.影像裁剪也降低影像重疊度,加快無(wú)人機(jī)影像影像匹配速度。同時(shí),無(wú)人機(jī)影像影像邊緣畸變較大,裁剪后的影像邊緣基本被剪掉,可以大大提高影像的配準(zhǔn)精度。針對(duì)影像裁剪的這一特點(diǎn),提出影像裁剪和抽稀相結(jié)合的方式對(duì)影像進(jìn)行處理,進(jìn)一步縮短影像拼接用時(shí),改善了影像邊緣畸變對(duì)影像拼接成圖的影響。
[Abstract]:Unmanned aerial vehicle (UAV) images have a high degree of overlap, too many amplitudes, and it takes a lot of time to concatenate images. Without affecting the quality of data, how to reduce the redundancy of aerial photographs is the main problem in this paper. Based on the land right confirmation project of a county in Gansu province and the measured UAV image as the basic data, this paper analyzes and researches on reducing the overlap degree and data redundancy of UAV image. The specific research contents and results are as follows: 1. Firstly, the image overlap degree of UAV data in the study area is calculated. Combined with the optimal overlap degree and specification requirements of UAV, it is found that there is a lot of redundancy in UAV images. Aiming at the characteristics of high overlap degree of UAV images, thinning methods with different intervals are used to dilute the images. The experimental results show that the larger the thinning interval is, the less time is used for image stitching and the greater the error in plane position is. At intervals of 4 images, the critical value of image overlap degree is reached, and the time of stitching is more than half of that of unextracted images. By constructing a sample database containing 138 aerial remote sensing images and using the method of knowledge base, on the basis of 10 points system, the weighted mean standard deviation (WMSD) is put forward. The average gradient and information entropy are used to evaluate the image quality, and a thinning method based on the comprehensive evaluation value is established. Its image stitching time is nearly half shorter than that of all image stitching, and the "white spot" phenomenon caused by overexposure image is obviously reduced, and the recognition degree of image is improved obviously, and the good control of the error in the plane position of the image is obtained. Image clipping also reduces image overlap and speeds up UAV image matching. At the same time, the edge distortion of UAV image is large, and the edge of the cut image is basically cut off, which can greatly improve the registration accuracy of the image. In view of this feature of image clipping, the combination of image clipping and rarefaction is proposed to further shorten the image stitching time and improve the influence of image edge distortion on image mosaic.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P237

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 李二俊;劉萬(wàn)林;余濤;謝東海;蔡慶空;;基于SURF算法的無(wú)人機(jī)航空?qǐng)D像自動(dòng)配準(zhǔn)研究[J];工程勘察;2013年10期

2 闞峻嶺;谷宗運(yùn);殷云霞;杜春敏;;基于ASIFT的醫(yī)學(xué)圖像配準(zhǔn)算法[J];安慶師范學(xué)院學(xué)報(bào)(自然科學(xué)版);2013年01期

3 楊小輝;王敏;;基于ASIFT的無(wú)縫圖像拼接方法[J];計(jì)算機(jī)工程;2013年02期

4 劉慶元;劉有;鄒磊;易柳城;;無(wú)人機(jī)遙感影像拼接方法探討[J];測(cè)繪通報(bào);2012年05期

5 殷瑞靜;陳水利;;基于ASIFT的低空遙感影像拼接技術(shù)[J];集美大學(xué)學(xué)報(bào)(自然科學(xué)版);2012年02期

6 陳碩;吳成東;陳東岳;;基于視覺(jué)顯著性特征的快速場(chǎng)景配準(zhǔn)方法[J];中國(guó)圖象圖形學(xué)報(bào);2011年07期

7 安建妮;劉貴喜;;利用特征點(diǎn)配準(zhǔn)和變換參數(shù)自動(dòng)辨識(shí)的圖像拼接算法[J];紅外與激光工程;2011年03期

8 謝雨來(lái);李醒飛;呂津瑋;高雅彪;;基于SURF算法的水下圖像實(shí)時(shí)配準(zhǔn)方法[J];計(jì)算機(jī)輔助設(shè)計(jì)與圖形學(xué)學(xué)報(bào);2010年12期

9 蘇俊英;;SIFT特征匹配無(wú)人飛艇多光譜影像拼接[J];應(yīng)用科學(xué)學(xué)報(bào);2010年06期

10 魯恒;李永樹(shù);何敬;陳強(qiáng);任志明;;一種基于特征點(diǎn)的無(wú)人機(jī)影像自動(dòng)拼接方法[J];地理與地理信息科學(xué);2010年05期

相關(guān)碩士學(xué)位論文 前6條

1 陶青松;基于ASIFT特征的圖像匹配技術(shù)研究[D];云南大學(xué);2012年

2 周茜;遙感影像自動(dòng)拼接技術(shù)研究[D];西安電子科技大學(xué);2012年

3 韓文超;基于POS系統(tǒng)的無(wú)人機(jī)遙感圖像拼接技術(shù)研究與實(shí)現(xiàn)[D];南京大學(xué);2011年

4 曹紅杏;基于特征的圖像拼接技術(shù)研究[D];中國(guó)科學(xué)院研究生院（西安光學(xué)精密機(jī)械研究所）;2008年

5 張春美;特征點(diǎn)提取及其在圖像匹配中的應(yīng)用研究[D];解放軍信息工程大學(xué);2008年

6 劉冬梅;圖像拼接算法研究[D];西安電子科技大學(xué);2008年

，

本文編號(hào)：2399049