【摘要】：圖像分割是一項(xiàng)應(yīng)用于圖像領(lǐng)域的技術(shù)手段,也是當(dāng)下研究人員研究圖像處理方面一項(xiàng)關(guān)鍵性題目。至今,其方法仍在不斷發(fā)展,比如基于邊緣、區(qū)域、閾值以及聚類方法。然而針對(duì)大型機(jī)場(chǎng)遙感圖的分割技術(shù)卻研究甚少,單一的算法難以分割機(jī)場(chǎng)目標(biāo),所以多方法結(jié)合的分割研究,值得我們深入研究。本文針對(duì)大型機(jī)場(chǎng)遙感圖像目標(biāo),進(jìn)行分割研究,并將分割結(jié)果使用到實(shí)驗(yàn)室紅外大場(chǎng)景建模中去,為后續(xù)項(xiàng)目的應(yīng)用提供很好的前提準(zhǔn)備。正如大家所熟知,不同的目標(biāo)使用不同的分割法,會(huì)有不同分割效果,所以找到針對(duì)機(jī)場(chǎng)目標(biāo)的分割方法尤為關(guān)鍵。考慮到機(jī)場(chǎng)遙感圖中分割目標(biāo)多,邊緣參差不齊,外加航拍圖像經(jīng)常會(huì)受到天氣等影響,呈現(xiàn)模糊等特點(diǎn),傳統(tǒng)方法很難得到好的分割結(jié)果。FCM算法是近年來應(yīng)用比較廣,針對(duì)邊緣模糊、分割目標(biāo)多,效果較好的方法,機(jī)場(chǎng)航拍圖像非常符合其分割目標(biāo)特征。因此,我們對(duì)FCM方法做一研究,對(duì)傳統(tǒng)FCM方法進(jìn)行改進(jìn),克服了傳統(tǒng)FCM的初始聚類中心選定的隨意性而導(dǎo)致的迭代次數(shù)多、效率差,以及傳統(tǒng)的方法易受噪聲影響的缺點(diǎn),從而提高了算法的效率以及抗噪性,使得算法強(qiáng)大和實(shí)用性更強(qiáng)。多方法結(jié)合分割大型機(jī)場(chǎng)遙感圖。首先,改進(jìn)傳統(tǒng)的FCM算法,對(duì)機(jī)場(chǎng)目標(biāo)進(jìn)行初分割,然后應(yīng)用Canny算子,提取邊緣,接著形態(tài)學(xué)半自動(dòng)方式優(yōu)化處理輪廓,最后將優(yōu)化的輪廓與原始圖匹配,提取出跑道目標(biāo);其次,進(jìn)行實(shí)驗(yàn)驗(yàn)證本文算法的效率和普適性,并討論未分割的建筑物群區(qū)域,提取出建筑物群;最后實(shí)現(xiàn)了多級(jí)分割機(jī)場(chǎng)目標(biāo)�？偨Y(jié)論文,闡述本文的優(yōu)勢(shì)和不足,以及后期還要努力的方向。實(shí)驗(yàn)結(jié)果表明:本文算法能得到良好的機(jī)場(chǎng)目標(biāo)分割效果,易于實(shí)現(xiàn)和理解,現(xiàn)實(shí)意義很大。
[Abstract]:Image segmentation is a technical method applied in the field of image, and it is also a key topic in the field of image processing. Up to now, its methods, such as edge, region, threshold and clustering methods, are still being developed. However, there are few researches on the segmentation of large airport remote sensing images, and it is difficult for a single algorithm to segment the airport objects. Therefore, the multi-method combined segmentation research is worth our in-depth study. In this paper, the segmentation of large airport remote sensing images is studied, and the segmentation results are applied to the modeling of large infrared scene in laboratory, which provides a good premise for the application of subsequent projects. As we all know, different targets use different segmentation methods, there will be different segmentation effects, so it is very important to find a segmentation method for airport targets. Considering that there are many segmentation targets and uneven edges in airport remote sensing images, and the aerial images are often affected by the weather and appear fuzzy, it is difficult to obtain good segmentation results by traditional methods. FCM algorithm is widely used in recent years. Aiming at the method of edge blur, multi-target segmentation and better effect, the airport aerial image is very consistent with the segmentation target feature. Therefore, we study the FCM method and improve the traditional FCM method, which overcomes the disadvantages of the random selection of the initial clustering center of the traditional FCM, which results in a lot of iterations, low efficiency, and the traditional method is susceptible to noise. Thus, the efficiency and noise resistance of the algorithm are improved, which makes the algorithm more powerful and practical. Multi-method combined with segmentation of large airport remote sensing images. Firstly, the traditional FCM algorithm is improved to segment the airport target first, then the edge is extracted by using Canny operator, then the contour is optimized by morphological semi-automatic method. Finally, the optimized contour is matched with the original image, and the runway target is extracted. Secondly, experiments are carried out to verify the efficiency and universality of the proposed algorithm, and the undivided area of the building cluster is discussed to extract the building group. Finally, the multi-level segmentation of the airport target is realized. This paper summarizes the advantages and disadvantages of this paper, as well as the direction of the later efforts. The experimental results show that the proposed algorithm can achieve good segmentation effect and is easy to be realized and understood.
【學(xué)位授予單位】：西安電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)碩士學(xué)位論文 前1條

1 趙雁;基于FCM算法的圖像分割技術(shù)研究[D];哈爾濱工業(yè)大學(xué);2012年

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本文編號(hào)：2401681