本文選題：遙感圖像 + 圖像分割��； 參考：《西安電子科技大學(xué)》2014年碩士論文

【摘要】：遙感圖像目標(biāo)分割,是遙感圖像識(shí)別和圖像解譯的重要步驟。遙感圖像分割具有非常強(qiáng)的目標(biāo)特性和圖片類型針對(duì)性,因而目標(biāo)分割算法必須通過(guò)精心設(shè)計(jì),圖像分割才能達(dá)到良好的效果。港口目標(biāo)作為一個(gè)國(guó)家重要的軍事基地和航運(yùn)交通樞紐,研究關(guān)于它的信息,具有重大戰(zhàn)略意義和民用意義。遙感圖像中港口目標(biāo)的分割和研究,當(dāng)今時(shí)代,無(wú)論在軍事領(lǐng)域還是民用領(lǐng)域都是至關(guān)重要的研究任務(wù),也是圖像解譯領(lǐng)域的一個(gè)熱點(diǎn)研究問(wèn)題。針對(duì)遙感圖像中港口目標(biāo)的分割和提取研究任務(wù),因其目標(biāo)與背景的復(fù)雜性和特征的多樣性,要達(dá)到精確的分割效果,需要針對(duì)特定目標(biāo)選擇合適的分割算法。本文的主要內(nèi)容是遙感圖像中港口目標(biāo)的分割算法研究與實(shí)現(xiàn),遙感圖像來(lái)源于google earth。選取兩幅遙感圖像,分析港口目標(biāo)的特征信息:灰度特征及結(jié)構(gòu)特征。在此基礎(chǔ)上選擇閾值分割法和脈沖耦合神經(jīng)網(wǎng)絡(luò)分割法,實(shí)現(xiàn)圖像分割。本文的主要研究?jī)?nèi)容如下:1.介紹了圖像閾值分割法,在一維最大類間方差法的基礎(chǔ)上研究了二維最大類間方差法,確定圖像分割的最佳閾值,實(shí)現(xiàn)港口目標(biāo)的分割。2.詳細(xì)研究了一種改進(jìn)的圖像閾值分割方法,加權(quán)參數(shù)直覺(jué)模糊熵閾值分割算法。在直覺(jué)模糊熵分割法的基礎(chǔ)上增加了權(quán)參數(shù),改善了直覺(jué)模糊集缺少對(duì)模糊不確定性度量的局限性。目的為改善傳統(tǒng)閾值分割法的分割效果,以及在一定程度上消除或抑制云霧因素對(duì)分割結(jié)果的影響。3.考慮到閾值分割方法自身的局限性,一個(gè)或一組閾值無(wú)法精確分割遙感圖像中的港口目標(biāo),在此基礎(chǔ)上研究了基于神經(jīng)網(wǎng)絡(luò)的分割算法:脈沖耦合神經(jīng)網(wǎng)絡(luò)(PCNN-Pulse Coupled Neural Networks)分割算法,介紹了此算法的核心內(nèi)容和分割原理,考慮到算法自身的局限性,加入了圖像邊緣算法及去噪步驟,對(duì)分割結(jié)果進(jìn)行優(yōu)化。實(shí)現(xiàn)上述幾種分割算法,通過(guò)對(duì)分割結(jié)果的分析與討論,驗(yàn)證它們的可行性。結(jié)果表明了這些算法在一定程度上提高了分割效率并且優(yōu)化了分割結(jié)果。
[Abstract]:Object segmentation of remote sensing image is an important step in remote sensing image recognition and image interpretation. Remote sensing image segmentation has very strong target characteristics and image type pertinence, so the target segmentation algorithm must be carefully designed in order to achieve good results. As an important military base and shipping transportation hub of a country, it is of great strategic and civil significance to study the information about port target. The segmentation and research of port targets in remote sensing images is an important research task in both military and civil fields, and it is also a hot research issue in the field of image interpretation. Due to the complexity of the target and background and the diversity of the features, it is necessary to select the appropriate segmentation algorithm for the specific target in order to achieve the accurate segmentation effect for the research task of port target segmentation and extraction in remote sensing image. The main content of this paper is the research and implementation of the segmentation algorithm of port target in remote sensing image, which comes from google earthland. Two remote sensing images are selected to analyze the feature information of port target: grayscale feature and structure feature. On this basis, the threshold segmentation method and the pulse coupled neural network segmentation method are selected to realize image segmentation. The main contents of this paper are as follows: 1. In this paper, the method of image threshold segmentation is introduced. Based on the one-dimensional maximum inter-class variance method, the two-dimensional maximum inter-class variance method is studied to determine the optimal threshold value of image segmentation, and to realize the segmentation of port target. An improved image threshold segmentation method, weighted parameter intuitionistic fuzzy entropy threshold segmentation algorithm, is studied in detail. Based on the method of intuitionistic fuzzy entropy segmentation, the weight parameter is added, and the limitation of the lack of fuzzy uncertainty measurement in intuitionistic fuzzy set is improved. Objective to improve the segmentation effect of the traditional threshold segmentation method and to eliminate or suppress the influence of cloud and fog factors on the segmentation results to a certain extent. Considering the limitation of threshold segmentation method, one or a group of thresholds can not accurately segment port targets in remote sensing images. On this basis, a neural network segmentation algorithm based on neural network is studied: PCNN-Pulse coupled Neural Network (PNN) segmentation algorithm. The core content and segmentation principle of the algorithm are introduced. Considering the limitations of the algorithm, the image edge algorithm and denoising steps are added to optimize the segmentation results. By analyzing and discussing the segmentation results, the feasibility of these algorithms is verified. The results show that these algorithms improve the segmentation efficiency to some extent and optimize the segmentation results.
【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 鄭瑋;基于模糊馬爾科夫隨機(jī)場(chǎng)的遙感圖像分割算法研究[D];電子科技大學(xué);2007年

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本文編號(hào)：2022193