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

【摘要】：遙感圖像目標分割,是遙感圖像識別和圖像解譯的重要步驟。遙感圖像分割具有非常強的目標特性和圖片類型針對性,因而目標分割算法必須通過精心設計,圖像分割才能達到良好的效果。港口目標作為一個國家重要的軍事基地和航運交通樞紐,研究關于它的信息,具有重大戰(zhàn)略意義和民用意義。遙感圖像中港口目標的分割和研究,當今時代,無論在軍事領域還是民用領域都是至關重要的研究任務,也是圖像解譯領域的一個熱點研究問題。針對遙感圖像中港口目標的分割和提取研究任務,因其目標與背景的復雜性和特征的多樣性,要達到精確的分割效果,需要針對特定目標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的分割算法研究與實現(xiàn),遙感圖像來源于google earth。選取兩幅遙感圖像,分析港口目標的特征信息:灰度特征及結構特征。在此基礎上選擇閾值分割法和脈沖耦合神經(jīng)網(wǎng)絡分割法,實現(xiàn)圖像分割。本文的主要研究內(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,實現(xiàn)港口目標的分割。2.詳細研究了一種改進的圖像閾值分割方法,加權參數(shù)直覺模糊熵閾值分割算法。在直覺模糊熵分割法的基礎上增加了權參數(shù),改善了直覺模糊集缺少對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分割結果的影響。3.考慮到閾值分割方法自身的局限性,一個或一組閾值無法精確分割遙感圖像中的港口目標,在此基礎上研究了基于神經(jīng)網(wǎng)絡的分割算法:脈沖耦合神經(jīng)網(wǎng)絡(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,對分割結果進行優(yōu)化。實現(xiàn)上述幾種分割算法,通過對分割結果的分析與討論,驗證它們的可行性。結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.
【學位授予單位】：西安電子科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TP751

【參考文獻】

相關碩士學位論文 前1條

1 鄭瑋;基于模糊馬爾科夫隨機場的遙感圖像分割算法研究[D];電子科技大學;2007年

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本文編號：2022193