本文選題：遙感圖像　切入點(diǎn)：紋理識(shí)別　出處：《江蘇大學(xué)》2014年博士論文

【摘要】：高分辨率遙感圖像的應(yīng)用,可以使我們獲取了更加精確、豐富和全面的信息。從遙感圖像中抽取出信息,通過識(shí)別出感興趣的目標(biāo)獲取知識(shí),完成圖像的理解是遙感圖像應(yīng)用的根本目標(biāo)。遙感圖像可以提供的信息中,道路信息是很重要的一部分。隨著道路信息的不斷更新,傳統(tǒng)的人工操作已無法滿足需求,于是,將遙感技術(shù)與電子技術(shù)及圖像識(shí)別技術(shù)結(jié)合起來,研究遙感圖像道路的自動(dòng)提取,對(duì)于道路監(jiān)控和GPS導(dǎo)航及地圖及時(shí)更新都有重大意義,也是目前國內(nèi)外研究的重點(diǎn)。 論文主要研究遙感圖像道路提取的方法,首先探討了在線數(shù)據(jù)庫中道路遙感圖像的識(shí)別分類,然后對(duì)遙感圖像云層進(jìn)行自動(dòng)識(shí)別的研究,以避免遙感圖像中存在云層覆蓋的情況,最后分別用兩種方法對(duì)遙感圖像中道路進(jìn)行提取。針對(duì)在線數(shù)據(jù)庫遙感圖像分類,提出基于文本與圖像信息融合的道路遙感圖像識(shí)別分類的方法,需要先提取圖像的文本特征與圖像特征,再將二者的特征進(jìn)行融合,通過支持向量機(jī)的訓(xùn)練,可得到較好的分類結(jié)果,該方法可移植到其他圖像在線數(shù)據(jù)庫分類識(shí)別與構(gòu)建上。針對(duì)遙感圖像云層,提取圖像的紋理特征,選擇4個(gè)紋理特征參數(shù)——角二階矩、對(duì)比度、相關(guān)度和熵對(duì)云層圖像進(jìn)行自動(dòng)識(shí)別。針對(duì)遙感圖像道路,首先使用結(jié)構(gòu)張量的方法進(jìn)行主方向的計(jì)算,并改進(jìn)了主方向的計(jì)算方法,結(jié)合Gibbs抽樣對(duì)道路進(jìn)行提取,該方法適用于有遮擋的道路,但無法說明道路的重要性；采用圓投影變換進(jìn)行道路提取,通過與初始模板匹配找到最優(yōu)模板來提取道路,該方法可以觀察到道路的重要性。 論文研究的具體內(nèi)容如下： (1)基于文本與圖像信息融合的道路遙感圖像識(shí)別。在線圖像數(shù)據(jù)庫中的圖像并不是在相同的實(shí)驗(yàn)室環(huán)境和相同的技術(shù)參數(shù)中統(tǒng)一測量的,因此,現(xiàn)有的方法并不能直接對(duì)道路遙感圖像進(jìn)行識(shí)別。針對(duì)這一問題,利用在線圖像數(shù)據(jù)庫中的道路遙感圖像及其注釋來獲得高精度的道路遙感圖像識(shí)別。利用空間金字塔關(guān)鍵字直方圖來描述圖像的特征,并通過融合圖像和文本信息提高了道路遙感圖像的識(shí)別精度。使用從圖像數(shù)據(jù)庫得到的圖像信息和文本信息訓(xùn)練支持向量機(jī)以得到更高的分類精度,然后在整合所有信息后可以得到支持向量機(jī)的后驗(yàn)概率值和最終結(jié)果。相較于使用單獨(dú)的圖像特征或單獨(dú)的文本特征進(jìn)行識(shí)別,該方法具有較好的識(shí)別準(zhǔn)確率和分類性能,可移植到其他圖像在線數(shù)據(jù)庫分類識(shí)別與構(gòu)建上。 (2)基于紋理特征對(duì)遙感圖像云層自動(dòng)識(shí)別。針對(duì)高分辨率遙感圖像中云層的自動(dòng)識(shí)別問題,提出一種基于圖像紋理特征的云層自動(dòng)識(shí)別方法,通過灰度共生矩陣來對(duì)圖像中云層和下墊面的紋理特性進(jìn)行統(tǒng)計(jì)分析,選擇對(duì)云層和下墊面進(jìn)行有效區(qū)分的4個(gè)紋理特征參數(shù)——角二階矩、對(duì)比度、相關(guān)度和熵對(duì)圖像進(jìn)行識(shí)別,最后通過圖像空間域的云層識(shí)別方法來對(duì)紋理識(shí)別結(jié)果進(jìn)行修正,有效提高了云層識(shí)別的準(zhǔn)確性,為遙感圖像道路提取奠定基礎(chǔ)。 (3)基于結(jié)構(gòu)張量進(jìn)行遙感圖像道路提取。針對(duì)基于結(jié)構(gòu)張量的主方向計(jì)算方法計(jì)算結(jié)果不夠精確的缺點(diǎn),對(duì)高斯濾波進(jìn)行改進(jìn)并結(jié)合canny算子,提出了改進(jìn)的主方向計(jì)算方法,然后基于局部主方向結(jié)合Gibbs抽樣進(jìn)行遙感圖像道路提取。該算法適用于有遮擋的道路圖像,可以比較精確地對(duì)道路進(jìn)行提取。 (4)基于圓投影變換進(jìn)行遙感圖像道路提取。本文中基于結(jié)構(gòu)張量的遙感圖像道路提取方法不能表明道路的關(guān)鍵性,采用圓投影變換理論進(jìn)行遙感圖像道路提取,可以比較精確的提取道路,同時(shí)從結(jié)果圖中觀察到道路的重要性信息。
[Abstract]:The application of high resolution remote sensing image, so that we can get more accurate, rich and comprehensive information. The information extracted from the remote sensing images, acquire knowledge through identifying the target of interest, image comprehension is the fundamental goal of the application of remote sensing image. The remote sensing images can provide information, road information is a part of very important. With the road information update, the traditional manual operation has been unable to meet the demand, so the combination of remote sensing technology and electronic technology and image recognition technology, automatic extraction of remote sensing image for roads, road monitoring and GPS navigation and map update are of great significance, is also the focus of research at home and abroad.
The main research method of extracting remote sensing image of the road, first discusses the classification of remote sensing image Road online database, and then research on the automatic identification of the remote sensing images of clouds, cloud cover to avoid the presence of remote sensing images, finally on the road in the remote sensing images were extracted by two methods. According to the online database of remote sensing image the classification method of text and image information fusion road remote sensing image classification recognition based on the need to extract text features and image features, and then the characteristics of the two integration, through the training of support vector machine, can get good classification results, this method can be transplanted to other online database and the construction of image classification and recognition in remote sensing images. Cloud image texture feature extraction, corner two moments 4 texture feature parameters, contrast, correlation The degree and entropy of automatic detection of cloud image. On the remote sensing image path calculation method of first use of the structure tensor principal direction, and improved the calculation method of the main direction, combined with the Gibbs sampling of road extraction, this method is applicable to a sheltered Road, but not to illustrate the importance of the road for road extraction; the circular projection transformation, with the initial template matching template to find the optimal extraction path, this method can observe the importance of the road.
The specific contents of the thesis are as follows:
(1) remote sensing identification text and image information fusion based on road image. The online image database image is not a unified measurement in the laboratory environment and the same technical parameters, therefore, the existing methods can not be directly identified on the road of remote sensing image. To solve this problem, using the way of remote sensing image online image in the database and comments to obtain road remote sensing image recognition with high accuracy. To describe the image feature space Pyramid keyword histogram, and through the integration of image and text information and improve the way of remote sensing image recognition accuracy. The use of the image information from the image database and text information of training support vector machine to obtain higher classification accuracy then, in the integration of all the information can be obtained after the support vector machine a posteriori probability and the final results. Compared with the single use of the map The recognition method of image feature or individual text feature has good recognition accuracy and classification performance, and can be transplanted to other image online database classification, recognition and construction.
(2) texture features of remote sensing image automatic recognition based on cloud. Aiming at the problem of automatic recognition in high resolution remote sensing image clouds, proposes a method for automatic identification of image texture features based on the clouds, through the gray level co-occurrence matrix for the analysis of images of clouds and surface texture characteristics, choose 4 texture parameters to effectively distinguish between clouds and surface -- angle of two order moment, contrast, correlation and entropy for image recognition, and finally through the cloud image recognition method of spatial domain to texture recognition result, effectively improve the accuracy of the recognition, lay the foundation for the extraction of remote sensing image.
(3) based on the structure tensor of remote sensing image road extraction. Based on the results of the analysis calculation method of principal direction based on structure tensor is not precise enough shortcomings, combined with improved Canny operator on Gauss filter, puts forward the main direction of the improved calculation method, and then the local principal direction based on Gibbs sampling for remote sensing image. The algorithm for road extraction in a road image block, on the road can be extracted accurately.
(4) the circle projection transform based on remote sensing image road extraction. The key in this paper extraction method can not show the way of remote sensing image path based on structure tensor, remote sensing image road extraction by theory circle projection transform, can be extracted accurately from the road, at the same time in the observation to the importance of the road information.

【學(xué)位授予單位】：江蘇大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2014
【分類號(hào)】：TP751

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