面向?qū)ο蟮母叻直媛蔬b感影像植被信息提取研究
發(fā)布時(shí)間：2018-01-02 08:07
本文關(guān)鍵詞：面向?qū)ο蟮母叻直媛蔬b感影像植被信息提取研究 出處：《吉林大學(xué)》2015年碩士論文 論文類型：學(xué)位論文
更多相關(guān)文章： 面向?qū)ο?/b> 多尺度分割 高分辨率遙感影像 影像融合 植被信息提取
【摘要】：近些年來了解和掌握植被覆蓋變化趨勢(shì)已經(jīng)成為了人們?nèi)找骊P(guān)注的焦點(diǎn)，不少研究人員做了大量與植被覆蓋相關(guān)問題的研究，，從而對(duì)植被資源進(jìn)行合理、有效、高效的管理，以便促進(jìn)資源環(huán)境與社會(huì)經(jīng)濟(jì)協(xié)調(diào)發(fā)展。本論文主要針對(duì)于對(duì)植被信息提取的研究，目前對(duì)植被分類的研究比較薄弱，分類精度不高，本文選取朝鮮某地區(qū)作為研究區(qū)，采用面向?qū)ο蟮姆椒▉硌芯恐脖环诸惖姆椒�，從而提高植被分類的精度�?本文利用GeoEye 1的高分辨率遙感影像，基于面向?qū)ο蟮挠跋穹诸惙椒�，�?duì)研究區(qū)的植被信息進(jìn)行提取，主要成果如下： （1）研究中首先根據(jù)研究區(qū)的地質(zhì)特征和所采用的數(shù)據(jù)源，對(duì)數(shù)據(jù)進(jìn)行幾何校正和影像融合等預(yù)處理。研究中還針對(duì)影像融合技術(shù)進(jìn)行了深入分析，提出了小波和IHS相結(jié)合的方式，提高了融合的影像的質(zhì)量和精度，為后期植被信息提取打下基礎(chǔ)。 （2）本文通過多尺度分割實(shí)驗(yàn)，探討了影像進(jìn)行分割的尺度問題及其參數(shù)的選擇問題，得到了基于影像不同特征的最優(yōu)尺度。在確定了不同地物的最優(yōu)尺度的基礎(chǔ)上，利用面向?qū)ο蟮姆诸惙椒�，分別針對(duì)不同地物的最優(yōu)尺度，將高分辨率遙感影像大致分為林地，草地，建筑物，裸地，河流5類，并對(duì)其結(jié)果利用混淆矩陣進(jìn)行精度評(píng)價(jià)，精度為90.14%，kappa系數(shù)為0.8514. （3）利用基于像元的遙感影像分類方法對(duì)同一區(qū)域的影像進(jìn)行監(jiān)督分類，非監(jiān)督分類和決策樹分類，進(jìn)行精度評(píng)價(jià)；結(jié)果表明，面向?qū)ο蟮姆诸惙椒ǚ诸惤Y(jié)果精度高于基于像元的分類方法，最后研究中利用最鄰近分類法和模糊分類的方法分別對(duì)林地和草地進(jìn)行了細(xì)分，實(shí)現(xiàn)了高分辨率遙感影像的植被信息提取。
[Abstract]:In recent years, understanding and mastering the trend of vegetation cover change has become the focus of increasing attention. Many researchers have done a lot of research on vegetation cover related issues, so that the vegetation resources are reasonable and effective. Efficient management, in order to promote the coordinated development of resources, environment and social economy. This paper mainly focuses on the study of vegetation information extraction, the current research on vegetation classification is relatively weak, classification accuracy is not high. In this paper, a certain area of North Korea is selected as the research area, and the method of vegetation classification is studied by using object-oriented method, so as to improve the accuracy of vegetation classification. In this paper, the high resolution remote sensing image of GeoEye 1 is used to extract the vegetation information from the study area based on the object oriented image classification method. The main results are as follows: Firstly, according to the geological characteristics of the study area and the data sources used, the data are preprocessed by geometric correction and image fusion. In the study, the image fusion technology is also deeply analyzed. The method of combining wavelet with IHS is proposed to improve the quality and accuracy of the fusion image and to lay a foundation for the extraction of vegetation information in the later stage. In this paper, the scale problem of image segmentation and the selection of its parameters are discussed through multi-scale segmentation experiments. The optimal scale based on different features of the image is obtained. On the basis of determining the optimal scale of different ground objects, the object oriented classification method is used to target the optimal scale of different ground objects. The high-resolution remote sensing images are divided into five categories: woodland, grassland, buildings, bare land and rivers. The accuracy of the results is evaluated by using the confusion matrix, and the accuracy is 90.14%. The kappa coefficient is 0.8514. Thirdly, the method of remote sensing image classification based on pixel is used to evaluate the accuracy of supervised classification, unsupervised classification and decision tree classification in the same region. The results show that the classification accuracy of the object-oriented classification method is higher than that of the pixel based classification method. In the end, the nearest neighbor classification method and fuzzy classification method are used to subdivide the forest land and grassland, respectively. The vegetation information extraction from high resolution remote sensing image is realized.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：P237
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