【摘要】：城市化帶來(lái)的大規(guī)模不透水地表擴(kuò)展己對(duì)城市生態(tài)環(huán)境造成重要影響,使原本透水性較好自然資源變成不具有透水性的建設(shè)用地,城市不透水地表的增加會(huì)加劇城市水資源的污染和植被的減少,使城市面臨著嚴(yán)重的生態(tài)環(huán)境問(wèn)題。城市不透水地表不僅僅是衡量城市化發(fā)展程度的重要指示器,還是衡量城市環(huán)境變化與社會(huì)經(jīng)濟(jì)發(fā)展的關(guān)鍵技術(shù)指標(biāo)之一。準(zhǔn)確有效地提取城市不透水地表信息,對(duì)城市可持續(xù)發(fā)展與規(guī)劃有著一定的指導(dǎo)作用。如何從環(huán)境復(fù)雜的城市地物中分類出詳細(xì)地物以及提取出不透水地表,是當(dāng)前遙感領(lǐng)域的熱點(diǎn)難點(diǎn)之一。本文以提取城區(qū)的不透水地表為最終目的,將湖南省衡陽(yáng)市某開發(fā)區(qū)作為研究區(qū),用機(jī)載LiDAR數(shù)據(jù)結(jié)合高分辨率影像數(shù)據(jù)協(xié)同聯(lián)合處理,采用面向?qū)ο蠓椒?分別對(duì)該城區(qū)的不透水地表信息提取使用單一影像數(shù)據(jù)和多源數(shù)據(jù)相對(duì)應(yīng)的分類方法得到分類結(jié)果,并對(duì)其進(jìn)行對(duì)比評(píng)價(jià)分析,完成精度較高的城市地物類型詳細(xì)分類。實(shí)現(xiàn)城市不透水地表信息的精細(xì)分類提取,為高精度不透水地表遙感估算提供了一種新的思路與方法。本文的創(chuàng)新性有:結(jié)合LiDAR與同機(jī)高分辨率航空影像,完成對(duì)城市地物信息的詳細(xì)分類;在僅有RGB影像的限制條件下,采用新型算法完成對(duì)不透水地表相關(guān)地物類型的提取;結(jié)合LiDAR與高分辨率航空影像的各自優(yōu)點(diǎn),完成對(duì)較易混淆地物類型的甄別。本文提出的地物分割尺度與分類方法體系,對(duì)于該類影像可提供有效的分類參考。得出的主要結(jié)論是:(1)對(duì)面向?qū)ο蠓治黾夹g(shù)進(jìn)行介紹,詳細(xì)論述了對(duì)影像的分割算法和模糊數(shù)學(xué)分類方法。主要對(duì)多尺度分割算法的理論、方法進(jìn)行了較為完善的概述,對(duì)分割參數(shù)的選定方法進(jìn)行了詳細(xì)的論述。其次,重點(diǎn)介紹了模糊分類理論,并在此基礎(chǔ)上闡述模糊分類規(guī)則及分類體系等。(2)構(gòu)建對(duì)研究區(qū)的R、G、B高分辨率航空影像的監(jiān)督分類工作流程與分類體系。多尺度分割是面向?qū)ο蠹夹g(shù)的關(guān)鍵及分類前提,故本文在所有分類步驟之前先對(duì)影像進(jìn)行多尺度分割,找到適用于城市各地物類型的分割參數(shù),最后根據(jù)監(jiān)督分類規(guī)則與方法,得到城區(qū)的詳細(xì)地物的分類結(jié)果以及不透水地表。(3)建立一套針對(duì)城市不透水地表信息提取的技術(shù)流程、分類體系及規(guī)則。綜合LiDAR數(shù)據(jù)和同機(jī)獲取的航空影像兩種數(shù)據(jù)源提取城區(qū)的不透水地表,采用模糊分類方法,創(chuàng)建一種適用于城市不透水地表的提取方法。分別用最佳分類結(jié)果和混淆矩陣精度驗(yàn)證方法對(duì)結(jié)果進(jìn)行評(píng)價(jià)分析,結(jié)果表明,該方法可得到較滿意的城區(qū)復(fù)雜地物的不透水地表信息。(4)多源數(shù)據(jù)協(xié)同方法可提高影像分類精度。通過(guò)單一航空影像和影像與LiDAR數(shù)據(jù)聯(lián)合這兩種形式提取城市不透水地表,對(duì)比二者的分類結(jié)果精度評(píng)價(jià),發(fā)現(xiàn)多源數(shù)據(jù)在提取不透水地表時(shí)可通過(guò)數(shù)據(jù)的優(yōu)勢(shì)互補(bǔ),使得分類結(jié)果最優(yōu)化。同時(shí)利用二者數(shù)據(jù)特征結(jié)合的方法,對(duì)可分性較差的地物,如裸地等此類特殊地物提出詳細(xì)合適的分類方法與特征參考,并得到較為精確的分類結(jié)果。
[Abstract]:The large-scale impermeable surface expansion brought about by urbanization has an important impact on the urban ecological environment, which makes the original water permeable and good natural resources become non permeable construction land. The increase of urban water permeable surface will aggravate the pollution of urban water resources and reduce the vegetation, and make the city face serious ecological environment problems. Urban water permeable surface is not only an important indicator to measure the degree of urbanization, but also one of the key technical indicators to measure urban environmental change and social and economic development. Accurate and effective extraction of urban water permeable surface information has a certain guiding role for urban sustainable development and planning. It is one of the hot and difficult points in the field of remote sensing to classify the detailed objects and extract the impermeable surface. In this paper, a development area in Hengyang, Hunan province is taken as the research area, and the airborne LiDAR data combined with high resolution image data is combined together, and the object oriented method is adopted. The classification results of the unpermeable surface information of the urban area are extracted and used in the classification of the single image data and the multi source data, and the comparative evaluation and analysis are carried out to complete the detailed classification of the urban land types with high precision. The fine classification and extraction of the information of the urban water permeable surface is realized, and the high precision and the water surface is not permeable to the surface. Remote sensing estimation provides a new idea and method. The innovation of this paper is: combining LiDAR and high resolution aerial images to complete the detailed classification of urban ground information. Under the limited conditions of RGB image, a new algorithm is used to complete the extraction of the type of the surface related to the surface of the impervious surface; combined with LiDAR and high resolution aviation The main conclusions of this paper are as follows: (1) the main conclusions are as follows: (1) the object oriented analysis technology is introduced, and the image segmentation algorithm and fuzzy mathematical classification are discussed in detail. Methods. The theory and method of multi scale segmentation algorithm are summarized, and the selected methods of the segmentation parameters are discussed in detail. Secondly, the fuzzy classification theory is introduced, and the fuzzy classification rules and classification systems are expounded on this basis. (2) the high resolution aerial images of R, G and B in the research area are constructed. Supervised classification workflow and classification system. Multi-scale segmentation is the key of object oriented technology and classification premise, so this paper divides the image into multi scale before all the classification steps, and finds the segmentation parameters suitable for various types of city objects. Finally, according to the supervised classification rules and methods, the detailed classification of the urban area is obtained. The results and impermeable surface. (3) establish a set of technical process, classification system and rules for urban water permeable surface information extraction, integrated LiDAR data and two kinds of data sources obtained by the same machine to extract the water surface of the urban area, and use the fuzzy classification method to create a method for extracting the water surface of the city. The results are evaluated and analyzed by the best classification results and the obfuscation matrix accuracy verification method. The results show that the method can obtain more satisfactory surface information of the complex terrain in urban areas. (4) the multi source data coordination method can improve the image classification accuracy. The two forms are combined with the single aerial image and the image and LiDAR data. Taking the urban impervious surface and evaluating the accuracy of the classification results of the two, it is found that the multi source data can be optimized by the advantages of the data in the extraction of the impervious surface and make the classification results optimized. At the same time, using the method of combining the characteristics of the two data, the detailed and appropriate points are put forward for the special objects such as the bare land, such as the bare land. Class method and feature reference, and get more accurate classification results.
【學(xué)位授予單位】：新疆大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P237

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本文編號(hào)：2145791