本文關(guān)鍵詞： 變化檢測(cè) 土地利用 對(duì)象特征 特征融合 數(shù)據(jù)維壓縮 監(jiān)督分類　出處：《昆明理工大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：及時(shí)、快速、準(zhǔn)確地獲取土地利用信息是各級(jí)政府開(kāi)展國(guó)土資源規(guī)劃、管理、保護(hù)和合理利用的前提條件和基本依據(jù)。利用多源,多時(shí)相,多分辨率遙感影像進(jìn)行土地利用變化檢測(cè),可獲取多目標(biāo)、多周期、多尺度的變化信息。論文以同一個(gè)研究區(qū)域不同范圍的QuickBird影像和Landsat多光譜數(shù)據(jù)為研究對(duì)象,從兩個(gè)方面展開(kāi)研究： 一、在深入研究常用遙感變化檢測(cè)方法的基礎(chǔ)上,針對(duì)傳統(tǒng)變化檢測(cè)方法在高分辨率遙感影像變化檢測(cè)中的局限性,利用面向?qū)ο蠓治黾夹g(shù)對(duì)實(shí)驗(yàn)區(qū)多時(shí)相QuickBird影像進(jìn)行變化檢測(cè)實(shí)驗(yàn),檢測(cè)出變化圖斑后與實(shí)驗(yàn)區(qū)更高分辨率的航攝影像及歷史矢量數(shù)據(jù)進(jìn)行對(duì)比,來(lái)分析該方法在土地利用變化檢測(cè)中的適用性； 二、基于需要了解某個(gè)時(shí)間段研究區(qū)域內(nèi)的土地利用變化趨勢(shì)但又缺乏歷史矢量數(shù)據(jù)及高分辨率遙感影像的假設(shè),以Landsat多光譜數(shù)據(jù)為研究對(duì)象,結(jié)合相應(yīng)的指數(shù),利用壓縮數(shù)據(jù)維的方法進(jìn)行分類后變化檢測(cè),獲取研究區(qū)域主要土地利用類型的變化信息,其具體研究?jī)?nèi)容及成果如下： (1)對(duì)遙感變化檢測(cè)的基本理論進(jìn)行深入研究,并概括分析了常用變化檢測(cè)方法的原理及優(yōu)缺點(diǎn)。 (2)以研究區(qū)域某個(gè)村為實(shí)驗(yàn)對(duì)象,對(duì)實(shí)驗(yàn)區(qū)多時(shí)相QuickBird影像進(jìn)行多尺度分割后,提取影像對(duì)象的光譜特征、紋理特征和形狀特征圖,利用提取的對(duì)象特征進(jìn)行變化檢測(cè),由于單一的特征所提取的信息具有一定的不確定性,不能唯一地準(zhǔn)確判定地表的變化信息,實(shí)驗(yàn)將特征級(jí)變化檢測(cè)結(jié)果進(jìn)行融合作為最終的檢測(cè)結(jié)果。后將實(shí)驗(yàn)結(jié)果與實(shí)驗(yàn)區(qū)0.2米航片、土地利用變更調(diào)查及基本農(nóng)田規(guī)劃數(shù)據(jù)制作的本底數(shù)據(jù)進(jìn)行對(duì)比分析,結(jié)果表明此方法能最大程度的檢測(cè)變化信息,但要運(yùn)用到實(shí)際的土地利用變化檢測(cè)項(xiàng)目中,需要對(duì)變化檢測(cè)結(jié)果進(jìn)行大量的篩選。 (3)以整個(gè)研究區(qū)域?yàn)閷?shí)驗(yàn)對(duì)象,通過(guò)分析研究區(qū)域主要的土地利用類型和地物分布特點(diǎn),選取土壤調(diào)節(jié)植被指數(shù)、歸一化建筑用地指數(shù)及修正歸一化差異水體指數(shù)代表實(shí)驗(yàn)區(qū)的三大主要土地利用類型—植被、建筑用地及水體。將TM影像6個(gè)波段及ETM影像7個(gè)波段壓縮為由它們衍生的3個(gè)采用比值運(yùn)算構(gòu)成的指數(shù)波段,并分析三個(gè)指數(shù)波段的相關(guān)性,將相關(guān)性很小的兩個(gè)波段作為XY軸構(gòu)成二維散點(diǎn)圖,利用基于幾何頂點(diǎn)的端元提取方法選擇分類樣本后對(duì)指數(shù)影像進(jìn)行分類后變化檢測(cè)。實(shí)驗(yàn)結(jié)果表明基于相同的分類樣本和分類方法,壓縮后的指數(shù)影像分類精度高于原始影像,利用壓縮數(shù)據(jù)維的分類后變化檢測(cè)方法可有效檢測(cè)研究區(qū)域主要土地利用類型的變化信息。
[Abstract]:Timely, rapid and accurate access to land use information is the prerequisite and basic basis for governments at all levels to carry out land and resource planning, management, protection and rational utilization. Multi-resolution remote sensing images can obtain multi-target, multi-period and multi-scale information for land use change detection. In this paper, QuickBird images and Landsat multispectral data of different range in the same research area are taken as the research object. The research is carried out from two aspects:. First, on the basis of in-depth study of common remote sensing change detection methods, aiming at the limitation of traditional change detection methods in high-resolution remote sensing image change detection, The object oriented analysis (OOA) technique is used to detect the change of multitemporal QuickBird images in the experimental area, and the change pattern is detected and compared with the higher resolution aerial photography and historical vector data in the experimental area. To analyze the applicability of this method in land use change detection; Secondly, based on the assumption that land use change trends in a certain period of time need to be understood but lack of historical vector data and high-resolution remote sensing images, the multi-spectral data of Landsat are taken as the research object and the corresponding indices are combined. The method of compressed data dimension is used to detect the changes after classification and obtain the change information of the main land use types in the study area. The specific research contents and results are as follows:. 1) the basic theory of remote sensing change detection is deeply studied, and the principle, advantages and disadvantages of the commonly used change detection methods are summarized. Taking a village in the research area as the experimental object, the multitemporal QuickBird image of the experimental area is segmented with multiple scales, then the spectral feature, texture feature and shape feature map of the image object are extracted, and the change detection is carried out by using the extracted object feature. Because the information extracted by a single feature has certain uncertainty, it is not possible to judge the change information of the earth's surface only accurately. In the experiment, the feature level change detection results are fused as the final detection results, and then the experimental results are compared with the background data of 0.2 m aerial photograph, land use change survey and basic farmland planning data in the experimental area. The results show that this method can detect the change information to the maximum extent, but in order to apply to the actual land use change detection project, a large number of change detection results need to be screened. Taking the whole study area as the experimental object, through analyzing the main land use types and the distribution characteristics of the ground objects in the study area, selecting the soil adjustment vegetation index, Normalized construction land index and modified normalized difference water body index represent the three main land use types in the experimental area-vegetation. Six bands of TM image and seven bands of ETM image are compressed into three exponential bands derived from TM image by ratio operation, and the correlation of the three exponential bands is analyzed. Using the two bands with little correlation as the XY axis to form a two-dimensional scatter diagram, Based on the geometric vertex extraction method, the classification samples are selected and the changes of the index images are detected after classification. The experimental results show that the classification accuracy of the compressed exponential images is higher than that of the original images based on the same classification samples and classification methods. The change information of the main land use types in the studied area can be detected effectively by using the classified change detection method of compressed data dimension.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P237

【引證文獻(xiàn)】

相關(guān)博士學(xué)位論文 前2條

1 祝錦霞;高分辨率遙感影像變化檢測(cè)的關(guān)鍵技術(shù)研究[D];浙江大學(xué);2011年

2 吳劍;基于面向?qū)ο蠹夹g(shù)的遙感震害信息提取與評(píng)價(jià)方法研究[D];武漢大學(xué);2010年

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本文編號(hào)：1504452