本文選題：變化檢測(cè)　切入點(diǎn)：像斑類別異質(zhì)度　出處：《成都理工大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：地理國(guó)情數(shù)據(jù)是各種地理信息的綜合和集成,反映國(guó)家自然資源、國(guó)家生態(tài)環(huán)境發(fā)展現(xiàn)狀,成為制定國(guó)家和區(qū)域發(fā)展戰(zhàn)略與發(fā)展規(guī)劃的重要基礎(chǔ),能夠表達(dá)地表特征和地表變化情況,通過(guò)一些變化檢測(cè)的方法可以將兩個(gè)時(shí)期的地理國(guó)情數(shù)據(jù)進(jìn)行檢測(cè)并得到檢測(cè)結(jié)果。本文主要研究地理國(guó)情數(shù)據(jù)變化檢測(cè)的方法,首先介紹了變化檢測(cè)理論、數(shù)據(jù)源、基本單元和變化檢測(cè)一般流程,變化檢測(cè)的數(shù)據(jù)源和基本單位在變化檢測(cè)過(guò)程中是非常重要的研究對(duì)象。變化檢測(cè)的一般流程又分為數(shù)據(jù)預(yù)處理、地理特征的提取、變化檢測(cè)和精度評(píng)估分析等。本文變化檢測(cè)采用的數(shù)據(jù)源是舊時(shí)期的矢量圖和新時(shí)期的遙感影像,變化檢測(cè)的基本單元是像斑。變化檢測(cè)方法是基于異質(zhì)度的地理國(guó)情數(shù)據(jù)變化檢測(cè)方法,該方法為了實(shí)現(xiàn)矢量圖與遙感影像的自動(dòng)變化檢測(cè),提出了一種基于像斑類別異質(zhì)度的矢量圖與遙感影像變化檢測(cè)方法。以矢量圖為約束,對(duì)遙感影像進(jìn)行影像分割獲取像斑,采用標(biāo)記分水嶺算法實(shí)現(xiàn)矢量圖約束的影像分割,在分水嶺的約束下利用傳統(tǒng)的標(biāo)記分水嶺算法實(shí)現(xiàn)影像分割;提取分割后像斑的直方圖作為像斑的特征,利用直方圖表達(dá)像斑的特征后,像斑的特征距離就轉(zhuǎn)換為直方圖的距離,再用G統(tǒng)計(jì)量的算法計(jì)算直方圖距離,利用像斑與舊時(shí)期同類別像斑特征距離的均值來(lái)構(gòu)建像斑的類別異質(zhì)度,像斑類別異質(zhì)度表示像斑與其舊時(shí)期所屬地物類別間的異質(zhì)性;采用大津法獲取各類別的異質(zhì)度閾值,將像斑的類別異質(zhì)度與所屬地類的類別異質(zhì)度閾值進(jìn)行比較,實(shí)現(xiàn)像斑的變化/未變化的判別。采用C++語(yǔ)言,基于GDAL(Geospatial Data Abstraction Library)開(kāi)源庫(kù)與ArcEngine平臺(tái),建設(shè)了地理國(guó)情常態(tài)化監(jiān)測(cè)數(shù)據(jù)變化檢測(cè)系統(tǒng)。系統(tǒng)實(shí)現(xiàn)了柵格與矢量數(shù)據(jù)的加載與疊加顯示、帶約束影像分割、像斑特征提取、像斑異質(zhì)度構(gòu)建、異質(zhì)度閾值獲取等功能。使用2014年的地理國(guó)情普查的地表覆蓋矢量數(shù)據(jù)與2016年QucikBird遙感影像做實(shí)驗(yàn)分析,為了驗(yàn)證本文方法的有效性,將本文方法同基于像斑灰度均值的方法進(jìn)行了對(duì)比,正確率提升了0.08,誤檢率與漏檢率分別下降了0.27、0.12。
[Abstract]:Geographical national data is the synthesis and integration of all kinds of geographic information, reflecting the development status of national natural resources and national ecological environment, and becoming an important basis for the formulation of national and regional development strategies and development plans. It can express the surface characteristics and the change of the surface, and can detect the data of two periods by some methods of change detection. This paper mainly studies the method of the change detection of the data of the geographical situation. First of all, it introduces the theory of change detection, data source, basic unit and general process of change detection. The data sources and basic units of change detection are very important research objects in the process of change detection. The general process of change detection is divided into data preprocessing, extraction of geographical features, The data sources used in this change detection are vector images of the old period and remote sensing images of the new period. The basic unit of change detection is the image spot. The change detection method is based on the heterogeneity of the geographic national conditions data change detection method, in order to realize the automatic change detection of vector image and remote sensing image, In this paper, a method of vector image and remote sensing image change detection based on the heterogeneity of image spot category is proposed. With vector image as constraint, remote sensing image is segmented to obtain image spot, and image segmentation with vector constraints is realized by using marked watershed algorithm. Under the constraint of watershed, the traditional marking watershed algorithm is used to realize image segmentation, and the histogram of image spot after segmentation is extracted as the feature of image spot, and the feature of image spot is represented by histogram. The feature distance of the image spot is transformed into the distance of the histogram, then the histogram distance is calculated by the algorithm of G statistic, and the class heterogeneity of the image spot is constructed by using the mean value of the feature distance between the image spot and the same image spot of the old period. The heterogeneity degree of image spot category indicates the heterogeneity between image spot and its old feature category, and the threshold value of different heterogeneity degree is obtained by using the method of Dajin, and the class heterogeneity degree of image spot is compared with that of the category heterogeneity of belongs to ground category. Based on the GDAL(Geospatial Data Abstraction Library open source library and ArcEngine platform, the system of detecting the change of the change of the normal monitoring data of the geographical national conditions is built. The system realizes the loading and superposition display of the raster and vector data, which is based on the C language and the open source library and ArcEngine platform of GDAL(Geospatial Data Abstraction Library. The functions of constrained image segmentation, image spot feature extraction, image spot heterogeneity construction, heterogeneity threshold acquisition and so on are analyzed experimentally using the ground cover vector data from the 2014 General Survey of Geographical conditions and the 2016 QucikBird remote sensing image. In order to verify the effectiveness of this method, the method is compared with the method based on the gray mean of image spot. The correct rate is increased by 0.08, and the false detection rate and the missed detection rate are reduced by 0.27 and 0.12 respectively.
【學(xué)位授予單位】：成都理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：P208

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