本文選題：面向對象 + 灰度閾值分割�。� 參考：《中南大學》2013年碩士論文

【摘要】：在地理空間數(shù)據(jù)庫中建筑物是核心地形要素之一,同時也是城市環(huán)境中不可缺少的重要組成部分以及人類活動的重要聚居地。而隨著社會的發(fā)展,建筑用地不斷的發(fā)生變化,加快空間數(shù)據(jù)庫的更新也變得非常重要。遙感技術的發(fā)展,使得人類精確識別和提取地物信息,自動更新GIS數(shù)據(jù)庫成為可能。因此,本文著重圍繞如何從高分辨率遙感影像上提取建筑物輪廓展開研究,研究的主要內容如下： (1)利用影像中波段數(shù)值運算的原理與綠波段中植被的反射率比較大的特性,采用綠波段與紅波段的差值運算方法,消減其他地物的亮度值,增加植被亮度值,經過形態(tài)學修復之后,采用簡單灰度閾值分割的算法提取出植被,再進行掩膜處理,消除植被對后續(xù)建筑物提取的干擾；最后,采用相同分辨率的不同區(qū)域的遙感影像數(shù)據(jù)驗證該植被提取方法的通用性。 (2)詳細分析多尺度分割算法原理和分割尺度的確定方法。采用多尺度分割算法和K鄰近的分類方法將去除植被的影像進行分割和分類識別,得出建筑物輪廓；最后設計了三個個對比試驗：1)僅僅利用灰度特征提取建筑物輪廓的結果與本研究采用方法的結果相比較,分析提取精度。2)本研究提取方法的結果與傳統(tǒng)的基于像元的分類提取結果進行對比,結果表明：利用面向對象的思想對本研究中的高分辨率遙感影像進行分類的方法具有明顯的優(yōu)越性,提取精度明顯提高。3)在相同分割條件下,對比分析采用知識規(guī)則分類提取結果與采用本研究方法提取結果,分析提取精度。 (3)文章提出了一種基于數(shù)字化的方法對于建筑物提取的結果中漏分、錯分的現(xiàn)象進行后處理,即使用3種不同的數(shù)字化方法(手扶跟蹤數(shù)字化,自動跟蹤數(shù)字化,GIS數(shù)據(jù)疊加匹配)分別對輪廓破壞建筑物輪廓進行了修復處理。為了加快提取速度,本文提出將提取的建筑物輪廓與GIS數(shù)據(jù)庫中有的相同地區(qū)矢量數(shù)據(jù)匹配疊加,快速自動修復破壞了的建筑物輪廓。三種方法都是通過c#和arcengien平臺開發(fā)實現(xiàn)。
[Abstract]:In the geospatial database, the building is one of the core terrain elements, and it is also an indispensable part of the urban environment and an important settlement of human activities. With the development of society, the land for building is changing constantly, so it is very important to speed up the updating of spatial database. With the development of remote sensing technology, it is possible to identify and extract the feature information accurately and update GIS database automatically. Therefore, this paper focuses on how to extract building contours from high-resolution remote sensing images. The main contents of the research are as follows: (1) by using the principle of wave band numerical operation in image and the characteristics of high reflectivity of vegetation in green band, the difference operation method between green band and red band is adopted to reduce the brightness value of other ground objects. Increase the brightness value of vegetation, after morphological repair, the simple gray threshold segmentation algorithm is used to extract vegetation, and then mask processing to eliminate the vegetation interference to the subsequent building extraction; finally, Remote sensing image data of different regions with the same resolution are used to verify the generality of the vegetation extraction method. (2) the principle of multi-scale segmentation algorithm and the method of determining segmentation scale are analyzed in detail. Multi-scale segmentation algorithm and K-neighborhood classification method are used to segment and identify the vegetation removal image, and the building contour is obtained. Finally, we design three contrast experiments: 1) the results of extracting the building contours using only gray features are compared with the results of the method used in this study. Analysis and extraction accuracy .2) the results of this method are compared with those of traditional pixel based classification. The results show that the method of classifying the high resolution remote sensing images in this study has obvious advantages, and the precision of extraction is improved by 3. 3) under the same segmentation conditions, the method can be used to classify the high resolution remote sensing images. Comparing the results of classification with knowledge rules and the results of this study, the paper analyzes the accuracy of extraction. (3) this paper proposes a digitized method for the missing points in the results of building extraction. Three different digitization methods (walking tracking digitization, automatic tracking digital GIS data superposition matching) were used to repair the damaged building contour. In order to speed up the extraction speed, this paper proposes to match and superposition the extracted building contour with the same area vector data in GIS database, so that the damaged building contour can be repaired quickly and automatically. All three methods are developed on the platform of c# and arcengien.
【學位授予單位】：中南大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P208

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