【摘要】：摘要：居民地是人類居住和進(jìn)行各種日�；顒�(dòng)的中心場(chǎng)所。在高分辨遙感影像中,居民地通常由密集的建筑物群,內(nèi)部的綠地,以及周邊的道路交通網(wǎng)所構(gòu)成。準(zhǔn)確有效地獲取居民地的實(shí)時(shí)信息,在“城鎮(zhèn)化”建設(shè)、數(shù)字城市、城市規(guī)劃、土地利用及GIS系統(tǒng)更新等多個(gè)領(lǐng)域都具有重要的現(xiàn)實(shí)及經(jīng)濟(jì)意義。為此,本文充分利用高分辨率遙感影像上居民地特有的局部特征,進(jìn)行了居民地提取和分類方面的探索和嘗試�？傮w而言,本文主要包括以下三個(gè)方面的研究工作： (1)基于邊緣密度特征的高分辨率遙感影像居民地提取 利用影像上居民地與非居民地的邊緣密度特征差異提取居民地,首先對(duì)影像進(jìn)行濾波平滑預(yù)處理,其次檢測(cè)影像上的邊緣特征并將其擬合成直線段,然后計(jì)算影像上像素點(diǎn)到所有邊緣直線段的空間距離,最后利用高斯函數(shù)量化邊緣密度并最終閾值分割提取居民地。該方法是一種全自動(dòng)的居民地提取方法,有效地避免了由于人為因素對(duì)提取結(jié)果帶來的影響,提高了居民地提取的精度。 (2)基于Gabor特征的高分辨率遙感影像居民地提取 居民地內(nèi)部除了包含豐富的邊緣特征之外,同時(shí)也具有密集的角點(diǎn)特征,而在角點(diǎn)處往往會(huì)出現(xiàn)明顯的灰度梯度和曲率變化,Gabor濾波對(duì)于這種變化具有較強(qiáng)的響應(yīng)。據(jù)此,本文首先利用Gabor濾波對(duì)影像做多尺度多角度的變換,其次檢測(cè)濾波圖像上的Gabor特征并優(yōu)化,最后構(gòu)建特征的空間投票矩陣并結(jié)合OStu閾值分割方法提取居民地。該方法同樣是一種非監(jiān)督的提取方法,相對(duì)于基于邊緣密度特征的提取方法,在運(yùn)行效率和居民地提取精度上都有一定的提高。 (3)高分辨率遙感影像城鎮(zhèn)及鄉(xiāng)村居民地監(jiān)督分類 利用以上兩種方法可以有效地提取高分辨率遙感影像上的居民地,但是未能對(duì)城鎮(zhèn)和鄉(xiāng)村居民地進(jìn)行更進(jìn)一步的分類,因此不能準(zhǔn)確地體現(xiàn)出城鄉(xiāng)之間的發(fā)展變化信息。為此,本文在以上兩種居民地提取方法的理論基礎(chǔ)上,充分利用城鎮(zhèn)和鄉(xiāng)村居民地的邊緣特征及Gabor特征的分布差異,發(fā)展了一種城鎮(zhèn)及鄉(xiāng)村居民地監(jiān)督分類方法。首先設(shè)計(jì)了五種可以體現(xiàn)城鄉(xiāng)居民地邊緣特征和Gabor特征分布差異的分類規(guī)則,然后構(gòu)建訓(xùn)練樣本集對(duì)各類規(guī)則進(jìn)行學(xué)習(xí),最后通過大量的測(cè)試樣本驗(yàn)證以上規(guī)則的分類精度。由于該方法只是作為城鎮(zhèn)居民地與鄉(xiāng)村居民地初級(jí)分類的一個(gè)探索,中間過程還不夠完善,因此所取得的分類精度有限,但是該方法具有獨(dú)創(chuàng)性意義。
[Abstract]:Absrtact: residential land is the central place for human beings to live and carry out various daily activities. In high resolution remote sensing images, residential land is usually composed of dense buildings, inner green space, and surrounding road traffic network. It is of great practical and economic significance to obtain the real time information of residents' land accurately and effectively in the fields of "urbanization" construction, digital city, urban planning, land use and GIS system update. Therefore, this paper makes full use of the local characteristics of residents in high resolution remote sensing images, and explores and tries to extract and classify residents. Overall, This paper mainly includes the following three aspects of research work: (1) based on the edge density characteristics of high-resolution remote sensing images of residents to extract and use the image of the edge density of residents and non-residents of the special density Differential extraction of residential land, First, the image is processed by filtering smoothing, then the edge features of the image are detected and synthesized into straight line segments, and then the spatial distance between pixels on the image and all edge line segments is calculated. Finally, the edge density is quantized by Gao Si function and the final threshold segmentation is used to extract the resident land. This method is a fully automatic extraction method for residents, which effectively avoids the influence of human factors on the extraction results. (2) the high resolution remote sensing image based on Gabor features not only contains rich edge features, but also has dense corner features. But there are obvious grayscale gradient and curvature change at the corner point. Gabor filter has strong response to this change. Based on this, this paper firstly uses Gabor filter to transform the image from multi-scale and multi-angle, then detects and optimizes the Gabor features on the filtered image. Finally, the spatial voting matrix of the feature is constructed and the residential area is extracted by using the OStu threshold segmentation method. This method is also an unsupervised extraction method, compared with the edge density feature extraction method. (3) the high resolution remote sensing image can be effectively extracted from the urban and rural areas by using the two methods of urban and rural land supervision and classification. (3) the two methods mentioned above can be effectively used to extract high resolution. (3) the two methods mentioned above can be effectively used to extract high resolution from urban and rural areas. The inhabitants of the remote sensing image, However, there is no further classification of urban and rural land, so it can not accurately reflect the information of development and change between urban and rural areas. Therefore, on the basis of the above two land extraction methods, this paper develops a land supervision classification method for urban and rural residents by making full use of the distribution differences between the edge characteristics and Gabor characteristics of urban and rural residential land. Firstly, five kinds of classification rules are designed, which can reflect the difference between urban and rural residents' marginal features and Gabor characteristics. Then, the training sample set is constructed to study the rules. Finally, a large number of test samples are used to verify the classification accuracy of the above rules. Because this method is only an exploration of the primary classification of urban and rural areas, and the intermediate process is not perfect, the accuracy of classification is limited, but the method is of original significance.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：P237

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