【摘要】：現(xiàn)如今隨著遙感影像空間分辨率的不斷提高,傳統(tǒng)意義上基于像元的影像分析方法不僅無法充分利用影像中的空間細節(jié)信息,還會出現(xiàn)較大的漏分誤差和錯分誤差,同時呈現(xiàn)出較為嚴重的“椒鹽噪聲”,大大影響了各種應(yīng)用結(jié)果的精度。而面向?qū)ο蟮挠跋穹治龇椒?能夠有效抑制上述問題,因此受到了地學領(lǐng)域的廣泛關(guān)注。論文運用面向?qū)ο蟮挠跋穹治鏊悸?重點圍繞基于高分辨率遙感影像的變化檢測、DEM影像的地形對象分割以及特殊地貌對象的提取進行了應(yīng)用研究。論文完成的主要工作有:1、論文提出了一種基于KL散度的面向?qū)ο筮b感影像變化檢測方法,通過計算得到的KL散度值進行自然裂點法進行分級,呈現(xiàn)了不同于傳統(tǒng)變化檢測方法的表現(xiàn)形式。本文先是計算面向?qū)ο笥跋穹指畹淖罴褏?shù),對前后不同時相的影像進行影像分割,將分割后的影像對象實施拓撲疊加處理。而后通過對每個影像對象進行前后時相影像的灰度分區(qū)統(tǒng)計,并由此進行KL散度的計算。最后通過不同分級方法對得到的KL散度值進行變化程度的劃分,并分析了不同分割參數(shù)以及不同分級方法對變化程度的影響。2、實現(xiàn)了基于區(qū)域生長法的地形區(qū)域自動分割方法,該方法以實際溝谷網(wǎng)絡(luò)的徑流節(jié)點為種子點集合,在統(tǒng)計分析地形特征量化指標的基礎(chǔ)上建立對應(yīng)的生長閾值進行區(qū)域分割,并進行分割區(qū)域的圖像處理和邊緣界線提取;利用獲取的邊緣界線,實現(xiàn)地形區(qū)域的自動分割。應(yīng)用該方法,以中國典型黃土高原黃土塬地貌區(qū)為例,運用并分析該區(qū)域地表剖面曲率結(jié)構(gòu)特征,實現(xiàn)了溝間、溝坡、溝底等三種地形類型的區(qū)域自動分割,生成三種特定的地形對象。3、完成了在面向?qū)ο蠓椒ǖ牡匦畏指罨A(chǔ)上,建立溝間、溝坡、溝底地形的影像分割層次,根據(jù)溝頭地貌特征設(shè)計多尺度分割參數(shù),進行溝坡層次的多尺度分割,生成針對溝頭區(qū)域的影像對象。并結(jié)合DEM影像數(shù)據(jù)所包含的水文特征、地形信息以及空間對象數(shù)據(jù)拓撲關(guān)系的組合,建立溝頭對象的識別模型,實現(xiàn)大面積流域范圍內(nèi)的溝頭識別。
[Abstract]:Nowadays, with the improvement of spatial resolution of remote sensing image, the traditional image analysis method based on pixel can not only not make full use of the spatial detail information in the image, but also have a large error of missing and error. At the same time, more serious "salt and pepper noise" appeared, which greatly affected the accuracy of various application results. The object-oriented image analysis method can effectively suppress the above problems, so it has received wide attention in geoscience field. In this paper, the object oriented image analysis method is used to study the terrain object segmentation based on high resolution remote sensing image and the extraction of special geomorphologic object. The main work accomplished in this paper is: 1. This paper proposes an object oriented remote sensing image change detection method based on KL divergence, which is classified by natural split point method by calculating the KL divergence value. It is different from the traditional change detection method. In this paper, the optimal parameters of object oriented image segmentation are first calculated, and the images with different phases are segmented, and the image objects are processed by topological superposition. Then, the grayscale partition statistics of each image object are carried out, and the KL divergence is calculated. Finally, the variation degree of the KL divergence is divided by different classification methods, and the influence of different segmentation parameters and different classification methods on the variation degree is analyzed. The automatic terrain region segmentation method based on regional growth method is realized. In this method, the runoff nodes of the actual valley network are taken as seed points, the corresponding growth threshold is established on the basis of statistical analysis of the quantitative index of terrain characteristics, and the image processing and edge boundary extraction of the segmented region are carried out. The obtained boundary line is used to realize the automatic segmentation of terrain area. By using this method, taking the typical loess plateau landform area of China as an example, using and analyzing the curvature structure characteristics of the surface section of this area, the automatic segmentation of the three topographic types of gully, gully slope and furrow bottom is realized. Three special topographic objects. 3 are generated. Based on the object oriented method, the image segmentation levels of the terrain between gully, slope and bottom are established, and the multiscale segmentation parameters are designed according to the geomorphological features of the gully head. The multi-scale segmentation of gully slope level is carried out to generate the image object for the gully head region. Based on the combination of hydrological characteristics, topographic information and spatial data topological relation, the recognition model of gully head object is established to realize the recognition of gully head in a large area of river basin.
【學位授予單位】：山東科技大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

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