【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(wú)法充分利用影像中的空間細(xì)節(jié)信息,還會(huì)出現(xiàn)較大的漏分誤差和錯(cuò)分誤差,同時(shí)呈現(xiàn)出較為嚴(yán)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(wèn)題,因此受到了地學(xué)領(lǐng)域的廣泛關(guān)注。論文運(yùn)用面向?qū)ο蟮挠跋穹治鏊悸?重點(diǎn)圍繞基于高分辨率遙感影像的變化檢測(cè)、DEM影像的地形對(duì)象分割以及特殊地貌對(duì)象的提取進(jìn)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(cè)方法,通過(guò)計(jì)算得到的KL散度值進(jìn)行自然裂點(diǎn)法進(jìn)行分級(jí),呈現(xiàn)了不同于傳統(tǒng)變化檢測(cè)方法的表現(xiàn)形式。本文先是計(jì)算面向?qū)ο笥跋穹指畹淖罴褏?shù),對(duì)前后不同時(shí)相的影像進(jìn)行影像分割,將分割后的影像對(duì)象實(shí)施拓?fù)浏B加處理。而后通過(guò)對(duì)每個(gè)影像對(duì)象進(jìn)行前后時(shí)相影像的灰度分區(qū)統(tǒng)計(jì),并由此進(jìn)行KL散度的計(jì)算。最后通過(guò)不同分級(jí)方法對(duì)得到的KL散度值進(jìn)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(jí)方法對(duì)變化程度的影響。2、實(shí)現(xiàn)了基于區(qū)域生長(zhǎng)法的地形區(qū)域自動(dòng)分割方法,該方法以實(shí)際溝谷網(wǎng)絡(luò)的徑流節(jié)點(diǎn)為種子點(diǎn)集合,在統(tǒng)計(jì)分析地形特征量化指標(biāo)的基礎(chǔ)上建立對(duì)應(yīng)的生長(zhǎng)閾值進(jìn)行區(qū)域分割,并進(jìn)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(shí)現(xiàn)地形區(qū)域的自動(dòng)分割。應(yīng)用該方法,以中國(guó)典型黃土高原黃土塬地貌區(qū)為例,運(yùn)用并分析該區(qū)域地表剖面曲率結(jié)構(gòu)特征,實(shí)現(xiàn)了溝間、溝坡、溝底等三種地形類(lèi)型的區(qū)域自動(dòng)分割,生成三種特定的地形對(duì)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(jì)多尺度分割參數(shù),進(jìn)行溝坡層次的多尺度分割,生成針對(duì)溝頭區(qū)域的影像對(duì)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(duì)象數(shù)據(jù)拓?fù)潢P(guān)系的組合,建立溝頭對(duì)象的識(shí)別模型,實(shí)現(xiàn)大面積流域范圍內(nèi)的溝頭識(shí)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(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP751

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 田丹;劉愛(ài)利;丁滸;張?chǎng)?齊威;;地貌形態(tài)類(lèi)型面向?qū)ο蠓诸?lèi)法的改進(jìn)[J];地理與地理信息科學(xué);2016年02期

2 劉瑋;李發(fā)源;熊禮陽(yáng);劉雙琳;王軻;;基于區(qū)域生長(zhǎng)的黃土地貌溝沿線提取方法與實(shí)驗(yàn)[J];地球信息科學(xué)學(xué)報(bào);2016年02期

3 佃袁勇;方圣輝;姚崇懷;;一種面向地理對(duì)象的遙感影像變化檢測(cè)方法[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2014年08期

4 ZHU Hongchun;TANG Guoan;QIAN Kejian;LIU Haiying;;Extraction and Analysis of Gully Head of Loess Plateau in China Based on Digital Elevation Model[J];Chinese Geographical Science;2014年03期

5 李亮;龔煈;李雪;王凱;;像斑直方圖相似性測(cè)度的高分辨率遙感影像變化檢測(cè)[J];遙感學(xué)報(bào);2014年01期

6 祝錦霞;王珂;;面向?qū)ο蟮母叻直媛视跋褡兓瘷z測(cè)方法研究[J];農(nóng)業(yè)機(jī)械學(xué)報(bào);2013年04期

7 宋效東;湯國(guó)安;周毅;田劍;;基于并行GVF Snake模型的黃土地貌溝沿線提取[J];中國(guó)礦業(yè)大學(xué)學(xué)報(bào);2013年01期

8 周啟鳴;;多時(shí)相遙感影像變化檢測(cè)綜述[J];地理信息世界;2011年02期

9 晏實(shí)江;湯國(guó)安;李發(fā)源;董有福;;利用DEM邊緣檢測(cè)進(jìn)行黃土地貌溝沿線自動(dòng)提取[J];武漢大學(xué)學(xué)報(bào)(信息科學(xué)版);2011年03期

10 陳杰;鄧敏;肖鵬峰;楊敏華;梅小明;;粗糙集高分辨率遙感影像面向?qū)ο蠓诸?lèi)[J];遙感學(xué)報(bào);2010年06期

相關(guān)博士學(xué)位論文 前3條

1 湯玉奇;面向?qū)ο蟮母叻直媛视跋癯鞘卸嗵卣髯兓瘷z測(cè)研究[D];武漢大學(xué);2013年

2 周毅;基于DEM的黃土高原正負(fù)地形及空間分異研究[D];南京師范大學(xué);2011年

3 黃慧萍;面向?qū)ο笥跋穹治鲋械某叨葐?wèn)題研究[D];中國(guó)科學(xué)院研究生院（遙感應(yīng)用研究所）;2003年

相關(guān)碩士學(xué)位論文 前5條

1 羅裳;基于KL熵矩陣近似問(wèn)題的研究[D];華南理工大學(xué);2014年

2 勞小敏;基于對(duì)象的高分辨率遙感影像土地利用變化檢測(cè)技術(shù)研究[D];浙江大學(xué);2013年

3 周增坡;基于多源數(shù)據(jù)的典型地貌形態(tài)特征提取方法研究[D];東北師范大學(xué);2009年

4 陳紹宇;高塬溝壑區(qū)溯源侵蝕發(fā)生發(fā)育規(guī)律研究[D];中國(guó)科學(xué)院研究生院（教育部水土保持與生態(tài)環(huán)境研究中心）;2009年

5 宋佳;基于DEM的我國(guó)地貌形態(tài)類(lèi)型自動(dòng)劃分研究[D];西北大學(xué);2006年

，

本文編號(hào)：2125649