本文選題：紋理特征提取 + 指紋識別技術��； 參考：《云南師范大學》2017年碩士論文

【摘要】：植被是覆蓋地表的植物群落總稱,是生態(tài)系統重要的組成部分。植被分類是遙感應用研究的一個熱點問題,高分辨率遙感影像具有豐富的紋理信息,可以有效改善植被分類精度。紋理特征提取是高分遙感圖像分類應用中的關鍵技術之一,現有的紋理特征提取方法普遍存在準確分類率低、計算復雜以及效率低等缺點。本研究以云南省西雙版納州納板河流域為例,分析高分辨率遙感影像植被紋理特征,提出一種基于指紋識別技術的植被紋理特征提取方法,并輔以紋理特征進行面向對象植被分類,分析紋理特征對面向對象植被分類精度的影響。研究成果總結如下:(1)提出并實現一種基于指紋識別技術的植被紋理特征提取方法基于指紋識別技術提出指紋紋理增強算法,將指紋紋理增強算法分別與灰度共生矩陣和局部二值模型算法相結合實現了基于指紋識別技術的紋理特征提取方法。以云南省西雙版納州納板河流域的WorldView-2及Pléiades影像為實驗數據,采用本文算法提取影像的紋理特征,并與基于RGB影像提取的紋理特征作對比分析。在WorldView-2數據實驗中,相比加入基于RGB影像提取的紋理特征的分類結果,加入基于指紋識別技術提取的GLCM紋理特征的分類總體精度達到了91.56%,提高了4.10%,Kappa系數達到了0.90,提高了0.05,加入基于指紋識別技術提取的LBP紋理特征的分類總體精度達到了89.36%,提高了3.41%,Kappa系數達到了0.87,提高了0.04。在Pléiades數據實驗中,采用所提出的紋理特征提取方法提取影像的紋理特征,并輔以紋理特征進行面向對象植被分類。相比加入基于RGB影像提取的紋理特征的分類結果,加入基于指紋識別技術提取的GLCM紋理特征的分類總體精度達到了88.60%,提高了2.91%,Kappa系數達到了0.86,提高了0.04。加入基于指紋識別技術提取的LBP紋理特征的分類總體精度達到了84.60%,提高了3.04%,Kappa系數達到了0.81,提高了0.04。對各個紋理特征提取算法采用不同的高分數據的分類結果表明:基于指紋識別技術紋理特征提取方法提取的紋理特征可在很大程度上改善紋理規(guī)則地類的分類精度。(2)紋理特征可以明顯改善高分辨遙感影像面向對象植被分類精度將提取的紋理特征加入到面向對象植被分類中,與未利用紋理特征的面向對象植被分類結果作對比分析。WorldView-2影像試驗中,采用基于RGB影像提取的GLCM紋理特征的總體分類精度為87.46%,提高了7.05%,Kappa系數為0.85,提高了0.09;加入基于RGB影像提取的LBP紋理特征的總體分類精度為85.95%,提高了5.44%,Kappa系數為0.83,提高了0.07;加入基于指紋識別技術紋理特征提取方法提取的GLCM和LBP紋理特征的總體分類精度分別提高了11.15%和8.95%,Kappa系數分別提高了0.14和0.11。在Pléiades影像試驗中,加入紋理特征后的面向對象植被分類精度也顯著提高。結果表明:運用紋理特征的面向對象植被分類可以顯著提高高分辨率遙感影像的植被分類精度。(3)實現基于單一數據源提取多分類特征的面向對象植被精細分類本文基于單一數據源提取影像對象的光譜特征、紋理特征、植被指數特征以及幾何特征等植被識別特征,對研究區(qū)自然林、橡膠林、香蕉、茶園以及耕地的面向對象分類結果中,自然林分類精度為95.43%,橡膠林分類精度達到94.33%,香蕉分類精度高達93.60%,茶園及耕地的分類精度均達到了83.00%以上�？傮w來說分類精度較高,實現了面向對象方法框架下基于單一數據源提取多分類特征的植被精細分類。
[Abstract]:Vegetation is the general name of plant community covering the surface, and it is an important part of the ecosystem. Vegetation classification is a hot issue in remote sensing application research. High resolution remote sensing images have rich texture information and can effectively improve the classification accuracy of vegetation. The extraction of texture features is one of the key technologies in the application of high-resolution remote sensing image classification. The existing texture feature extraction methods have the disadvantages of low accurate classification rate, complex calculation and low efficiency. This study took the Nanban River Basin in Xishuangbanna, Yunnan Province as an example, to analyze the vegetation texture features of high resolution remote sensing images, and put forward a method of vegetation texture feature extraction based on fingerprint recognition technology, supplemented with texture special. The results are summarized as follows: (1) a fingerprint recognition method based on fingerprint recognition technology is proposed and implemented based on fingerprint recognition technology to enhance the fingerprint texture enhancement algorithm and the fingerprint texture enhancement algorithm and gray scale respectively. Combining the symbiotic matrix with the local two value model algorithm, the texture feature extraction method based on fingerprint recognition technology is realized. The WorldView-2 and Pl e iades images of the Nanban River Basin in Xishuangbanna, Yunnan province are taken as experimental data, and the texture features of the images are extracted by this algorithm, and the scores are compared with the texture features based on the RGB images. In the WorldView-2 data experiment, compared with the classification results of texture features extracted based on RGB images, the overall accuracy of the classification of GLCM texture features extracted with the fingerprint recognition technology is 91.56%, 4.10%, the Kappa coefficient is 0.90, and 0.05 is increased, and the LBP texture feature extracted based on fingerprint recognition technology is added. The overall accuracy of the classification has reached 89.36%, increased by 3.41%, and the Kappa coefficient reached 0.87. In the Pl e iades data experiment, 0.04. was used to extract the texture features of the image with the proposed texture feature extraction method, and the texture features were used to classify the object oriented vegetation. The classification of the texture features based on the RGB image extraction was added to the classification. As a result, the overall accuracy of the classification of GLCM texture features extracted with fingerprint recognition technology reached 88.60%, increased by 2.91%, and the coefficient of Kappa reached 0.86. The overall accuracy of the classification of LBP texture features extracted by 0.04. based on fingerprint recognition technology was 84.60%, 3.04%, Kappa coefficient reached 0.81, and 0.04. increased. The classification results of various texture feature extraction algorithms using different high score data show that texture feature extraction based on fingerprint recognition technology can improve the classification accuracy of texture classification. (2) texture features can obviously improve the accuracy of object-oriented vegetation classification in high resolution remote sensing images. The extracted texture features are added to the object-oriented vegetation classification. Compared with the object-oriented vegetation classification results that are not used for texture features, the overall classification accuracy of the GLCM texture features based on RGB images is 87.46%, increased by 7.05%, the Kappa coefficient is 0.85, and 0.09, and the addition of RGB is based on RGB. The overall classification accuracy of the LBP texture features extracted by the image is 85.95%, 5.44%, the Kappa coefficient 0.83, and 0.07. The overall classification accuracy of the GLCM and LBP texture features extracted from the texture feature extraction method based on fingerprint recognition technology is increased by 11.15% and 8.95% respectively, and the Kappa coefficient is increased by 0.14 and 0.11. respectively in Pl e iades shadow. In the experiment, the accuracy of the object-oriented vegetation classification after adding texture features has also been improved significantly. The results show that the classification accuracy of the vegetation can be significantly improved by the object-oriented vegetation classification using the texture features. (3) to realize the fine classification of the object oriented vegetation based on the multiple classification characteristics based on the single data source. The classification accuracy of natural forest, rubber forest, banana, tea garden and cultivated land is 95.43%, the classification precision of the rubber forest is 94.33%, and the precision of banana classification is high. Up to 93.60%, the classification accuracy of tea garden and cultivated land has reached over 83%. In general, the precision of classification is high, and the fine classification of Vegetation Based on the multi classification characteristics based on the single data source is realized under the framework of object-oriented method.
【學位授予單位】：云南師范大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：Q949;P237

【參考文獻】

相關期刊論文 前10條

1 楊盼盼;孫興齊;候智庭;;鄭州市植被覆蓋度動態(tài)監(jiān)測與分析[J];平頂山學院學報;2015年05期

2 陳曉丹;李思明;;圖像分割研究進展[J];現代計算機(專業(yè)版);2013年33期

3 胡榮明;魏曼;楊成斌;賀俊斌;;以SPOT5遙感數據為例比較基于像素與面向對象的分類方法[J];遙感技術與應用;2012年03期

4 何彩周;袁國林;姜婷;;納板河保護區(qū)生態(tài)旅游資源分析與評價[J];環(huán)境科學導刊;2011年06期

5 胡杏花;朱谷昌;徐文海;;基于分形理論的遙感影像分類研究[J];遙感信息;2011年05期

6 趙麗花;李衛(wèi)國;杜培軍;;基于多時相HJ衛(wèi)星的冬小麥面積提取[J];遙感信息;2011年02期

7 宋本欽;李培軍;;加入改進LBP紋理的高分辨率遙感圖像分類[J];國土資源遙感;2010年04期

8 鮑海英;李艷;尹永宜;;城市航空影像的陰影檢測和陰影消除方法研究[J];遙感信息;2010年01期

9 蘭明娟;魏虹;熊春妮;周晨霓;;基于TM影像的重慶市北碚區(qū)地表植被覆蓋變化[J];西南大學學報(自然科學版);2009年04期

10 劉麗;匡綱要;;圖像紋理特征提取方法綜述[J];中國圖象圖形學報;2009年04期

相關博士學位論文 前2條

1 陳杰;高分辨率遙感影像面向對象分類方法研究[D];中南大學;2010年

2 路威;面向目標探測的高光譜影像特征提取與分類技術研究[D];中國人民解放軍信息工程大學;2005年

相關碩士學位論文 前6條

1 付莉娜;指紋識別算法的研究與優(yōu)化[D];西安科技大學;2012年

2 肖霄;圖像LBP特征提取的研究與應用[D];吉林大學;2011年

3 章智儒;紋理特征提取算法及其在面向對象分類技術中的應用研究[D];電子科技大學;2009年

4 田瓊花;遙感影像紋理特征提取及其在影像分類中的應用[D];華中科技大學;2007年

5 倪永婧;基于紋理細節(jié)的圖像去噪算法的研究[D];燕山大學;2006年

6 王俊一;基于紋理特征的自動指紋識別系統研究[D];華中科技大學;2005年

，

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