發(fā)布時間：2018-07-05 11:22

  本文選題：ALOS多光譜影像 + 決策樹　； 參考：《南京農(nóng)業(yè)大學》2013年碩士論文

【摘要】：赤水河流域人地矛盾尖銳,土地利用方式不合理嚴重影響了流域土地資源可持續(xù)利用和生態(tài)環(huán)境保護,及時了解赤水河流域各區(qū)域的土地利用現(xiàn)狀,對流域土地利用合理性分析、水土流失治理、生態(tài)環(huán)境保護等工作的開展具有重要的意義。探索一種能在研究范圍內(nèi)達到精度與效率統(tǒng)一的土地利用信息提取方法能為流域綜合管理的相關(guān)工作提供重要支持。 本研究以赤水河流域為研究區(qū),以覆蓋該區(qū)域的10景ALOS多光譜遙感影像為數(shù)據(jù)源,根據(jù)每景影像的覆蓋范圍將全流域劃分為十個子研究區(qū),選擇其中一子研究區(qū)為試驗區(qū),在充分分析和統(tǒng)計八種典型地物在影像光譜特征、歸一化差異植被指數(shù)(NDVI)、歸一化差異水體指數(shù)NDWI、數(shù)字高程模型(DEM)及影像波段運算后特征值的數(shù)值差異的基礎(chǔ)上,確定了區(qū)分地物的閾值,探索建立了以閾值為規(guī)則的二叉決策樹模型,進行土地利用分類。將該思路和方法拓展到其余九個子研究區(qū),并分析了影響不同影像分類精度的因子。 主要研究結(jié)果如下： (1)構(gòu)建了具有地域代表性的1號子研究區(qū)(試驗區(qū))的基于規(guī)則的決策樹模型,該方法對八種地物分類結(jié)果的總體精度為89.05%,Kappa系數(shù)為0.8741,總體精度相比最大似然法、支持向量機法分別提高12.39%、10.78%；Kappa系數(shù)相比最大似然法和支持向量機法則分別提高0.1412、0.1238�？傮w來說,該模型在不同程度上減少了對林地、草灌、河流、水庫坑塘、梯坪地、水田、建設(shè)用地這七種地物分類結(jié)果的錯分、漏分誤差,其中減少幅度以河流、水庫坑塘最為明顯,最大減少幅度分別為50.25%、46.71%。說明該方法在1號子研究區(qū)內(nèi)具有較好的適用性和可操作性。 (2)將試驗成功的方法與思路拓展至全流域范圍,10個子研究區(qū)中,基于規(guī)則的決策樹法在8個區(qū)域的分類結(jié)果總體精度和Kappa系數(shù)明顯優(yōu)于最大似然法,其分類結(jié)果最高總體精度達到90.59%,Kappa系數(shù)為0.8811,二者分別比最大似然法分別高出7.4%和0.0887,說明基于規(guī)則的決策樹法在赤水河流域具有一定的普適性,可以有效提高地物的分類精度。 (3)根據(jù)本研究采集的屬于赤水河流域范圍內(nèi)的156個GPS野外實測點,經(jīng)過差分校正后建立數(shù)據(jù)庫,對全流域提取出的土地利用圖進行精度驗證。結(jié)果表明,156個點位中有133個點位的地物分類正確,野外實測點驗證精度為85.26%。 (4)數(shù)據(jù)源的時相對決策樹模型的構(gòu)建有影響。另外,從統(tǒng)計學方面講,利用多元線性回歸模型探討得出：本研究共有10幅影像10個子研究區(qū)域,其分類結(jié)果的Kappa系數(shù)和區(qū)域總面積成反比,和水田面積成正比,也就是說,在10個不同區(qū)域內(nèi),某幅影像研究區(qū)域的區(qū)域總面積越大,分類精度越低；某幅影像研究區(qū)域的水田面積越大,分類精度越高。
[Abstract]:The contradiction between man and land in Chishui River Basin is sharp, and the unreasonable land use mode has seriously affected the sustainable utilization of land resources and the protection of ecological environment in the basin, so as to understand the current situation of land use in various areas of the Chishui River Basin in a timely manner. It is of great significance to analyze the rationality of land use, to control soil and water loss, and to protect the ecological environment. To explore a land use information extraction method which can achieve the unity of precision and efficiency within the scope of research can provide important support for the relevant work of integrated watershed management. Taking the Chishui River Basin as the study area and the 10 Alos multispectral remote sensing images covering the region as the data source, the whole basin is divided into ten sub-study areas according to the coverage of each scene image, and one of the sub-study areas is selected as the experimental area. Based on the analysis and statistics of the spectral characteristics, normalized differential vegetation index (NDVI), normalized differential water body index (NDWI), digital elevation model (Dem) and the numerical difference of the eigenvalues of the eight typical ground objects in the image band, The threshold value of distinguishing ground objects was determined and the binary decision tree model was established to classify land use. The method is extended to the other nine sub-study areas, and the factors that affect the classification accuracy of different images are analyzed. The main results are as follows: (1) the rule-based decision tree model of sub-research area No. 1 (experimental area) with geographical representation is constructed. The overall accuracy of this method for the classification of eight ground objects is 89.05 and the Kappa coefficient is 0.8741. Compared with the maximum likelihood method, the support vector machine method increases the maximum likelihood coefficient by 12.39 and the Kappa coefficient from the maximum likelihood method and the support vector machine method by 0.1412 and 0.1238, respectively. In general, the model reduces the misclassification and leakage errors of forest land, grass irrigation, rivers, reservoirs, terraces, paddy fields and construction land to varying degrees. The reservoir pit is the most obvious, the maximum reduction range is 50.25 and 46.71 respectively. The results show that the method has good applicability and maneuverability in the No. 1 sub-research area. (2) the successful test methods and ideas are extended to the whole watershed area and 10 sub-research areas. The total accuracy and Kappa coefficient of the rule-based decision tree method in eight regions are better than that of the maximum likelihood method. The highest overall accuracy of the classification is 90.59 and the Kappa coefficient is 0.8811, which is 7.4% and 0.0887 higher than that of the maximum likelihood method, respectively, which indicates that the rule-based decision tree method is universal in the Chishui River Basin. The classification accuracy of ground objects can be improved effectively. (3) according to 156 GPS field survey points which belong to the Chishui River Basin, the database is established after differential correction. The accuracy of the land use map extracted from the whole basin is verified. The results show that 133 of 156 points are correctly classified, and the accuracy of field measurement is 85.26. (4) the time of data source is relative to the construction of decision tree model. In addition, from the statistical point of view, using the multivariate linear regression model, it is concluded that the Kappa coefficient of 10 images in this study is inversely proportional to the total area of the region and is directly proportional to the area of the paddy field, that is, the Kappa coefficient of the classification results is inversely proportional to the total area of the paddy field. In 10 different regions, the larger the total area of an image is, the lower the classification accuracy is, and the larger the paddy field area is, the higher the classification accuracy is.
【學位授予單位】：南京農(nóng)業(yè)大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：P237

【參考文獻】

相關(guān)期刊論文 前10條

1 游浩辰;許章華;劉健;余坤勇;張新珠;;GIS支持下的山區(qū)遙感影像決策樹分類研究[J];北京聯(lián)合大學學報(自然科學版);2011年01期

2 程彬;姜琦剛;周云軒;湛邵斌;;基于ASTER數(shù)據(jù)遙感影像的決策樹分類[J];吉林大學學報(地球科學版);2007年01期

3 曹敏;史照良;沈泉飛;;ALOS影像在土地覆被分類中最佳波段選取的研究[J];測繪通報;2008年09期

4 王樹根;日本ALOS衛(wèi)星簡介[J];測繪信息與工程;2000年01期

5 黃真理;論赤水河流域資源環(huán)境的開發(fā)與保護[J];長江流域資源與環(huán)境;2003年04期

6 王忠鎖;姜魯光;黃明杰;張琛;于秀波;;赤水河流域生物多樣性保護現(xiàn)狀和對策[J];長江流域資源與環(huán)境;2007年02期

7 任曉冬;黃明杰;;赤水河流域產(chǎn)業(yè)狀況與綜合流域管理策略[J];長江流域資源與環(huán)境;2009年02期

8 吳海平;劉順喜;張榮慧;;ALOS在土地資源調(diào)查與監(jiān)測中的應(yīng)用研究[J];測繪與空間地理信息;2009年05期

9 何宇華;謝俊奇;劉順喜;;ALOS衛(wèi)星遙感數(shù)據(jù)影像特征分析及應(yīng)用精度評價[J];地理與地理信息科學;2008年02期

10 胥海威;何寬;;改進隨機決策樹群算法在監(jiān)督分類中的應(yīng)用[J];地理與地理信息科學;2010年06期

相關(guān)博士學位論文 前1條

1 任曉冬;赤水河流域綜合保護與發(fā)展策略研究[D];蘭州大學;2010年

，

本文編號：2100043