發(fā)布時間：2018-05-24 23:07

  本文選題：SVM + 區(qū)域生長法�。� 參考：《江蘇大學》2017年碩士論文

【摘要】：中國城鎮(zhèn)整體進入快速城市化階段,動態(tài)監(jiān)控并準確預測城鎮(zhèn)擴張成為目前學術界研究的熱點和政府決策的重點問題。美國軍事氣象衛(wèi)星(Defence Meteorological Satellite Program,DMSP)搭載的Operational Linescan System(OLS)傳感器獲取的全球夜間燈光數(shù)據(jù)是進行相關大尺度城市化研究的一種有效數(shù)據(jù)源。利用DMSP/OLS夜間燈光數(shù)據(jù)提取城鎮(zhèn)用地信息可以為大尺度城鎮(zhèn)用地空間格局分布研究提供科學依據(jù)。而利用有效的圖像識別分類算法是提取城鎮(zhèn)用地的關鍵,結合改進的支持向量機分類算法提取夜間燈光數(shù)據(jù)中的城鎮(zhèn)用地,分析長時間序列城鎮(zhèn)用地時空格局演變特征,并模擬城鎮(zhèn)未來擴張規(guī)模,對制定宏觀政策提供理論依據(jù)和參考借鑒。本文運用不變目標區(qū)域法對長時間序列的DMSP/OLS夜間燈光數(shù)據(jù)進行校正,提出了改進的SVM分類算法,對江蘇省內具有代表性的城鎮(zhèn)進行了邊界提取,分析了城鎮(zhèn)的空間擴展格局、總體發(fā)展趨勢及其中心轉移情況,并利用GM(1,1)灰色模型對南京城鎮(zhèn)未來的發(fā)展狀況進行預測。論文的主要內容及其結論如下:(1)采用不變目標區(qū)域法對夜間燈光數(shù)據(jù)進行飽和校正,能夠有效緩解燈光數(shù)據(jù)的飽和效應;通過連續(xù)性校正以及像元異常波動校正,降低相同年份不同傳感器影像差異,減少相鄰年份數(shù)據(jù)異常波動,提高不同年份數(shù)據(jù)間的連續(xù)性和可比較性。(2)提出了基于改進的SVM分類算法,提取城鎮(zhèn)用地,并結合Landsat 8影像人工提取城鎮(zhèn)用地進行精度評價。對比結果表明,基于改進的SVM分類算法提取的城鎮(zhèn)用地信息在總體精度、Kappa系數(shù)和用戶精度等方面都具有明顯的優(yōu)越性。(3)采用扇形分析、同心圓分析和城鎮(zhèn)用地中心平均轉移等方法對南京長時間序列城鎮(zhèn)發(fā)展情況進行分析,發(fā)現(xiàn)南京城鎮(zhèn)發(fā)展情形經(jīng)歷了先緩慢后快速的擴張過程。南京主城區(qū)在1992年城鎮(zhèn)區(qū)域的基礎向四周擴散,沿長江及南北交通走廊發(fā)展,城鎮(zhèn)土地密度不斷增大。(4)最后選用GM(1,1)預測模型對南京城鎮(zhèn)未來的發(fā)展情況進行了預測,GM(1,1)預測模型的檢驗精度檢驗表明模型精度有很好可行性。通過計算發(fā)現(xiàn),2018年與2020年南京城鎮(zhèn)用地數(shù)量預測規(guī)模分別為2030.88km~2和2564.26 km~2。
[Abstract]:China's cities and towns as a whole enter the stage of rapid urbanization, dynamic monitoring and accurate prediction of urban expansion has become the focus of academic research and government decision-making. The global nighttime light data obtained by the Operational Linescan system OLS sensor on the US military weather satellite Defence Meteorological Satellite Program DMSPis is an effective data source for large-scale urbanization research. Using DMSP/OLS night lighting data to extract urban land information can provide scientific basis for spatial distribution of large scale urban land use. Using effective image recognition and classification algorithm is the key to extract urban land, combined with the improved support vector machine classification algorithm to extract urban land from night lighting data, and analyze the spatial and temporal pattern evolution characteristics of long-term urban land use in a long time series. It simulates the scale of urban expansion in the future and provides theoretical basis and reference for macro-policy formulation. In this paper, the invariant target region method is used to correct the DMSP/OLS night light data of long time series, and an improved SVM classification algorithm is proposed. The boundary of the representative towns in Jiangsu Province is extracted, and the spatial expansion pattern of the towns is analyzed. The general trend of development and the situation of center transfer are discussed, and the future development of Nanjing cities and towns is forecasted by using GM1 / 1) grey model. The main contents and conclusions of this paper are as follows: (1) the saturation effect of night light data can be effectively alleviated by using the invariant target region method, and the saturation effect of light data can be effectively alleviated by means of continuity correction and pixel abnormal fluctuation correction. To reduce the difference of different sensor images in the same year, to reduce the abnormal fluctuation of data in adjacent years, and to improve the continuity and comparability among the data of different years, a modified SVM classification algorithm is proposed to extract urban land. Combined with Landsat 8 image artificial extraction of urban land for accuracy evaluation. The comparison results show that the urban land information extracted based on the improved SVM classification algorithm has obvious advantages in terms of overall accuracy and user accuracy. Based on the analysis of concentric circle analysis and the average transfer of urban land center, it is found that the development of Nanjing cities and towns experienced a slow and rapid expansion process in the long time series of cities and towns in Nanjing. The foundation of the main urban area of Nanjing spread around in 1992 and developed along the Yangtze River and the north-south transportation corridor. Finally, the future development of cities and towns in Nanjing is forecasted by using the GM1) forecasting model. The accuracy test of the model shows that the model is feasible. It is found by calculation that the predicted scale of urban land use in Nanjing in 2018 and 2020 is 2030.88km~2 and 2564.26 km / m respectively.
【學位授予單位】：江蘇大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP751

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相關期刊論文 前10條

1 曹子陽;吳志峰;匡耀求;黃寧生;;DMSP/OLS夜間燈光影像中國區(qū)域的校正及應用[J];地球信息科學學報;2015年09期

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本文編號：1930969