本文選題：遙感 + 土地利用變化　； 參考：《西北農(nóng)林科技大學(xué)》2017年碩士論文

【摘要】：河套灌區(qū)作為我國(guó)三個(gè)特大型灌區(qū)之一,地處黃河中游,同時(shí)也是亞洲最大的自流引水灌區(qū),其作用和地位舉足輕重。為了準(zhǔn)確掌握河套灌區(qū)在1990年至2010年間的土地利用變化情況,本文以TM遙感影像為數(shù)據(jù)源對(duì)1990年、2000年和2010年河套灌區(qū)的土地利用情況進(jìn)行分類和統(tǒng)計(jì)分析,從而為河套灌區(qū)的土地資源管理和灌區(qū)用水合理分配提供科學(xué)依據(jù)。主要研究?jī)?nèi)容如下:(1)研究河套灌區(qū)TM遙感圖像的拼接和裁剪方法,并對(duì)拼接后的全景圖像進(jìn)行目視解譯并建立灌區(qū)土地分類系統(tǒng)。三期遙感影像均分別包含三景圖像,每一景圖像分別包括7個(gè)波段,根據(jù)TM遙感圖像所含信息量較大的特點(diǎn)和河套灌區(qū)面積較大的實(shí)際情況,故本文選用具有旋轉(zhuǎn)、尺度縮放不變性的SURF算法對(duì)圖像進(jìn)行特征點(diǎn)提取,采用基于Shearlet變換算法對(duì)配準(zhǔn)之后的圖像進(jìn)行融合,拼接后獲得完整的河套灌區(qū)全貌,借助河套灌區(qū)的矢量文件采用多邊形不規(guī)則裁剪算法進(jìn)行圖像裁剪提取出研究區(qū)域。最佳指數(shù)法簡(jiǎn)單高效、方便計(jì)算,故本文采用該方法分別獲得每期影像的最佳波段組合。(2)分別采用三種監(jiān)督分類方法包括最大似然法、神經(jīng)網(wǎng)絡(luò)法和支持向量機(jī)法進(jìn)行土地分類,后將分層分類和支持向量機(jī)法相結(jié)合改進(jìn)分類方法。支持向量機(jī)分類是用以研究小樣本情況下機(jī)器分類的方法,在實(shí)際的遙感影像分類應(yīng)用中表現(xiàn)出較好的適用性。同時(shí)傳統(tǒng)的單分類器對(duì)不同的地物類別識(shí)別準(zhǔn)確率不盡相同沒(méi)有針對(duì)性,而分層分類正是針對(duì)不同地物采用不同的分類策略。故本文采用支持向量機(jī)和分層分類相結(jié)合的策略進(jìn)行土地分類方法改進(jìn)。結(jié)果證明支持向量機(jī)相比于前兩者分類效果較好,同時(shí),將支持向量機(jī)和分層分類相結(jié)合,充分發(fā)揮各自優(yōu)勢(shì),分類效果顯著提高。1990年分層分類得到的總體分類精度是86.10%,Kappa系數(shù)是0.81。2000年分層分類得到的總體分類精度是93.66%,Kappa系數(shù)是0.91。2010年分層分類得到的總體精度是92.80%,Kappa系數(shù)是0.90。(3)對(duì)河套灌區(qū)土地分類結(jié)果數(shù)據(jù)借助土地利用動(dòng)態(tài)度進(jìn)行統(tǒng)計(jì)分析。為了獲得定量的河套灌區(qū)從1990年至2000年的土地利用變化統(tǒng)計(jì)信息,土地利用動(dòng)態(tài)度定量地描述了土地利用的變化速度,對(duì)預(yù)測(cè)未來(lái)土地利用變化趨勢(shì)有積極的作用。故本文采用該方法獲得了河套灌區(qū)從1990年至2010年土地利用變化統(tǒng)計(jì)結(jié)果,1990年至2000年的總體土地利用動(dòng)態(tài)度是2.08%,2000年至2010年的總體土地利用動(dòng)態(tài)度是2.83%,而1990年至2010年的總體土地利用動(dòng)態(tài)度是1.13%。綜合這二十年間的變化情況,變化速率最快的是居民地,達(dá)13.80%,而變化速率最慢的是耕地,達(dá)0.97%。
[Abstract]:As one of the three super large irrigation areas in China, Hetao Irrigation District is located in the middle reaches of the Yellow River and is also the largest self-flowing irrigation area in Asia. In order to accurately understand the land use change in Hetao Irrigation area from 1990 to 2010, the land use of Hetao Irrigation area in 1990, 2000 and 2010 was classified and statistically analyzed by using TM remote sensing image as the data source. It provides scientific basis for land resource management and rational allocation of water in Hetao Irrigation District. The main contents of this paper are as follows: (1) the paper studies the mosaic and tailoring of TM remote sensing images in Hetao Irrigation area, and sets up a land classification system for irrigation areas by visual interpretation of the stitched panoramic images. The three stages of remote sensing images are composed of three images, each of which includes seven bands. According to the characteristics of the large amount of information in TM remote sensing images and the large area of Hetao irrigation area, this paper chooses rotation. The scale scaling invariant SURF algorithm is used to extract the feature points of the image, and the image after registration is fused based on the Shearlet transform algorithm, and the complete panorama of Hetao irrigation area is obtained after stitching. With the help of vector files in Hetao Irrigation area, the irregular polygon clipping algorithm is used to extract the research area. The best exponent method is simple and efficient, so this paper uses this method to obtain the best band combination of each image, respectively) and uses three supervised classification methods, including the maximum likelihood method, respectively. The neural network method and the support vector machine method are used to classify the land, and then the hierarchical classification method and the support vector machine method are combined to improve the classification method. Support vector machine (SVM) classification is a method used to study machine classification in the case of small samples, which has shown good applicability in the application of remote sensing image classification. At the same time, the traditional single classifier does not have the same accuracy for different ground objects classification, and the hierarchical classification adopts different classification strategies for different ground objects. Therefore, the strategy of combining support vector machine and hierarchical classification is used to improve the land classification method. The results show that the support vector machine is more effective than the former two classification methods. At the same time, support vector machine and hierarchical classification are combined to give full play to their respective advantages. The overall classification accuracy obtained by stratified classification in 1990 is 86.10kappa coefficient is 0.81.The overall classification accuracy obtained by stratified classification in 2000 is 93.666kappa coefficient is 0.91.The overall accuracy of stratified classification in 2010 is 92.80kappa coefficient 0.90.0.The Kappa coefficient of stratified classification is 0.90.0.The Kappa coefficient of stratified classification in 1990 is 0.90.The Kappa coefficient of stratified classification is 0.90.93. Land classification data of irrigation district were analyzed by land use dynamic degree. In order to obtain quantitative statistical information of land use change from 1990 to 2000 in Hetao Irrigation area, the land use dynamic degree describes quantitatively the change rate of land use, which plays an active role in predicting the trend of land use change in the future. Therefore, the statistical results of land use change from 1990 to 2010 in Hetao Irrigation District were obtained by using this method. The total land use dynamic attitude from 1990 to 2000 was 2.08, the total land use dynamic attitude from 2000 to 2010 was 2.83, and that from 1990 to 2010 was 2.83. The overall attitude of land use is 1.13%. Synthesizing the change of these two decades, the fastest change rate is the resident land (13.80%), while the slowest change rate is cultivated land (0.97%).
【學(xué)位授予單位】：西北農(nóng)林科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：S127;F301.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 郭俊理;郭尚家;靳晴文;;寶雞市區(qū)土地利用變化分析[J];寶雞文理學(xué)院學(xué)報(bào)(自然科學(xué)版);2016年01期

2 馮逍;肖鵬峰;李琦;劉小喜;吳小翠;;三維Gabor濾波器與支持向量機(jī)的高光譜遙感圖像分類[J];光譜學(xué)與光譜分析;2014年08期

3 曹琦;陳興鵬;師滿江;姚勇;;黑河中游土地利用/覆蓋變化及主導(dǎo)因素驅(qū)動(dòng)力[J];農(nóng)業(yè)工程學(xué)報(bào);2014年05期

4 宮麗瑋;閆利;;植被指數(shù)和紋理特征在影像解譯中的應(yīng)用[J];北京測(cè)繪;2014年01期

5 李鑫;丁建麗;王剛;張嚴(yán)俊;張U，

本文編號(hào)：1866819