發(fā)布時間：2018-04-18 14:33

  本文選題：濕地 + 遙感��； 參考：《中南林業(yè)科技大學(xué)》2017年碩士論文

【摘要】：目前,濕地資源作為一種與森林、海洋生態(tài)系統(tǒng)同樣地位的重要自然資源,它的變化和可持續(xù)保護(hù)利用是地球科學(xué)核心內(nèi)容的重要部分。利用遙感技術(shù)在濕地區(qū)域進(jìn)行監(jiān)測成為越來越重要的手段,可以解決濕地研究中一些科學(xué)問題,如濕地類型信息、濕地景觀信息、濕地變化特征等。通過這些研究可以推動濕地區(qū)域生態(tài)資源保護(hù)和發(fā)展,從而保障政府對濕地保護(hù)工程和保護(hù)區(qū)建設(shè)的科學(xué)、正確的決策。研究選擇東洞庭湖作為主要的研究區(qū),以GF-1遙感影像為數(shù)據(jù)源,對所采用的遙感數(shù)據(jù)源進(jìn)行預(yù)處理,再進(jìn)行最佳波段組合、最佳融合方法分析,然后將遺傳算法技術(shù)和Fisher判別法引入濕地類型提取,一方面優(yōu)化支持向量機(jī)算法實現(xiàn)濕地類型高精度提取,另一方面采用Fisher判別法對濕地類型進(jìn)行簡單、高效、自動提取;最后對四種算法的提取結(jié)果進(jìn)行分析評價,旨在分析出適用于GF-1遙感影像濕地類型提取的最優(yōu)算法,完善濕地類型提取算法體系,為今后濕地遙感研究提供依據(jù)。主要研究結(jié)果如下:(1)最佳波段組合分析研究研究綜合考慮光譜特征與信息量大小,通過標(biāo)準(zhǔn)差、信息熵、最佳指數(shù)3個定量評價指標(biāo)以及目視效果判斷,確定GF-1遙感影像最佳波段組合為RGB=432。(2)最佳融合方法分析研究融合效果評價采用了定量評價的方式,從光譜繼承性和空間融入度兩方面分析融合效果,采用了均值、相關(guān)系數(shù)、熵、標(biāo)準(zhǔn)差、梯度五個指標(biāo)分別對主成分變換融合、Gram-Schmidt融合和基于平滑濾波亮度調(diào)整融合的結(jié)果進(jìn)行定量評價。在光譜繼承性和空間融入度上SFIM融合法都要優(yōu)于其他融合方法,SFIM既能提高空間分辨率,又很好的保留了光譜信息,有利于信息提取,對于GF-1號遙感影像來說SFIM是一種較好的融合方法。(3)遺傳算法優(yōu)化的支持向量機(jī)對支持向量機(jī)和遺傳算法優(yōu)化的支持向量機(jī)提取結(jié)果進(jìn)行精度評價,支持向量機(jī)的總體精度83.79%,kappa系數(shù)0.7985,遺傳算法優(yōu)化的支持向量機(jī)的總體精度88.14%,kappa系數(shù)0.8527,兩者總體精度相差4.35個百分點(diǎn),kappa系數(shù)相差0.0542,提取時間基本一致。充分說明,遺傳算法優(yōu)化的支持向量機(jī)在濕地類型提取上的有效性,且提取精度明顯提高。(4)Fisher判別法自動提取在GF-1號遙感影像濕地類型提取時間上,Fisher判別法收斂性大為改善,數(shù)據(jù)迭代次數(shù)明顯減少,提取速度提升顯著,提取結(jié)果只需要49秒。在大批量影像處理上Fisher判別法優(yōu)勢明顯,既能滿足了總體精度要求,也能大大縮短濕地類型提取時間。(5)濕地類型提取算法比較通過實驗得出:總體精度上遺傳算法優(yōu)化的支持向量機(jī)與面向?qū)ο鬀Q策樹最高為88.14%,其次是Fisher判別法總體精度85.17%,支持向量機(jī)總體精度最低83.79%;遺傳算法優(yōu)化的支持向量機(jī)Kappa系數(shù)最高0.8572,其次是面向?qū)ο鬀Q策樹和Fisher判別法Kappa系數(shù)分別為0.8217、0.8129,支持向量機(jī)的Kappa系數(shù)最低0.7985,這充分說明遺傳算法優(yōu)化的支持向量機(jī)在濕地類型提取的總體精度優(yōu)于Fisher判別法和支持向量機(jī),Kappa系數(shù)優(yōu)于其他三種提取方法,且改善明顯。Fisher判別法的提取時間最短49秒,其次是面向?qū)ο鬀Q策樹165秒,遺傳算法優(yōu)化的支持向量機(jī)和支持向量機(jī)提取時間分別為253秒、249秒,這說明Fisher判別法在濕地類型提取時間上優(yōu)于其他三種分類方法,且提升顯著。
[Abstract]:At present, the wetland resources as a kind of important natural resources and forests, marine ecosystem the same position, it changes and sustainable utilization is an important part of the scientific core of the earth. Monitoring become more and more important means in the wetland area by using remote sensing technology, can solve some scientific research directions, such as wetland type information wetland landscape, information, wetland changes. These studies can promote wetland ecological resources protection and development, in order to protect the government of wetland protection and protection area construction engineering science, the correct decision. On the East Dongting Lake as a case study area, using GF-1 image as data source for remote sensing data source. The pretreatment, then the optimal band combination analysis of optimal fusion method, and then the genetic algorithm and Fisher discriminant method is introduced into the wetland The type of extraction, a optimization algorithm of support vector machine to achieve high precision extraction of wetland types, on the other hand, using the Fisher discriminant method of wetland types were simple, efficient, automatic extraction; at the end of the four algorithms of the extraction results were evaluated to analyze optimal algorithm suitable for GF-1 remote sensing image extraction of wetland types, improve wetland the type of extraction system, provide the basis for future wetland remote sensing research. The main results are as follows: (1) the optimal band combination analysis considering the spectral features and the size of the information, the standard deviation of information entropy, the best judgment index 3 quantitative evaluation index and visual effect, determine the best combination for the band GF-1 remote sensing image RGB=432. (2) the best fusion methods of analysis and evaluation study on the effect of fusion using quantitative evaluation method, integration of the two aspects of the melt and the space from the spectrum of inheritance Moreover, the average correlation coefficient, entropy, standard deviation, gradient index of five principal component transform fusion, Gram-Schmidt fusion and quantitative evaluation of smoothing filter brightness adjustment based on the fusion results. The spectrum of inheritance and space integration SFIM fusion method is superior to other fusion methods, which can improve the SFIM the spatial resolution, and it is good to retain the spectral information, is conducive to the information extraction for GF-1 remote sensing image SFIM is a better fusion method. (3) support vector machine optimized by genetic algorithm of support vector machine and genetic algorithm support vector machine extraction results to evaluate the accuracy, the overall accuracy of 83.79% vector machine, kappa coefficient is 0.7985, the overall accuracy of 88.14% support vector machine optimized by genetic algorithm, the kappa coefficient is 0.8527, the overall accuracy is 4.35 percentage points, kappa coefficient is 0.0542, The extraction time is basically the same. Shows that the effectiveness of support vector machine optimized by genetic algorithm in wetland extraction, and the extraction accuracy is obviously improved. (4) Fisher discrimination method of automatic extraction in the extraction time of GF-1, the remote sensing image of wetland types, Fisher discriminant of convergence is greatly improved, the number of iterations of data significantly reduced the extraction rate significantly enhance the extraction results, only 49 seconds. In large quantities of image processing Fisher discriminant method has obvious advantages, which can not only satisfy the requirements of the overall accuracy, can greatly shorten the extraction time. Wetlands (5) wetland type extraction algorithm by comparing experiment results: the overall accuracy of support vector machine optimized by genetic algorithm with the object oriented decision tree up to 88.14%, followed by Fisher discriminant analysis, the overall accuracy is 85.17%, the overall accuracy of support vector machine minimum 83.79%; genetic algorithm support vector machine Kappa factor 0.8572, followed by object oriented decision tree and Fisher discriminant Kappa coefficients were 0.8217,0.8129, support vector machine Kappa the lowest coefficient of 0.7985, which fully shows that the support vector machine optimized by genetic algorithm method and support vector machine in overall accuracy is better than Fisher wetland extraction, the Kappa coefficient is better than the other three kinds of extraction methods. The extraction time and improved.Fisher discrimination method of the shortest 49 seconds, followed by object oriented decision tree for 165 seconds, genetic algorithm and support vector machine SVM extraction time was 253 seconds, 249 seconds, indicating that the extraction time Fisher discriminant method is superior to the other three kinds of classification methods in wetland types, and enhance significant.

【學(xué)位授予單位】：中南林業(yè)科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：X87;X171
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本文編號：1768767