本文選題：鄱陽湖　切入點(diǎn)：濕地　出處：《江西理工大學(xué)》2014年碩士論文

【摘要】：隨著遙感影像應(yīng)用越來越廣泛，利用遙感影像提取濕地特征，對(duì)濕地信息進(jìn)行科學(xué)的獲取與管理，為濕地和濕地物種的保護(hù)提供了一種新途徑。而鄱陽湖濕地是亞洲面積最大的濕地，資源豐富，類型眾多，極具代表性。因此，，利用遙感技術(shù)對(duì)鄱陽湖區(qū)濕地特征進(jìn)行分類提取研究具有現(xiàn)實(shí)意義。 本文首先介紹了遙感影像分類以及濕地信息提取方面的國內(nèi)外研究現(xiàn)狀；其次，介紹了鄱陽湖區(qū)概況，并對(duì)遙感影像數(shù)據(jù)進(jìn)行了預(yù)處理，采集了樣本數(shù)據(jù)；接著，采用ISODATA分類法、K-Means分類法、平行六面體分類法、最小距離法、最大似然法、決策樹算法和BP神經(jīng)網(wǎng)絡(luò)算法分別對(duì)鄱陽湖區(qū)都昌縣濕地特征進(jìn)行分類提取，得到分類結(jié)果并評(píng)價(jià)其分類效果；然后，對(duì)決策樹算法和BP神經(jīng)網(wǎng)絡(luò)算法進(jìn)行了集成研究，提出一種基于決策樹與BP神經(jīng)網(wǎng)絡(luò)的集成算法，并利用該算法實(shí)現(xiàn)了都昌縣濕地特征分類提取，得到了分類結(jié)果并進(jìn)行了分類精度評(píng)價(jià)；最后，對(duì)文中運(yùn)用的分類方法的分類精度進(jìn)行了綜合對(duì)比分析，并采用集成算法實(shí)現(xiàn)了鄱陽湖區(qū)濕地特征提取。 通過實(shí)驗(yàn)得到，ISODATA分類法、K-Means分類法、平行六面體分類法、最小距離法、最大似然法、決策樹算法、BP神經(jīng)網(wǎng)絡(luò)算法以及集成算法的總體分類精度分別為：75.58%、82.16%、75.46%、77.63%、82.69%、85.03%、89.96%和92.93%。實(shí)驗(yàn)結(jié)果表明，基于決策樹與BP神經(jīng)網(wǎng)絡(luò)的集成算法在濕地特征提取中分類精度明顯高于其它算法，從而為濕地信息的獲取提供了一種新方法。
[Abstract]:With the application of remote sensing image more and more widely, the characteristics of wetland are extracted from remote sensing image, and the information of wetland is obtained and managed scientifically. Poyang Lake wetland is the largest wetland in Asia, rich in resources, numerous types and representative. It is of practical significance to study the classification and extraction of wetland features in Poyang Lake area by remote sensing technology. This paper first introduces the classification of remote sensing images and wetland information extraction at home and abroad research status; secondly, introduced the Poyang Lake region, and the remote sensing image data preprocessing, collected the sample data; ISODATA classification, parallel hexahedron classification, minimum distance method, maximum likelihood method, decision tree algorithm and BP neural network algorithm were used to classify and extract the wetland features of Duchang County in Poyang Lake region. Then, the decision tree algorithm and BP neural network algorithm are integrated, and an integration algorithm based on decision tree and BP neural network is proposed. The algorithm is used to extract the wetland features of Duchang County, and the classification results are obtained and the classification accuracy is evaluated. Finally, the classification accuracy of the classification method used in this paper is compared and analyzed. The integrated algorithm is used to extract the wetland features in Poyang Lake region. The total classification accuracy of K-Means classification, parallel hexahedron classification, minimum distance method, maximum likelihood method, decision tree algorithm and BP neural network algorithm are 85.58 / 75.4677.630.89. 96% and 92.933% respectively. The experimental results show that. The classification accuracy of the integrated algorithm based on decision tree and BP neural network is obviously higher than that of other algorithms in wetland feature extraction, which provides a new method for wetland information acquisition.
【學(xué)位授予單位】：江西理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：P237

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