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

【摘要】：隨著高光譜技術(shù)的不斷發(fā)展,高光譜遙感在樹(shù)種識(shí)別方面的應(yīng)用越來(lái)越廣泛。傳統(tǒng)遙感只能識(shí)別植被、水體、裸地等差異較大的地類(lèi),無(wú)法滿(mǎn)足喬木樹(shù)種的精細(xì)識(shí)別要求。高光譜數(shù)據(jù)具有光譜分辨率高、波段數(shù)目多、數(shù)據(jù)量龐大的顯著特點(diǎn),為喬木樹(shù)種的精細(xì)分類(lèi)與識(shí)別提供了可能。喬木樹(shù)種的分類(lèi)一直是林業(yè)遙感監(jiān)測(cè)研究的一個(gè)技術(shù)難點(diǎn),為尋找便于識(shí)別喬木樹(shù)種高光譜數(shù)據(jù)的最佳監(jiān)測(cè)窗口。本文以黃豐橋國(guó)有林場(chǎng)為主要研究區(qū),以杉木、馬褂木、馬尾松和樟樹(shù)為研究對(duì)象,以JM(Jeffreys-Matusita)距離和變換離散度為最佳監(jiān)測(cè)波段的篩選算法,然后使用馬氏距離判別法對(duì)計(jì)算所得的最佳波段窗口進(jìn)行驗(yàn)證,從而確定最佳波段區(qū)間的范圍。此外,利用MATLAB軟件GUI界面構(gòu)建框架,結(jié)合開(kāi)發(fā)組件與高光譜數(shù)據(jù)處理算法,開(kāi)發(fā)相應(yīng)的數(shù)據(jù)處理軟件,實(shí)現(xiàn)對(duì)高光譜數(shù)據(jù)的一鍵式處理,快速、高效的得出最佳監(jiān)測(cè)波段窗口。本文歷時(shí)2年多,對(duì)杉木、馬褂木、馬尾松和樟樹(shù)進(jìn)行定點(diǎn)定期觀(guān)測(cè),共采集數(shù)據(jù)2000多條,利用以上觀(guān)測(cè)數(shù)據(jù),進(jìn)行高光譜數(shù)據(jù)的預(yù)處理、相關(guān)性分析和相對(duì)輻射校正,然后使用JM距離算法和變換離散度算法計(jì)算樹(shù)種之間在所有光譜區(qū)間(340nm~2500nm)上的區(qū)分距離值,以大于等于1.9的波段區(qū)間為最佳監(jiān)測(cè)窗口,再用馬氏距離判別法對(duì)得到的最佳波段窗口進(jìn)行驗(yàn)證和精度評(píng)價(jià),最終確定喬木樹(shù)種遙感監(jiān)測(cè)的最佳波段窗口。為快速、高效處理高光譜數(shù)據(jù)提供一定的參考,為喬木樹(shù)種遙感監(jiān)測(cè)提供最佳的波段窗口。主要研究結(jié)果有:(1)對(duì)于實(shí)測(cè)喬木樹(shù)種地面高光譜數(shù)據(jù)最佳監(jiān)測(cè)波段的選擇,研究得到高光譜數(shù)據(jù)濾波、相關(guān)性分析、相對(duì)輻射校正等比較成熟有效的高光譜數(shù)據(jù)預(yù)處理方法。(2)對(duì)于實(shí)測(cè)喬木樹(shù)種地面高光譜數(shù)據(jù)最佳監(jiān)測(cè)波段的選擇,研究得出JM距離算法作為喬木樹(shù)種遙感監(jiān)測(cè)波段篩選算法比較理想。(3)研究得出喬木樹(shù)種遙感監(jiān)測(cè)最佳波段窗口為:杉木與馬褂木為1572～1591nm;杉木與馬尾松為993～1013nm;杉木與樟樹(shù)為2139～2159nm;馬褂木與馬尾松為729～742nm,1601～1606nm;馬褂木與樟樹(shù)為1710～1729nm;馬尾松與樟樹(shù)為1596～1625nm。(4)研發(fā)了一套一鍵式、快速管理與分析高光譜數(shù)據(jù)的數(shù)據(jù)處理系統(tǒng)。通過(guò)可視化界面,實(shí)現(xiàn)研究人員對(duì)數(shù)據(jù)的快速、高效處理,以及數(shù)據(jù)對(duì)人的及時(shí)響應(yīng),形成人與數(shù)據(jù)之間的直接價(jià)值連接,將數(shù)據(jù)存儲(chǔ)管理與數(shù)據(jù)處理分析集成到一起。使得系統(tǒng)可以完成高光譜數(shù)據(jù)的管理與存儲(chǔ),又能完成對(duì)高光譜數(shù)據(jù)的深入處理和分析,形成一個(gè)數(shù)據(jù)管理與分析處理的一體化平臺(tái)。
[Abstract]:With the development of hyperspectral technology, hyperspectral remote sensing is more and more widely used in tree species identification. Traditional remote sensing can only identify vegetation, water, bare land and other land species, which can not meet the requirements of fine identification of tree species. The hyperspectral data have the characteristics of high spectral resolution, large number of bands and large amount of data, which provides the possibility for the fine classification and recognition of tree species. The classification of tree species has always been a technical difficulty in forestry remote sensing monitoring. In order to find the best monitoring window for identifying hyperspectral data of tree species. In this paper, Huangfeng Bridge National Forest Farm is taken as the main research area, Chinese fir, mandarin, Masson pine and camphor tree as the research object, and the distance and transform dispersion of JMJM Jeffreys-Matusita as the best screening algorithm for monitoring band. Then the Mahalanobis distance discriminant method is used to verify the optimal band window and to determine the range of the best band range. In addition, using the MATLAB software GUI interface to build a framework, combined with the development of components and hyperspectral data processing algorithm, the development of the corresponding data processing software, to achieve the hyperspectral data one-key processing, fast and efficient to obtain the best monitoring band window. In this paper, the Chinese fir, mandarin, Pinus massoniana and camphor tree were observed at fixed points for more than 2 years. More than 2000 data were collected, which were used for preprocessing, correlation analysis and relative radiation correction of hyperspectral data. Then the JM distance algorithm and the transform dispersion algorithm are used to calculate the distance value of the tree species on all spectral intervals (340 nm-1). The best monitoring window is the band range greater than 1.9. Finally, the best band window of tree species is determined by using Markov distance discriminant method to verify and evaluate the precision of the best band window. It provides a certain reference for fast and efficient processing of hyperspectral data and provides the best band window for tree species remote sensing monitoring. The main results of this study are: (1) selection of the best monitoring band for the ground hyperspectral data of tree species measured. The filtering of hyperspectral data and the analysis of correlation are obtained. Relative radiation correction is a mature and effective preprocessing method for hyperspectral data. The selection of the best monitoring band for the ground hyperspectral data of tree species measured. It is concluded that JM distance algorithm is an ideal selection algorithm for remote sensing monitoring band of Arbor species.) the optimum band window of remote sensing monitoring for Arbor tree species is 1572 ~ 1591 nm for Chinese fir and Pinus mandarinensis; 9931 ~ 1013 nm for Chinese fir and Masson pine; and 931 nm for Chinese fir and camphor. A one-button model was developed for mandarin and Pinus massoniana at 2139nm; Masson pine and Pinus massoniana for 1601nm; mandarin and camphor for 1710101729nm; Pinus massoniana and camphor for 1596Nm. A data processing system for fast management and analysis of hyperspectral data. Through the visual interface, the researchers can deal with the data quickly and efficiently, and the data can respond to the people in time, form the direct value connection between the data and the data, and integrate the data storage management and the data processing analysis together. The system can complete the management and storage of hyperspectral data, and also complete the in-depth processing and analysis of hyperspectral data, and form an integrated platform for data management and analysis.
【學(xué)位授予單位】：中南林業(yè)科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：S771.8
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本文編號(hào)：1774596