發(fā)布時(shí)間：2018-12-18 09:58

【摘要】：作為地球生態(tài)系統(tǒng)的重要組成部分,植被與人類活動息息相關(guān)。植被信息提取對于監(jiān)測環(huán)境變化、農(nóng)業(yè)發(fā)展以及地質(zhì)災(zāi)害預(yù)測等方面具有重要意義。特別在山區(qū),植被信息可以作為預(yù)測塌方、泥石流等災(zāi)害的重要指標(biāo)。傳統(tǒng)獲取植被信息的方法非常有限,主要通過人工地表實(shí)測以及光學(xué)遙感兩種方法。人工地表實(shí)測通常難以實(shí)施并且無法獲取大尺度數(shù)據(jù);光學(xué)遙感易受天氣影響,在災(zāi)害多發(fā)的雨季無法獲得遙感圖像。SAR技術(shù)具有不受云、霧、雨影響的特點(diǎn),能夠全天候監(jiān)測地表信息。因?yàn)槲⒉ň哂写┩感?對植被與其他地物具有良好的區(qū)分性,并且更容易反演植被生物量信息。本文內(nèi)容是基于全極化SAR圖像的植被生物量信息提取技術(shù)研究,主要包括目標(biāo)極化分解方法研究、植被覆蓋信息提取技術(shù)研究以及植被生物量信息提取三部分內(nèi)容,具體如下:(1)Cloude分解對于后散散射系數(shù)較低的平面散射機(jī)制區(qū)域會錯(cuò)誤地分解為高熵體散射機(jī)制,對于這個(gè)現(xiàn)象,本文給出了解釋:這是由于后散散射系數(shù)較低區(qū)域信噪比較低,造成各極化通道后散散射系數(shù)差異性降低,從而表現(xiàn)為高熵體散射機(jī)制。(2)本文針對Yamaguchi分解受地形影響問題,使用極化方位角補(bǔ)償方法,降低了地形對分解方法的影響。(3)本文提出結(jié)合最大類間方差法的H/α-Wishart分類方法,有效改善水體、道路以及陰影錯(cuò)分為植被的問題,同時(shí)分類結(jié)果比傳統(tǒng)H/α-Wishart分類方法保留更多細(xì)節(jié)。(4)本文提出基于區(qū)域的Yamaguchi-SVM分類方法,改善了傳統(tǒng)Yamaguchi-SVM分類方法對于復(fù)雜地形區(qū)域分類結(jié)果零散點(diǎn)較多情況,并把分類準(zhǔn)確率從62.4%提高到71.3%。(5)本文對邛崍和昭覺兩個(gè)區(qū)域?qū)崿F(xiàn)了完整的基于全極化SAR圖像植被生物量信息提取流程。邛崍研究區(qū)測量值與反演均值的均方誤差為0.6622kg/m,相關(guān)系數(shù)為0.893;昭覺研究區(qū)實(shí)驗(yàn)點(diǎn)一有75.0%的像元的反演值在實(shí)驗(yàn)范圍內(nèi)的實(shí)驗(yàn)點(diǎn)為83.3%,兩個(gè)實(shí)驗(yàn)點(diǎn)的最大值偏移于1.62kg/m以內(nèi)。
[Abstract]:As an important part of the earth's ecosystem, vegetation is closely related to human activities. Vegetation information extraction plays an important role in monitoring environmental change, agricultural development and geological hazard prediction. Especially in mountainous areas, vegetation information can be used as an important index to predict landslides, debris flows and other disasters. The traditional methods of obtaining vegetation information are very limited, mainly through artificial surface measurement and optical remote sensing. Artificial surface measurement is usually difficult to carry out and can not obtain large-scale data. Optical remote sensing is easy to be affected by weather, and remote sensing images can not be obtained in rainy season. SAR technology is not affected by cloud, fog and rain, and can monitor surface information all the time. Because microwave is penetrating, it is better to distinguish vegetation from other ground objects, and it is easier to retrieve vegetation biomass information. The content of this paper is the research of vegetation biomass information extraction technology based on fully polarized SAR image, which includes three parts: target polarization decomposition method, vegetation cover information extraction technology and vegetation biomass information extraction. The main results are as follows: (1) the Cloude decomposition can misrepresent the plane scattering mechanism region with low backscattering coefficient into a high entropy volume scattering mechanism. The explanation given in this paper is that the difference of backscattering coefficient is reduced due to the low signal-to-noise ratio in the lower region of the backscattering coefficient. Therefore, the scattering mechanism of high entropy volume is presented. (2) in this paper, the polarization azimuth compensation method is used to solve the problem that Yamaguchi decomposition is affected by topography. The influence of terrain on decomposition method is reduced. (3) in this paper, the H / 偽-Wishart classification method combined with the maximum inter-class variance method is proposed to effectively improve the classification of water bodies, roads and shadows into vegetation. At the same time, the classification results retain more details than the traditional H- 偽-Wishart classification method. (4) this paper proposes a region-based Yamaguchi-SVM classification method, which improves the situation that the traditional Yamaguchi-SVM classification method has more scattered points for the classification results of complex terrain regions. The classification accuracy is improved from 62.4% to 71.3%. (5) the extraction process of vegetation biomass information based on fully polarized SAR images is realized for Qionglai and Zhaojue regions. The mean square error between the measured value and the inversion mean in Qionglai study area is 0.6622 kg / m, and the correlation coefficient is 0.893; In Zhaojue research area, 75.0% of the pixel inversion value is 83.3% in the experimental range, and the maximum value of the two experimental points is less than 1.62kg/m.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TN957.52

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