【摘要】：為實(shí)現(xiàn)區(qū)域尺度上對(duì)作物整體長(zhǎng)勢(shì)的實(shí)時(shí)、無(wú)損監(jiān)測(cè),本文以2012年6月-7月在黑河流域開(kāi)展的大型星-機(jī)-地遙感綜合試驗(yàn)獲取的CASI航空觀測(cè)數(shù)據(jù)及地面實(shí)測(cè)數(shù)據(jù)為主要數(shù)據(jù)源,以eCognition影像處理軟件為平臺(tái),利用多尺度分割技術(shù)對(duì)高光譜遙感影像進(jìn)行信息提取,進(jìn)而對(duì)長(zhǎng)勢(shì)參數(shù)LAI、Cab、N進(jìn)行反演研究,旨在探討和分析面向?qū)ο蟮亩喑叨确指顚?duì)高光譜遙感作物長(zhǎng)勢(shì)參數(shù)反演的影響,以求從理論和方法上獲得有關(guān)高光譜數(shù)據(jù)農(nóng)業(yè)應(yīng)用的一點(diǎn)新成果和經(jīng)驗(yàn)。本文主要圍繞以下三點(diǎn)進(jìn)行深入探討研究：首先,基于面向?qū)ο蟮亩喑叨确指罴夹g(shù),結(jié)合目視解譯和逐步試錯(cuò)法,并在相對(duì)區(qū)域重疊度的評(píng)價(jià)指標(biāo)下篩選出最優(yōu)分割尺度參數(shù),即各個(gè)波段的權(quán)重因子為1,形狀因子權(quán)重為0.4,緊密度為0.5,分割尺度大小為70。其次,在綜合考慮CASI影像特征和以往研究的基礎(chǔ)上,對(duì)作物長(zhǎng)勢(shì)參數(shù)與植被指數(shù)進(jìn)行統(tǒng)計(jì)描述及相關(guān)性分析,建立由反演能力及其穩(wěn)定性均較好的植被指數(shù)組成的多元線性遙感估測(cè)模型。最后,根據(jù)面向?qū)ο蠖喑叨确指詈笥跋竦奶攸c(diǎn),結(jié)合符合研究區(qū)實(shí)際情況的作物長(zhǎng)勢(shì)參數(shù)反演模型,對(duì)研究區(qū)以?xún)煞N實(shí)驗(yàn)方案即影像的先分割再反演方法和先反演再分割方法進(jìn)行對(duì)比分析研究,實(shí)現(xiàn)了區(qū)域尺度上作物整體長(zhǎng)勢(shì)的遙感監(jiān)測(cè)。研究結(jié)果表明：先分割后反演的結(jié)果優(yōu)于先反演后分割方法,其LAI、Cab、N的模型估測(cè)精度不僅都達(dá)到了80%以上,而且決定系數(shù)R2也均通過(guò)了0.95可靠性水平的顯著性檢驗(yàn),充分展示了面向?qū)ο蠖喑叨确指罴夹g(shù)應(yīng)用于作物參數(shù)反演的獨(dú)特優(yōu)勢(shì),使得針對(duì)整體大田作物的反演結(jié)果更加客觀、準(zhǔn)確,具有普適性,實(shí)現(xiàn)了高光譜遙感影像對(duì)于大面積大區(qū)域的作物長(zhǎng)勢(shì)信息的有效提取。
[Abstract]:In order to achieve real-time, non-destructive monitoring of the overall growth of crops at the regional scale, In this paper, the CASI aeronautical observation data and ground measured data obtained from the large-scale satellite-machine-ground remote sensing experiment carried out in the Heihe River Basin from June to July 2012 are taken as the main data sources, and the eCognition image processing software is used as the platform. Using multi-scale segmentation technology to extract information from hyperspectral remote sensing image, and then to study the inversion of growth parameter LAI,Cab,N. The purpose of this paper is to discuss and analyze the effect of object-oriented multi-scale segmentation on the inversion of hyperspectral remote sensing crop growth parameters. In order to obtain some new achievements and experiences about the application of hyperspectral data in agriculture in theory and method. This paper mainly focuses on the following three aspects: firstly, based on the object-oriented multi-scale segmentation technology, combined with visual interpretation and step by step trial and error method, the optimal segmentation scale parameters are selected under the evaluation index of relative regional overlap degree. That is, the weight factor of each band is 1, the weight of shape factor is 0.4, the tightness is 0.5, and the scale of segmentation is 70. Secondly, on the basis of considering the characteristics of CASI images and previous studies, the relationship between crop growth parameters and vegetation index was analyzed. A multivariate linear remote sensing estimation model composed of vegetation indices with good inversion ability and stability was established. Finally, according to the characteristics of the object oriented multi-scale segmented image and the inversion model of crop growth parameters in accordance with the actual situation in the study area, In this paper, two experimental schemes, the first segmentation and then inversion, and the first inversion and segmentation, are compared and analyzed, and the remote sensing monitoring of the whole crop growth on the regional scale is realized. The results show that the results of first segmentation and then inversion are superior to those of the first inversion and then the segmentation method. The model estimation accuracy of LAI,Cab,N not only reaches more than 80%, but also the determination coefficient R2 has passed the significance test of 0.95 reliability level. It fully demonstrates the unique advantages of object-oriented multi-scale segmentation technology in crop parameter inversion, which makes the inversion results for the whole field crop more objective, accurate and universal. The hyperspectral remote sensing image can effectively extract crop growth information from large area and large area.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類(lèi)號(hào)】：S127

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