本文選題：Hyperion高光譜遙感　切入點：城市地物　出處：《浙江大學(xué)》2013年碩士論文　論文類型：學(xué)位論文

【摘要】：城市化飛速發(fā)展的今天,對于城市環(huán)境信息的監(jiān)測對于改善城市生態(tài)環(huán)境、規(guī)范城市規(guī)劃管理等具有重要的意義。城市下墊面尤其是大量不同年代、材料、成分的人工地物,其光譜多樣性遠(yuǎn)超過自然環(huán)境。高光譜數(shù)據(jù)豐富的光譜信息可以彌補(bǔ)傳統(tǒng)遙感數(shù)據(jù)源光譜分辨率方面的不足,從而實現(xiàn)對城市地物更為精細(xì)的識別和分類。對此,本文從以下幾個方面對高光譜城市地物識別進(jìn)行了探討： 首先,本文闡述了采用高光譜數(shù)據(jù)進(jìn)行城市研究的意義和目標(biāo)；介紹了高光譜遙感硬件的發(fā)展概況,概括了大氣校正技術(shù)、光譜特征提取、影像融合及地物識別和分類技術(shù)等影像分析技術(shù)的研究動態(tài),以及高光譜遙感在地質(zhì)調(diào)查、植被分析、水環(huán)境監(jiān)測、農(nóng)業(yè)信息和大氣環(huán)境等領(lǐng)域的應(yīng)用；提出里本次研究的主要內(nèi)容和研究框架。 其次,對本次研究的區(qū)塊現(xiàn)狀進(jìn)行介紹,針對Hyperion高光譜數(shù)據(jù),通過幾何校正、輻射定標(biāo)、波段選擇以及FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hyper-cubes)大氣校正消除Smile效應(yīng)等一系列預(yù)處理,獲得地物的真實反射率；再根據(jù)研究區(qū)內(nèi)幾種典型地物在全波段范圍內(nèi)的光譜特性以及不同波段的信息量和相關(guān)性,對波段進(jìn)行重采樣,保留信息含量多、相關(guān)性小和地物可分性強(qiáng)的波段作為最佳波段。 在此基礎(chǔ)上,通過總結(jié)歸納遙感影像數(shù)據(jù)融合的發(fā)展現(xiàn)狀及各常用算法的優(yōu)缺點,采用Gram-Schimdt (GS)正交化變換法,以高分辨率的SPOT全色影像為基準(zhǔn)影像,對高光譜數(shù)據(jù)進(jìn)行融合處理,融合后的影像在空間分辨率上有明顯的提高,并且地物的光譜信息損失不大,保持了原有的光譜形態(tài)。 再次,對城市常見地物類型的光譜特征進(jìn)行分析,根據(jù)研究區(qū)實際情況,通過實地調(diào)查及遙感影像目視解譯,確定九類城市地物作為研究對象。在此基礎(chǔ)上針對現(xiàn)有地物端元提取方法的不足,采用純凈像元指數(shù)與光譜角匹配(SAM, Spectral Angle Mapper)相結(jié)合的方法提取了九類地物的端元光譜并建立參考光譜庫,作為后續(xù)地物識別分類的基礎(chǔ)。 最后,針對現(xiàn)有常用的高光譜影像識別及分類方法,采用監(jiān)督分類中的光譜角匹配方法(SAM)和線性光譜分解方法(LSU, Linear Spectral Unmixing)分別對最佳波段選擇前后及數(shù)據(jù)融合前后的高光譜影像進(jìn)行識別分類,并進(jìn)行圖像結(jié)果及地物面積統(tǒng)計分析。結(jié)果表明：星載高光譜數(shù)據(jù)可以較為準(zhǔn)確的識別出常見的城市地物類型,采用的識別方法對結(jié)果尤為重要,并且高光譜影像的融合處理可以一定程度上提高分類結(jié)果的精細(xì)度和準(zhǔn)確度；當(dāng)采用SAM方法對融合后的影像進(jìn)行識別時,其地物面積統(tǒng)計誤差僅為11.61%,而采用LSU方法對未經(jīng)融合的影像進(jìn)行識別,其誤差將達(dá)到65.63%,并且其圖像結(jié)果渾濁不清,無法分辨各個地物類型的分布及聚集形式。
[Abstract]:With the rapid development of urbanization, the monitoring of urban environmental information is of great significance for improving the urban ecological environment and standardizing urban planning and management. The spectral diversity is far greater than that of the natural environment. The spectral information rich in hyperspectral data can make up for the deficiencies in spectral resolution of traditional remote sensing data sources, thus achieving more precise recognition and classification of urban features. This paper discusses the recognition of hyperspectral urban features from the following aspects:. Firstly, the significance and goal of using hyperspectral data in urban research are described, and the development of hyperspectral remote sensing hardware is introduced. The atmospheric correction technology and spectral feature extraction are summarized. The research trend of image analysis technology, such as image fusion and ground object recognition and classification, and the application of hyperspectral remote sensing in geological survey, vegetation analysis, water environment monitoring, agricultural information and atmospheric environment. Put forward the main contents and research framework of this study. Secondly, the current status of the blocks in this study is introduced. For Hyperion hyperspectral data, a series of preprocessing processes such as geometric correction, radiometric calibration, band selection and FLAASH Fast Line-of-sight Atmospheric Analysis of Spectral Hyper-cubes-based atmospheric correction to eliminate Smile effect are presented. According to the spectral characteristics of several typical ground objects in the study area and the amount and correlation of information in different bands, the true reflectivity of the ground objects is obtained. The band with small correlation and strong separability is the best band. On this basis, by summing up the development of remote sensing image data fusion and the advantages and disadvantages of the common algorithms, we adopt the Gram-Schimdt / GSH) orthogonal transform method, and take the high-resolution SPOT panchromatic image as the reference image. The fusion of hyperspectral data shows that the spatial resolution of the fused image is improved obviously and the spectral information of the ground object is not lost so that the original spectral form is maintained. Thirdly, the spectral characteristics of common urban features are analyzed, according to the actual situation of the study area, through field investigation and remote sensing image visual interpretation, Nine kinds of urban features are determined as the object of study. Based on this, the shortcomings of the existing methods for extracting endpoints of ground objects are pointed out. Using the method of pure pixel index and Spectral Angle Mapper, the end-element spectra of nine kinds of ground objects were extracted and the reference spectrum database was established as the basis for the subsequent classification of ground objects. Finally, aiming at the existing hyperspectral image recognition and classification methods, The spectral angle matching method (SAM) and the linear spectral decomposition method (LSUU, Linear Spectral mixing) were used to identify and classify the hyperspectral images before and after the optimal band selection and data fusion, respectively. The results show that the space-borne hyperspectral data can accurately identify the common types of urban features, and the recognition method is particularly important to the results. And the fusion processing of hyperspectral image can improve the precision and accuracy of the classification results to some extent. When the fusion image is identified by SAM method, The statistical error of the ground object area is only 11.61, but the LSU method is used to recognize the unfused image, the error will reach 65.63, and the result of the image is not clear, so it can not distinguish the distribution and aggregation form of each type of object.
【學(xué)位授予單位】：浙江大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：P237;TU984

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