本文選題：熱紅外高光譜 + 稀疏表示　； 參考：《中國地質(zhì)大學(xué)(北京)》2017年碩士論文

【摘要】：高光譜遙感圖像包含豐富的光譜信息,對地物具有更強的分辨能力,但也帶來了很多復(fù)雜的問題,如維數(shù)過多和數(shù)據(jù)不確定性等。傳統(tǒng)的遙感圖像處理方法較難滿足當(dāng)前遙感應(yīng)用的需求,有必要針對具體應(yīng)用需求探索專門的遙感圖像處理方法。論文以“熱紅外高光譜礦化蝕變礦物提取方法研究與應(yīng)用示范(地大北京)”項目為依托,開展了熱紅外高光譜遙感圖像的分類及應(yīng)用研究。針對熱紅外高光譜數(shù)據(jù),引入了模式識別領(lǐng)域當(dāng)前先進的方法——稀疏表示作為技術(shù)手段,綜合考慮空間維和光譜維的信息,提出了一種鄰域加權(quán)的稀疏表示分類方法,并以巖性分類為例,在甘肅柳園研究區(qū)進行了應(yīng)用。主要研究內(nèi)容與成果如下:1.以TASI數(shù)據(jù)為例,從熱紅外輻射傳輸過程出發(fā),研究了熱紅外高光譜遙感圖像的大氣校正和溫度與發(fā)射率分離預(yù)處理方法,分別研究了MODTRAN模型、ASTER-TES方法和ISSTES方法。采用MODTRAN模型和ASTER-TES方法對TASI數(shù)據(jù)進行了預(yù)處理,反演得到了研究區(qū)的TASI數(shù)據(jù)地表發(fā)射率產(chǎn)品。2.系統(tǒng)研究了稀疏表示的理論與方法,建立了基于稀疏表示的高光譜數(shù)據(jù)分類模型。對稀疏表示問題優(yōu)化模型和稀疏表示分類模型分別進行了展開研究。其一,根據(jù)稀疏表示分類方法和熱紅外高光譜遙感圖像的特性,提出了一種鄰域加權(quán)的稀疏表示分類方法(SRCWN)。其二,引入了K-SVD作為類別字典構(gòu)建的方法,基于類別字典將未知像元進行稀疏表示。其三,對稀疏表示結(jié)果進行各類別重構(gòu)誤差計算,以重構(gòu)誤差最小化規(guī)則確定未知像元所屬類別。該方法以稀疏表示分類法為基礎(chǔ),充分考慮了熱紅外高光譜遙感數(shù)據(jù)的光譜特性、鄰近空間信息和數(shù)據(jù)的稀疏性,可以更有效地對地物像元類別進行區(qū)分。在甘肅柳園研究區(qū),采用本文方法開展了巖性分類應(yīng)用,得到了研究區(qū)高光譜遙感巖性分類圖。3.結(jié)合測試數(shù)據(jù)對各分類方法進行了對比評價,本文方法在總體精度和Kappa系數(shù)上較SAM、SVM和SRC均有一定的提升。結(jié)合野外驗證資料從總體和局部的角度分別評價了TASI數(shù)據(jù)的巖性分類應(yīng)用情況,總體評價表明本文方法的分類結(jié)果與實際情況基本符合,局部表現(xiàn)較傳統(tǒng)SAM法類別邊界更為清晰。
[Abstract]:Hyperspectral remote sensing images contain rich spectral information and have a stronger ability to distinguish ground objects, but also bring many complex problems, such as excessive dimension and data uncertainty.The traditional remote sensing image processing method is difficult to meet the needs of the current remote sensing application. It is necessary to explore a special remote sensing image processing method for the specific application needs.In this paper, the classification and application of thermal infrared hyperspectral remote sensing images are studied based on the project of "extraction method and application demonstration of thermo-infrared hyperspectral mineralized altered minerals (Beijing)".For thermal infrared hyperspectral data, a neighborhood weighted sparse representation classification method is proposed by introducing the current advanced method of pattern recognition-sparse representation as a technical means, considering the spatial dimension and spectral dimension information synthetically.Taking lithologic classification as an example, it is applied in Liuyuan research area of Gansu province.The main research contents and results are as follows: 1.Taking TASI data as an example, the atmospheric correction and preprocessing methods of temperature and emissivity separation for thermal infrared hyperspectral remote sensing images are studied, and the MODTRAN model ASTER-TES method and ISSTES method are studied respectively.The MODTRAN model and ASTER-TES method are used to preprocess the TASI data, and the surface emissivity product of TASI data in the study area is obtained by inversion.The theory and method of sparse representation are studied systematically, and the classification model of hyperspectral data based on sparse representation is established.The optimization model of sparse representation problem and the classification model of sparse representation are studied respectively.Firstly, according to the characteristics of sparse representation classification method and thermal infrared hyperspectral remote sensing image, a neighborhood weighted sparse representation classification method is proposed.Secondly, K-SVD is introduced as a method to construct class dictionaries, which sparse represents unknown pixels based on category dictionaries.Thirdly, the reconstruction error of each class is calculated for the sparse representation result, and the unknown pixel belongs to a class is determined by the minimum rule of reconstruction error.Based on sparse representation classification, the spectral characteristics of thermal infrared hyperspectral remote sensing data are fully taken into account, and the sparsity of adjacent spatial information and data is considered.In the study area of Liuyuan, Gansu Province, the lithologic classification was carried out by using this method, and the hyperspectral remote sensing lithologic classification map .3in the study area was obtained.Based on the test data, the classification methods are compared and evaluated. The overall accuracy and Kappa coefficient of this method are better than that of SAMSVM and SRC.The lithologic classification and application of TASI data are evaluated from both the overall and local aspects combined with field verification data. The overall evaluation shows that the classification results of this method are basically consistent with the actual situation.The local performance is clearer than the traditional SAM method.
【學(xué)位授予單位】：中國地質(zhì)大學(xué)(北京)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：P627
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本文編號：1749301