【摘要】：隨著傳感器技術(shù)的發(fā)展,熱紅外傳感器已經(jīng)由最初的單波段技術(shù)發(fā)展到了多波段技術(shù),又發(fā)展到目前的高光譜技術(shù)。高光譜熱紅外遙感數(shù)據(jù)在地質(zhì)、環(huán)境、水文、自然災(zāi)害等領(lǐng)域具有非常重要的作用,但熱紅外傳感器接收到的輻射亮度是溫度與比輻射率的函數(shù),所以溫度與比輻射率的分離成為熱紅外遙感的核心問(wèn)題,而大氣校正是溫度與比輻射率分離精確求解的基礎(chǔ)。因此,本文依托“熱紅外高光譜礦化蝕變礦物提取方法研究與應(yīng)用示范”項(xiàng)目,基于航空高光譜熱紅外遙感數(shù)據(jù)(TASI),就大氣校正和溫度與比輻射率分離兩個(gè)方面開(kāi)展研究工作,取得了如下成果與結(jié)論:1、大氣校正(1)基于大氣輻射傳輸模型的大氣校正方法中,本文研究了MODTRAN模型,依據(jù)研究區(qū)的地理位置,反演得到了不同水汽和溫度條件下的大氣波譜。該方法操作簡(jiǎn)單,但無(wú)法有效反映時(shí)相差異性和局地差異性。(2)基于圖像的大氣校正方法中,本文研究了AAC算法和ISAC算法,并針對(duì)AAC算法抗噪性弱,計(jì)算結(jié)果具有多樣性的問(wèn)題,提出了AAC算法的復(fù)合改進(jìn)算法。復(fù)合算法利用ISAC算法黑體像元標(biāo)定的方法,重新計(jì)算了大氣透過(guò)率之比(Tr)和相鄰兩強(qiáng)弱吸收波段的路徑輻射之差(Pd),有效解決了計(jì)算結(jié)果多樣性的問(wèn)題。利用復(fù)合改進(jìn)算法,開(kāi)展溫度與比輻射率分離實(shí)驗(yàn),結(jié)果表明反演得到的比輻射率波譜較MODTRAN模型和ISAC算法更接近野外實(shí)測(cè)波譜。2、溫度與比輻射率分離(1)針對(duì)NEM模塊反演的初始比輻射率仍有大氣吸收線(xiàn)殘留的問(wèn)題,利用逐步求精的方法(SR-TES算法)獲得最佳初始比輻射率,最大程度地去除了初始比輻射率中大氣吸收線(xiàn)的殘留。(2)基于經(jīng)驗(yàn)關(guān)系式的溫度與比輻射率分離方法中,本文研究了ASTER-TES算法,并在常用的經(jīng)驗(yàn)關(guān)系式:MMD-ε_(tái)(min)的基礎(chǔ)上,進(jìn)一步研究了MMR-ε_(tái)(min)和VAR-ε_(tái)(min),發(fā)現(xiàn)VAR-ε_(tái)(min)反演精度更高,從而改進(jìn)了經(jīng)驗(yàn)關(guān)系式。(3)基于光譜平滑的溫度與比輻射率分離方法,本文研究了基于相關(guān)性的溫度與比輻射率分離的方法(CBTES算法),并將經(jīng)驗(yàn)關(guān)系式與光譜平滑判據(jù)結(jié)合,提出了CBTES與VAR的復(fù)合算法,研究表明復(fù)合算法的反演精度更高。3、溫度與比輻射率分離的影響因素分析系統(tǒng)分析了三個(gè)影響CBTES算法、ASTER-TES算法和復(fù)合算法反演精度的因素(噪聲、大氣下行輻射和波譜分辨率),發(fā)現(xiàn)復(fù)合算法具有一定的抗噪性;對(duì)大氣下行輻射不敏感;對(duì)波譜分辨率穩(wěn)定性均較好,可以推廣到其他數(shù)據(jù)類(lèi)型。4、預(yù)處理流程基于TASI數(shù)據(jù),選取性能最好的算法,即基于AAC的復(fù)合改進(jìn)算法和CBTES與VAR的復(fù)合算法形成了預(yù)處理流程。
[Abstract]:With the development of sensor technology, thermal infrared sensor has been developed from the original single-band technology to multi-band technology, and then to the current hyperspectral technology. Hyperspectral thermal infrared remote sensing data play a very important role in geology, environment, hydrology, natural disasters and other fields, but the radiance received by the thermal infrared sensor is a function of temperature and specific emissivity. Therefore, the separation of temperature and specific emissivity becomes the core problem of thermal infrared remote sensing, and atmospheric correction is the basis for the accurate resolution of the separation of temperature and specific emissivity. Therefore, based on the project of "Research and Application demonstration of extraction method and Application of Thermo-infrared Hyperspectral mineralization alteration Minerals", based on airborne hyperspectral thermal infrared remote sensing data (TASI), this paper has carried out research work on atmospheric correction and separation of temperature and specific emissivity. In the atmospheric correction method based on atmospheric radiative transfer model, the MODTRAN model is studied in this paper. According to the geographical location of the study area, the atmospheric spectra under different water vapor and temperature conditions are obtained. This method is simple to operate, but it can not effectively reflect the temporal and local differences. (2) in the image-based atmospheric correction method, the AAC algorithm and the ISAC algorithm are studied in this paper. A compound improved AAC algorithm is proposed. The composite algorithm recalculates the atmospheric transmittance ratio (Tr) and the difference of path radiation (PD) between two adjacent strong and weak absorption bands by using ISAC algorithm blackbody pixel calibration method, which effectively solves the problem of multiplicity of calculation results. The separation experiment of temperature and specific emissivity was carried out by using compound improved algorithm. The results show that the obtained specific emissivity spectrum is closer to the field measured spectrum of 0.2 than the MODTRAN model and the ISAC algorithm. The separation of temperature and specific emissivity (1) the initial specific emissivity of the NEM module still has the problem of atmospheric absorption line residue. The optimal initial specific emissivity is obtained by using the progressive refinement method (SR-TES algorithm), and the residue of the atmospheric absorption line in the initial specific emissivity is removed to the maximum extent. (2) in the separation method of temperature and specific emissivity based on empirical relation, In this paper, ASTER-TES algorithm is studied, and on the basis of the commonly used empirical relation: MMD- 蔚 _ (min), we further study that MMR- 蔚 _ (min) and VAR- 蔚 _ (min), have higher inversion accuracy, thus improving the empirical relation. (3) the separation method of temperature and specific emissivity based on spectral smoothing. In this paper, the method of temperature and specific emissivity separation based on correlation (CBTES algorithm) is studied, and a composite algorithm of CBTES and VAR is proposed by combining empirical relation with spectral smoothing criterion. The results show that the inversion accuracy of the composite algorithm is higher than that of the composite algorithm. The factors influencing the separation of temperature and specific emissivity are analyzed systematically. Three factors (noise) affecting the retrieval accuracy of the CBTES algorithm and the composite algorithm are analyzed. Atmospheric downlink radiation and spectral resolution), it is found that the composite algorithm has some noise resistance, is not sensitive to atmospheric downlink radiation, has good stability to spectral resolution, and can be extended to other data types. 4. The pretreatment process is based on TASI data. The preprocessing process is formed by selecting the best performance algorithm, that is, the composite improved algorithm based on AAC and the composite algorithm of CBTES and VAR.
【學(xué)位授予單位】：中國(guó)地質(zhì)大學(xué)(北京)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TP722.5

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