TASI数据的预处理方法研究
发布时间：2018-07-16 23:49
【摘要】：随着传感器技术的发展,热红外传感器已经由最初的单波段技术发展到了多波段技术,又发展到目前的高光谱技术。高光谱热红外遥感数据在地质、环境、水文、自然灾害等领域具有非常重要的作用,但热红外传感器接收到的辐射亮度是温度与比辐射率的函数,所以温度与比辐射率的分离成为热红外遥感的核心问题,而大气校正是温度与比辐射率分离精确求解的基础。因此,本文依托“热红外高光谱矿化蚀变矿物提取方法研究与应用示范”项目,基于航空高光谱热红外遥感数据(TASI),就大气校正和温度与比辐射率分离两个方面开展研究工作,取得了如下成果与结论:1、大气校正(1)基于大气辐射传输模型的大气校正方法中,本文研究了MODTRAN模型,依据研究区的地理位置,反演得到了不同水汽和温度条件下的大气波谱。该方法操作简单,但无法有效反映时相差异性和局地差异性。(2)基于图像的大气校正方法中,本文研究了AAC算法和ISAC算法,并针对AAC算法抗噪性弱,计算结果具有多样性的问题,提出了AAC算法的复合改进算法。复合算法利用ISAC算法黑体像元标定的方法,重新计算了大气透过率之比(Tr)和相邻两强弱吸收波段的路径辐射之差(Pd),有效解决了计算结果多样性的问题。利用复合改进算法,开展温度与比辐射率分离实验,结果表明反演得到的比辐射率波谱较MODTRAN模型和ISAC算法更接近野外实测波谱。2、温度与比辐射率分离(1)针对NEM模块反演的初始比辐射率仍有大气吸收线残留的问题,利用逐步求精的方法(SR-TES算法)获得最佳初始比辐射率,最大程度地去除了初始比辐射率中大气吸收线的残留。(2)基于经验关系式的温度与比辐射率分离方法中,本文研究了ASTER-TES算法,并在常用的经验关系式:MMD-ε_(min)的基础上,进一步研究了MMR-ε_(min)和VAR-ε_(min),发现VAR-ε_(min)反演精度更高,从而改进了经验关系式。(3)基于光谱平滑的温度与比辐射率分离方法,本文研究了基于相关性的温度与比辐射率分离的方法(CBTES算法),并将经验关系式与光谱平滑判据结合,提出了CBTES与VAR的复合算法,研究表明复合算法的反演精度更高。3、温度与比辐射率分离的影响因素分析系统分析了三个影响CBTES算法、ASTER-TES算法和复合算法反演精度的因素(噪声、大气下行辐射和波谱分辨率),发现复合算法具有一定的抗噪性;对大气下行辐射不敏感;对波谱分辨率稳定性均较好,可以推广到其他数据类型。4、预处理流程基于TASI数据,选取性能最好的算法,即基于AAC的复合改进算法和CBTES与VAR的复合算法形成了预处理流程。
[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.
【学位授予单位】：中国地质大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP722.5
[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.
【学位授予单位】：中国地质大学(北京)
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP722.5
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