P1 T 和P2T、P1和P2之间具有显著的相关性。根据回归方程对温度与比辐射率进行尺度校正结果表明，基于统计方法建立的回归方程能有效校正温度与比辐射率的尺度效应，但没有改变不同TES 算法之间的尺度效应相对大小。
Airborne hyperspectral thermal infrared data spans a very broad requirement of application. Presently, serveral developed countries carried out the application study of airborne hyperspectral thermal infrared imaging. It is of core theoretical and practical problem to study the algorithm of temperature and emissivity separation algorithms. The main data resources include two aspects: TASI data and field measurement results.The research discusses serveral algorithms of temperature and emissivity separation, and their scaling effect. Main results and conclusions are summarized as follows:
1. Based on the characters of TASI data, the basic process flowing for TASI data is built, whose main steps are radiance calibration, atmospheric correction, geometric correction, image mosaic, temperature and emissivity separation, the application of temperature and emissivity products.
2. The study of temperature and emissivity separation After the study of the algorithm of Aster_TES, alpha derived emissivity method, and ISSTES, this paper improves alpha derived emissivity, and ISSTES method combination with the TASI data, and then retrieves the accuracy temperature and emissivity.
During the Aster_TES method, the new empirical relationships between the εmin and the statistical parameters of emissivity spectrum, such as MMD, MMR and VAR are created. For TASI data, the accuracy of temperature is best using empirical relationships between the εmin and MMR, and the accuracy of emissivity is best using empirical relationships between the εmin and VAR.
The improved derived emissivity method introduced to the corrections of atmospheric effects, and Wien approximation is perfected theoretically. After analysis of the sensitivity and retrieve accuracy of improved method, the result shows that accuracy of temperature and emissivity is worse than Aster_TES method.
For ISSTES method, the accuracy of temperature retrieved by the cost function of second order difference is best, but the noise of temperature image is more than the other cost functions; the ISSTES method can only get the relatively emissivity shape, so other constraint conditions must be used for the accuracy value of emissivity.
Using for reference of good idea of Aster_TES and ISSTES methods, sunch as keeping the emissivity spectral shape and the smoothness of emissivity spectrum, the author puts forward the new algorithm named as emissivity and temperature separation based on noise separation (NSTES). This method takes the noise removing and other empirical relationship into consideration, and makes a difference for the precision of emissivity and the SNR of temperature images.
Spectral resolution is main affect factor for the pericision of TES algorithms. With the improvement of spectral resolution, the pericision of retrieved temperature becomes more and more high. When spectral resolution is less than 0.172μm, precision of temperature increases fast with the improvement of spectral resolution; when spectral resolution is higher than 0.172μm, precision of temperature increases slow with the improvement of spectral resolution. So 0.172μm can be taken as theoretical reference for the design of hyperspectral thermal infrared sensors.
Lastly, the author makes the temperature images application for diurnal Temperature range model, next work will apply the temperature to the thermal inertia, Evapotranspiration and the urban energy banlance.
3. Scale Effect This dissertation discusses four scale transformation methods: center pixels method, simple average method, point spread function method (PSF), wavelet transform method (WTM).
Center pixels method is not fit for scale transformation; the spatial autocorrelation of images using PSF method is higher than that of images using simple average method and wavelet transform method; but the standard deviation of images using PSF method is lower than that of images using simple average method and wavelet transform method. Wavelet transformation method transforms the image from spatial domain to frequent domain, and causes to error because of processing the edge pixels for each moving windows.
The implementation efficiency of average method is best, next best is PSF method, and the wavelet transformation method is worse. After analysis of the difference between T P1 and T P2 , the scale effect of temperature for city is greater than that for country; the scale effect of Aster_TES algorithm is less than that of NSTES algorithm. With increase in scale transformation windows, the histogram of difference in temperature becomes more and more scattered, and the peak value becomes lower and lower. There are significant relationship between T P1 and T P2, εP1 and εP2 . After regression analysis and scale correction, the result shows that the regression equation on the base of statistical method can decrease the scale effect of temperature and emissivity, but cant change the relative size of TES algorithm. Local variance and semi-variance are fit for scale choice. The optimal spatial resolution for hyperspectral thermal infrared is 6.25m~8.75m.