Study On Preprocessing Methods Based On Thermal Airborne Spectrographic Imager

With the development of the technology of sensors,thermal infrared sensors have been improved to multi-spectral techniques and then hyper-spectral techniques currently form the single-wavelength techniques initially.Hyperspectral thermal infrared remote-sensing imagery plays an significant role in the field of geology,environment,hydrology,natural disasters and so on.It is the key problem to solve the temperature and emissivity separation in the hyper-spectral thermal infrared remote-sensing,in which the atmospheric compensation is the base.Consequently,the paper focus on the atmospheric compensation and temperature and emissivity separation based on the Thermal Airborne Spectrographic Imager(TASI).Main results and conclusions are summarized as follows:1.Atmospheric Compensation(1)The paper studies the MODTRAN based on the atmospheric radiation transferring model and gains the atmospheric spectra of different kinds of water and temperature according to the location of the study field.(2)Considering of the imagery,we carry out the research of the AAC algorithm and ISAC algorithm and find out that the AAC algorithm's noise immunity is weak resulting the results are uncertain.In order to face the question,the combination of the AAC algorithm is proposed which recalculates the transmittance radio(Tr)and the path radiance difference between strong and weak absorption channels(Pd)based on the the sign of the black body.In the temperature and emissivity separation experiments,the emissivity spectra inverted from the combination of the AAC algorithm are more closer to that from the MDOTRAN and ISAC algorithm.2.Temperature and Emissivity Separation(1)Due to the atmospheric absorption lines' residual from the initial emissivity spectrum in the module of NEM,the SR-TES algorithm is applied to remove the residual as much as possible in order to retain the best initial emissivity spectrum at the same time.(2)The empirical correlation between the minimum of the emissivity and MMD,between the minimum of the emissivity and MMR,between the minimum of theemissivity and VAR,are built respectively in the ASTER-TES algorithm and the third one shows higher accuracy.So we use the empirical correlation between the minimum of the emissivity and MMR instead of that between the minimum of the emissivity and MMD.(3)Considering of the smoothness of spectrum only,the CBTES algorithm is studied firstly.In order to test the precision of union of the smoothness of spectrum and empirical correlation,the combination of the CBTES and VAR algorithm is presented and shows higher accuracy.3.Factors of the Temperature and Emissivity SeparationWe analysis three factors,noise,atmospheric down-welling radiation and spectrum revolution,among the temperature and emissivity separation algorithms of CBTES,ASTER-TES and combination of the CBTES and VAR algorithm.And three conclusions are drew as follows:Firstly,combination of the CBTES and VAR algorithm's noise immunity is higher than the others.Secondly the combination algorithm is less sensitive to the atmospheric down-welling radiation.Finally he combination algorithm is more steady for the spectrum revolution so that it can be expanded to other data type.4.Preprocessing ProcessThe combination of the AAC algorithm and the combination of the CBTES and VAR algorithm are chose to constitute the preprocessing process.