一个用神经网络优化的针对ASTER数据反演地表温度和发射率的多波段算法

AN OPTIMIZAED MULTIPLE-BAND ALGORITHM BY USING NEURAL NETWORK FOR SEPARATING LAND SURFACE EMISSIVITY AND TEMPERATURE FROM ASTER IMAGERY

提出了针对ASTER数据同时反演地表温度和发射率的多波段算法。即利用ASTER数据的第11～14热红外波段建立热辐射传输方程，并同时对相应波段的发射率建立近似线性方程，得到6个方程6个未知数，从而形成了针对ASTER数据的同时反演地表温度和发射率的多通道算法。利用3种方法求解方程：①先分类，然后进行数学计算；②利用最小二乘法；③利用神经网络方法。利用辐射传输模型MODTRAN4模拟数据进行反演及验证分析，结果表明，神经网络能够提高算法的精度和实用性，反演的地表温度平均误差为0．5℃，反演的发射率平均误差分别在0．007（11、12波段）和0．006（13、14波段）以下。

A multiple - band algorithm is proposed in this paper to separate land surface temperature and emissivity from ASTER data. Three methods can be used to solve the equations. The first is the performance of classification for the images and the formulation of different equations, followed by the solution of the equations. The second is least - squares. The third is the simulation of the database according to the characteristics of object emissivities and the utilization of the neural network to solve equations. An analysis indicates that the neural network can improve the practicability and accuracy of the algorithm. The accuracy of neural network proves to be very high for the test data simulated from MODTRAN 4. An application example is given in this paper, and the analysis suggests that the neural network also possesses the self - study capability. The simulation data show that the average error of land surface temperature is below 0.5℃, and the error of emissivity in band 11 ～ 14 is below 0.007 （ band 11,12 ） and 0. 006 （ band 13,14） , respectively.