基于多层感知器网络的FTIR高光谱数据温度和发射率光谱同步反演  

Multi-Layer Perceptron Neural Network Based Algorithm for Simultaneous Retrieving Temperature and Emissivity from Hyperspectral FTIR Data

作　　者：程洁[1,3] 肖青[1] 李小文[1,2] 柳钦火[1,3] 杜永明[1,2] 

机构地区：[1]中国科学院遥感应用研究所,北京师范大学,遥感科学国家重点实验室,北京100101 [2]北京师范大学,北京100875 [3]中国科学院研究生院,北京100039

出　　处：《光谱学与光谱分析》2008年第4期780-783,共4页Spectroscopy and Spectral Analysis

基　　金：中国科学院知识创新工程重要方向性项目(KZCX2-YW-3B,KZCK3-YW-338);国家自然科学基金重点项目(40730525);国家自然科学基金项目(40501042)资助

摘　　要：文章以土壤为例,首先指出了典型的温度反射率分离算法由高光谱FTIR数据反演温度和发射率的局限;当地物出射能量的真值和地物真实温度对应的黑体辐射在数值上的差别与仪器的噪声等效光谱辐射亮度在相同的数量级上时,产生奇异发射率的概率很大,野外测量时这种现象在714和1250cm-1附近经常发生。针对这个局限,构建了一个三层的感知器(MLP)网络,利用ASTER光谱库中的土壤发射率光谱生成训练样本,MODIS光谱库中的土壤发射率光谱生成测试样本,对网络进行训练和测试,取得了比较好的结果。同时利用光谱平滑迭代算法(ISSTES)由测试样本反演土壤的温度和发射率,并与MLP方法的结果进行比较,MLP方法反演的土壤发射率精度在可接受的范围之内,略低于ISSTES算法,MLP方法的优点在于,它能够克服典型的温度发射率算法的局限,可以作为典型的温度发射率分离算法有益的补充。The present paper firstly points out the defect of typical temperature and emissivity separation algorithms when dealing with hyperspectral FTIR data： the conventional temperature and emissivity algorithms can not reproduce correct emissivity value when the difference between the ground-leaving radiance and object＇s blackbody radiation at its true temperature and the instrument random noise are on the same order, and this phenomenon is very prone to occur rence near 714 and 1 250 cm-1 in the field measurements. In order to settle this defect, a three-layer perceptron neural network has been introduced into the simulta- neous inversion of temperature and emissivity from hyperspectral FTIR data. The soil emissivity spectra from the ASTER spectral library were used to produce the training data, the soil emissivity spectra from the MODIS spectral library were used to pro- duce the test data, and the result of network test shows the MLP is robust. Meanwhile, the ISSTES algorithm was used to re- trieve the temperature and emissivity form the test data. By comparing the results of MLP and ISSTES, we found the MLP can overcome the disadvantage of typical temperature and emisivity separation, although the rinse of derived emissivity using MLP is lower than the ISSTES as a whole. Hence, the MLP can be regarded as a beneficial complementarity of the typical temperature and emissivity separation.

关 键 词：FTIR 土壤 温度 发射率光谱 神经网络 

分 类 号：TP7[自动化与计算机技术—检测技术与自动化装置]