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| 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 | |
| 引用本文： | 毛克彪,唐华俊,陈仲新,王永前.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[J].国土资源遥感,2007,18(3):18-22. |
| 作者姓名： | 毛克彪 唐华俊 陈仲新 王永前 |
| 作者单位： | 1. 中国农业科学院农业资源与农业区划研究所/农业部资源遥感与数字农业重点开放实验室,北京,100081;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;中国科学院研究生院,北京,100049 2. 中国农业科学院农业资源与农业区划研究所/农业部资源遥感与数字农业重点开放实验室,北京,100081 3. 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 |
| 基金项目： | 中央级公益性科研院所基本科研业务费专项资金资助项目 , 农业部资源遥感与数字农业重点开放实验室开放基金 , 国家高技术研究发展计划(863计划) , 国家科技支撑计划 |
| 摘 要： | 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MODTRAN 4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。 |
| 关 键 词： | 亮度温度 地表温度(LST) ASTER 神经网络 |
| 文章编号： | 1001-070X（2007）03-0018-05 |
| 收稿时间： | 2006-12-26 |
| 修稿时间： | 2006-12-262007-03-12 |
AN OPTIMIZAED MULTIPLE-BAND ALGORITHM BY USING NEURAL NETWORK FOR SEPARATING LAND SURFACE EMISSIVITY AND TEMPERATURE FROM ASTER IMAGERY |
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| MAO Ke-biao,TANG Hua-jun,CHEN Zhong-xin,WANG Yong-qian.AN OPTIMIZAED MULTIPLE-BAND ALGORITHM BY USING NEURAL NETWORK FOR SEPARATING LAND SURFACE EMISSIVITY AND TEMPERATURE FROM ASTER IMAGERY[J].Remote Sensing for Land & Resources,2007,18(3):18-22. | |
| Authors: | MAO Ke-biao TANG Hua-jun CHEN Zhong-xin WANG Yong-qian |
| Institution: | 1. Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Belting 100081, China; 2. State Key Laboratory of Remote Sensing Science Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal University, Beijing 100101, China; 3. Graduate School of Chinese Academy of Sciences, Beijing 100049, China |
| Abstract: | 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. |
| Keywords: | Lightness temperature LST ASTER Neural network(NN) |
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