A neural network technique for separating land surface emissivity and temperature from ASTER imagery
文献类型：EI期刊论文
| 作者 | Mao Kebiao ; Shi Jiancheng ; Tang Huajun ; Li Zhao-Ang ; Wang Xiufeng ; Chen Kim-Shan |
| 发表日期 | 2008 |
| 关键词 | Computer simulation Database systems Neural networks Nonlinear systems Optimization Radiative transfer Remote sensing |
| 英文摘要 | Four radiative transfer equations for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands 11,12,13, and 14 are built involving six unknowns (average atmospheric temperature, land surface temperature, and four band emissivities), which is a typical ill-posed problem. The extra equations can be built by using linear or nonlinear relationship between neighbor band emissivities because the emissivity of every land surface type is almost constant for bands 11,12,13, and 14. The neural network (NN) can make full use of potential information between band emissivities through training data because the NN simultaneously owns function approximation, classification, optimization computation, and self-study ability. The training database can be built through simulation by MODTRAN4 or can be obtained from the reliable measured data. The average accuracy of the land surface temperature is about 0.24 K, and the average accuracy of emissivity in bands 11, 12, 13, and 14 is under 0.005 for test data. The retrieval result by the NN is, on average, higher by about 0.7 K than the ASTER standard product (AST08), and the application and comparison indicated that the retrieval result is better than the ASTER standard data product. To further evaluate self-study of the NN, the ASTER standard products are assumed as measured data. After using AST09, AST08, and AST05 (ASTER Standard Data Product) as the compensating training data, the average relative error of the land surface temperature is under 0.1 K relative to the AST08 product, and the average relative error of the emissivity in bands 11,12,13, and 14 is under 0.001 relative to AST05, which indicates that the NN owns a powerful self-study ability and is capable of suiting more conditions if more reliable and high-accuracy ASTER standard products can be compensated. © 2007 IEEE. |
| 出处 | IEEE Transactions on Geoscience and Remote Sensing
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| 卷 | 46期:1页:200-208 |
| 收录类别 | EI |
| 语种 | 英语 |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/24693]  |
| 专题 | 地理科学与资源研究所_历年回溯文献 |
| 推荐引用方式 GB/T 7714 | Mao Kebiao,Shi Jiancheng,Tang Huajun,et al. A neural network technique for separating land surface emissivity and temperature from ASTER imagery. 2008. |
入库方式： OAI收割
来源：地理科学与资源研究所
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