Study on the Modeling of Earth-Atmosphere Coupling over Rugged Scenes for Hyperspectral Remote Sensing
ZHAO Hui-jie1, JIANG Cheng1*, JIA Guo-rui1, 2
1. School of Instrumentation Science & Opto-Electronics Engineering, Beihang University, Precision Opto-mechatronics Techology Key-Laboratory of Ministry of Education, Beijing 100191, China 2. Key Laboratory of Airborne Geophysics and Remote Sensing Geology, Ministry of Land and Resources, Beijing 100083, China
Abstract:Adjacency effects may introduce errors in the quantitative applications of hyperspectral remote sensing, of which the significant item is the earth-atmosphere coupling radiance. However, the surrounding relief and shadow induce strong changes in hyperspectral images acquired from rugged terrain, which is not accurate to describe the spectral characteristics. Furthermore, the radiative coupling process between the earth and the atmosphere is more complex over the rugged scenes. In order to meet the requirements of real-time processing in data simulation, an equivalent reflectance of background was developed by taking into account the topography and the geometry between surroundings and targets based on the radiative transfer process. The contributions of the coupling to the signal at sensor level were then evaluated. This approach was integrated to the sensor-level radiance simulation model and then validated through simulating a set of actual radiance data. The results show that the visual effect of simulated images is consistent with that of observed images. It was also shown that the spectral similarity is improved over rugged scenes. In addition, the model precision is maintained at the same level over flat scenes.
[1] Otterman J, Fraser R S. Applied Optics, 1979, 18: 2852. [2] Matthew M W, Adler-Golden S M, Berk A, et al. Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, 2003, 41(6): 1322. [3] Vermote E F, Tanre D, Deuze J L, et al. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35: 675. [4] Miesch C, Poutier L, Achard V, et al. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(7): 1552. [5] Miesch C, Briottet X, Kerr Y H, et al. Applied Optics, 2000, 39: 6830. [6] Lenot X, Achard V, Poutier L. Remote Sensing of Environment, 2009, 113: 1664. [7] LIU Guang-yuan, QIU Jin-yuan(刘广员,邱金垣). Chinese Journal of Atmospheric Sciences(大气科学),2004, 28(2): 311. [8] Schott J R. Remote Sensing: The Image Chain Approach. Oxford University Press, 2007. [9] Jiang C, Zhao Huijie, Jia Guorui. IEEE International Conference on Imaging Systems and Techniques. Manchester, 2012. 195. [10] Kruse F A, Boardman J W, Lefkoff A B, et al. Proc. of the 10th Australasian Remote Sensing and Photogrammetry Conference. Adelaide, 2000. 18. [11] San B T, Suzen M L. International Journal of Remote Sensing, 2005, 26(22): 5013. [12] Jay S P, Debra B, Stephen L C. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1160. [13] Iikura Y. IEEE Geoscience and Remote Sensing Symposium. Toronto, 2002, 6: 3489. [14] TONG Qing-xi, ZHANG Bing, ZHENG Lan-fen(童庆禧,张 兵,郑兰芬). Hyperspectral Remote Sensing(高光谱遥感). Beijing: Higher Education Press(北京:高等教育出版社), 2006.