Atmospheric Correction of Airborne Hyperspectral Image Based on Fruit Fly-Powell Optimization Algorithm
PAN Cen-cen1, YAN Qing-wu1, DING Jian-wei2, ZHANG Qian-qian1, TAN Kun1*
1. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
2. The Second Surveying and Mapping Institute of Hebei Province, Shijiazhuang 050037, China
Abstract：Atmospheric correction of airborne hyperspectral is the basis of quantitative retrieval of hyperspectral remote sensing. However, the comparison analysis of aerial and field synchronous data was relatively rare, and it was mainly studied in this paper that the different atmospheric correction methods are compared with the fieldwork spectral of Hyspex hyperspectral remote sensing data. Based on the existing several atmospheric correction methods, a novel atmospheric correction method was proposed in this paper: Firstly, we used Fruit fly-Powell optimization algorithm, spectral performance parameters, that is, shift at the center wavelength (σλ) and Full Width of Half Maximum (σFWHM) are retrieved, so the original spectral is recalibrated. We used the spectral of recalibration, and MODerate spectral resolution atmospheric TRANsmittance algorithm (MODTRAN) was applied for atmospheric correction. Ground synchronous measured reflectance data of five types of typical objects was used, and it was then evaluated the accuracy of the method proposed in this paper and other five generally used atmospheric correction methods: QUick Atmospheric Correction (QUAC), Empirical Line Correction (ELC), Second Simulation of the Satellite Signal in the Solar Spectrum(6S) atmospheric correction, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) correction and MODTRAN atmospheric correction. Determination coefficient (R2) and root mean square error (RMSE) were introduced to evaluate the accuracy of the atmospheric correction results. Accuracy evaluation results showed that the proposed MODTRAN optimized based on Fruit fly-Powell algorithm in this paper was the best, with R2 above 80%, and RMSE within 15%; the results of MODTRAN，FLAASH and 6S atmospheric correction methods were closer to the proposed new method, and also the accuracy of the three atmospheric correction results were relatively stable, with R2 above 70%, RMSE around 20%. Moreover, QUAC and ELC methods were instable. It is concluded that Fruit fly-Powell algorithm is effective and feasible to estimate σλ and σFWHM, and thus the accuracy of the novel atmospheric correction method is better than the existing various atmospheric correction methods.
潘岑岑，闫庆武，丁建伟，张倩倩，谭 琨. 基于果蝇-鲍威尔优化的航空高光谱影像大气校正方法[J]. 光谱学与光谱分析, 2018, 38(01): 224-234.
PAN Cen-cen, YAN Qing-wu, DING Jian-wei, ZHANG Qian-qian, TAN Kun. Atmospheric Correction of Airborne Hyperspectral Image Based on Fruit Fly-Powell Optimization Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(01): 224-234.
[1] Goetz A F. Remote Sensing of Environment, 2009, 113：S5.
[2] Brook A, Dor E B. Remote Sensing of Environment, 2011, 115(6): 1543.
[3] Griffin M K, Burke H-H K. Lincoln Laboratory Journal, 2003, 14(1): 29.
[4] Milton E J, Schaepman M E, Anderson K, et al. Remote Sensing of Environment, 2009, 113: S92.
[5] Gao B C, Montes M J, Davis C O, et al. Remote Sensing of Environment, 2009, 113: S17.
[6] Kruse F. Proceedings of the Summaries of 13th JPL Airborne Geoscience Workshop, Jet Propulsion Lab, Pasadena, CA, F, 2004.
[7] Liu Y, Wang T, Ma L, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2630.
[8] Wang T, Yan G, Ren H, et al. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(11): 3924.
[9] Richter R, Schl?pfer D, Müller A. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(5): 1772.
[10] Gao B C, Montes M J, Davis C O. Remote Sensing of Environment, 2004, 90(4): 424.
[11] Green R O, Pavri B E, Chrien T G. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(6): 1194.
[12] Vermote E F, Tanré D, Deuzé J L, et al. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 675.
[13] Liang S, Fang H. IEEE Geoscience and Remote Sensing Letters, 2004, 1(2): 112.
