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The adjacency effect, the contribution of the neighboring pixels to the radiance of the line of sight pixel, is caused by the Rayleigh scattering of atmospheric molecules and Mie scattering of aerosol particles. The adjacency effect will cause the reflectance of each pixel in the apparent reflectance satellite image to be between the real reflectance and the average background reflectance, reducing the accuracy of the surface reflectance inversion. Therefore, it is very important to remove the adjacency effect to improve the accuracy of retrieving the surface reflectance from satellite images. The most critical issue of the adjacency effect is to accurately calculate the weight of the contribution of each background pixel to the adjacency effect. The weight value of the contribution of each background pixel to the adjacency effect mainly depends on the spatial distance between the target pixel and the background pixel, the difference in reflectance between the target pixel and the background pixel, and the optical thickness of atmospheric molecules and the optical thickness of aerosol. At present, the commonly used weight function for calculating the weight value considers only the influence of optical thickness and spatial distance on the weight value. These weight functions are applied to a relatively uniform surface. However, when these weight functions are applied to an inhomogeneous surface, they will greatly reduce the accuracy of the adjacency effect correction. The combination of ground features in satellite images with the sub-meter spatial resolution is complex, so the influence of the difference in reflectance between the target pixel and the background pixel on the adjacency effect must be considered. The adaptive atmospheric correction algorithm proposed in this paper can adjust the weight value of the contribution of background pixels to the adjacency effect according to the spatial distance between the target pixel and the background pixel, the difference in reflectance between the target pixel and the background pixel, and the difference between the atmospheric molecules’ optical thickness and aerosol optical thickness. The adaptive atmospheric correction algorithm is used to correct the adjacency effect on GF-2 panchromatic satellite images. The results show that the adaptive atmospheric correction algorithm can effectively remove the adjacency effect in sub-meter spatial resolution optical satellite images, improve both the accuracy of quantitative study and the satellite image quality.
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Keywords:
- adjacency effect /
- sub-meter satellite image /
- adaptive atmospheric correction /
- quantitative remote sensing
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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图 1 不同目标像元反射率与背景像元反射率组合情况下的
${L_{{\rm{background}}}}/{L_{{\rm{target}}}}$ Figure 1. The value of
${L_{{\rm{background}}}}/{L_{{\rm{target}}}}$ for different combinations of target reflectance and background reflectance.
图 2 自适应大气校正算法(adaptive-AC)流程图
Figure 2. Flowchart of the adaptive-AC.
图 3 GF-2 全色及可见近红外波段相对光谱辐亮度响应
Figure 3. Relative spectral radiance response (RSRR) for GF-2 VNIR bands.
图 4 GF-2全色波段卫星图像 (a) 表观反射率图; (b) 基于自适应大气校正算法校正后的卫星影像(记为“adaptive-AC地表真实反射率图”); (c)基于6S模型中的大气校正算法校正后的卫星影像(记为“6S-AC地表真实反射率图”); (d)基于MODTRAN模型中的大气校正算法校正后的卫星影像(记为“MODTRAN-AC地表真实反射率图”)
Figure 4. GF-2 panchromatic band image: (a) Apparent reflectance image; (b) atmospheric correction result based on adaptive-AC (denoted as “adaptive-AC real surface reflectance image”); (c) atmospheric correction result based on the atmospheric algorithm in 6S model (denoted as “6S-AC real surface reflectance image”); (d) atmospheric correction result based on the atmospheric algorithm in MODTRAN model (denoted as “MODTRAN-AC real surface reflectance image”).
表 1 大气参数和观测几何条件
Table 1. Atmospheric parameters and observed geometric conditions.
成像时间 2020-03-20 11:28:33 太阳天顶角/(°) $ {37.8709}^{} $ 太阳方位角/(°) $ {152.372}^{} $ 观测天顶角/(°) $ {12.503}^{} $ 观测方位角/(°) $ {97.6684}^{} $ 气溶胶类型 大陆型气溶胶 气溶胶光学厚度 (550 nm) 0.4018 大气模式 中纬度夏季 波段 0.4—0.9 μm 
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表 2 图4中各图像的清晰度、对比度、熵值
Table 2. Values of the
${\rm{CLAR}}$ ,${\rm{CONT}}$ and${\rm{ENTR}}$ for each image in Fig.4.卫星图像 ${\rm{CLAR}}$ ${\rm{CONT}}$ ${\rm{ENTR}}$ 表观反射率图 2184.1856 0.6715 4.9749 adaptive-AC地表
真实反射率图3869.6462 0.8827 5.7793 6S-AC地表真实反射率图 2883.8700 0.7614 5.3759 MODTRAN-AC地表
真实反射率图3925.8358 0.8009 5.8047
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[1] Sei A 2015 Appl. Opt. 54 3748
Google Scholar
[2] Tanre D, Deschamps P Y, Devaux C, Herman M 1988 J. Geophys. Res. 93 15955
Google Scholar
[3] Ma J W, Qin D, Chun F 2006 IEEE Trans. Geosci. Remote Sens. 44 729
Google Scholar
[4] Quinten V, Kevin R 2018 Remote Sens. Environ. 216 586
Google Scholar
[5] Kaufman Y J 1988 IEEE Trans. Geosci. Remote Sens. 26 441
Google Scholar
[6] Malik C, Xavier L, Mireille G, Bruno L, Xavier B, Audrey M, Sylvain J, Yannick D, Veribuque S 2009 Opt. Express 27 319
Google Scholar
[7] Warren M A, Simis S G H, Martinez V V, Poser K, Bresciani M, Alikas K, Spyrakos E, Giardino C, Ansper A 2019 Remote Sens. Environ. 225 267
Google Scholar
[8] Keukelaere D L, Sterckx S, Adriaensen S, Knaeps E, Reusen I, Giardino C, Bresciani M, Hunter P, Neil C, Van D, Vaiciute D 2018 Eur. J. Remote. Sens. 51 525
Google Scholar
[9] Bulgarelli B, Giuseppe Z 2018 Remote Sens. Environ. 209 423
Google Scholar
[10] Kiselev V, Bulgarelli B, Heege T 2015 Remote Sens. Environ. 157 85
Google Scholar
[11] Minomura M, Kuze H, Takeuchi N 2001 Opt. Rev. 8 133
Google Scholar
[12] Guanter L, Richter R, Kaufmann H 2009 Int. J. Remote Sens. 30 1407
Google Scholar
[13] Semenov A A, Moshkov A V, Pozhidayev V N, Barducci A, Marcoionni P, Pippi I 2011 IEEE Trans. Geosci. Remote Sens. 49 2623
Google Scholar
[14] Svetlana Y K, Eric F V 2007 Appl. Opt. 46 4455
Google Scholar
[15] 马晓珊, 郭晓勇, 孟新, 杨震, 彭晓东, 李立钢, 谢文明 2015 红外与毫米波学报 34 250
Google Scholar
Ma X S, Guo X Y, Meng X, Yang Z, Peng X D, Li L G, Xie W M 2015 J. Infrared Millim. Waves 34 250
Google Scholar
[16] Phillip N R, Kendall L C 1995 Appl. Opt. 34 4453
Google Scholar
[17] 汤兴, 易维宁, 杜丽丽, 崔文煜 2016 光学学报 36 0228003
Google Scholar
Tang X, Yi W N, Du L, Cui W 2016 Acta Opt. Sin. 36 0228003
Google Scholar
[18] Benjamin T, Vincent R, Mireille H, Olivier H, Sebastien M, Gilles B 2016 Remote Sens. 8 696
Google Scholar
[19] Richter R 1996 Comput. Geosci. 22 785
Google Scholar
[20] Simone G, Pedersen M, Hardeberg J Y 2012 Vis. Commun. Image R. 23 491
Google Scholar
[21] Jesús A P, Federico V G, Martin M S, Carlos M D, Eduardo S E, Alfredo P A 2018 Remote Sens. 10 219
Google Scholar
[22] Xie Y, Li Z, Li D, Xu H, Li K T 2015 Remote Sens. 7 9928
Google Scholar
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