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Remote Sensing for Natural Resources    2024, Vol. 36 Issue (2) : 105-115     DOI: 10.6046/zrzyyg.2022465
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Derivation of tasseled cap transformation coefficients for GF-6 WFV sensor data
ZHANG Haojie1(), YANG Lijuan1,2, SHI Tingting2,3, WANG Shuai1()
1. School of Geography and Oceanography, Minjiang University, Fuzhou 350108, China
2. Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350108, China
3. School of Economics and Management of Minjiang University, Fuzhou 350108, China
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Abstract  

Tasseled cap transformation (TCT), one of the most common methods in image enhancement, has been extensively applied in remote sensing. However, high-resolution satellite sensors (like GF-6 WFV) usually lack short-wave infrared bands, leading to distorted wetness components in TCT coefficients obtained using the conventional Gram-Schmidt (G-S) orthogonalization method. Hence, this study selected 12 GF-6 WFV images covering different regions, temporal phases, and seasons, as well as six synchronous Landsat8 images for wetness component regression, determining the wetness component coefficient of the GF-6 WFV sensor. Furthermore, it employed the inversed G-S algorithm to deduce the brightness, greenness, and other components, deriving the TCT coefficient of the GF-6 WFV sensor. This study found that: ①Adjusting the derivation order of the wetness component in TCT (that is, the derivation of the wetness component comes before that of other components like brightness and greenness) allows more effective derivation of the TCT coefficient of the GF-6 WFV sensor, avoiding the distortion of the wetness component; ②The TCT components of the GF-6 WFV sensor exhibited stable characteristics, with surface features displaying a typical “tasseled cap” distribution in the feature plane composed by various TCT components; ③Despite the differences in band setting and spectral response, GF-6 WFV and Landsat8 OLI sensors manifested high consistency in corresponding TCT components, with a correlation coefficient of up to 0.8.
Keywords GF-6      tasseled cap transformation      Landsat8      Gram-Schmid orthogonalization      wetness component     
ZTFLH:  TP79  
Issue Date: 14 June 2024
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Haojie ZHANG
Lijuan YANG
Tingting SHI
Shuai WANG
Cite this article:   
Haojie ZHANG,Lijuan YANG,Tingting SHI, et al. Derivation of tasseled cap transformation coefficients for GF-6 WFV sensor data[J]. Remote Sensing for Natural Resources, 2024, 36(2): 105-115.
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https://www.gtzyyg.com/EN/10.6046/zrzyyg.2022465     OR     https://www.gtzyyg.com/EN/Y2024/V36/I2/105
序号 所属地区 地点 成像日期 GF-6景序列号 Landsat8列/行 影像用途
1 中国东北 辽宁省朝阳市北票市 2021/10/27 482807 121/031
2 中国东北 河北省衡水市冀州区 2021/11/26 490462 123/034
3 中国中部 江苏省南京市溧水区 2020/02/20 303336 120/038
4 中国中部 贵州省贵阳市花溪区 2020/11/14 385098 124/041 实验影像
5 中国东部 浙江省杭州市建德市 2021/08/30 466717 —
6 中国南部 广西壮族自治区河池市宜州区 2021/01/15 402120 —
7 中国西北 新疆维吾尔自治区阿拉尔市 2021/09/18 472503 —
8 中国北部 山东省济南市历下区 2019/08/17 247481 123/034
9 中国中部 湖北省荆门市东宝区 2021/09/21 473310 —
10 中国东部 安徽省黄山市徽州区 2020/04/09 320284 — 验证影像
11 中国南部 广西壮族自治区贺州市钟山县 2022/03/08 517213 —
12 中国西北 新疆维吾尔自治区阿克苏地区阿克苏市 2019/01/29 178877 147/031
Tab.1  List of GF-6 and Landsat8 data sources
Fig.1  Spectral curves of different types of ground objects
序号 变换分量 蓝光 绿光 红光 近红外 红边1 红边2 紫光 黄光
1 亮度 0.248 6 0.323 1 0.346 4 0.541 6 0.345 0 0.438 1 0.051 9 0.327 0
2 绿度 -0.240 4 -0.332 6 -0.380 8 0.560 4 -0.189 0 0.431 5 -0.086 7 -0.378 2
3 湿度 0.123 2 0.386 1 -0.512 6 -0.205 2 -0.432 0 0.338 6 -0.189 9 0.440 0
4 蓝度 0.723 2 -0.002 3 -0.220 6 0.269 6 -0.246 7 -0.305 5 0.420 1 -0.157 6
5 黄度 -0.080 8 0.178 4 0.114 5 -0.381 3 -0.050 8 0.506 2 0.658 0 -0.333 7
6 橙度 0.004 7 -0.037 5 0.007 0 0.005 4 0.012 5 -0.010 9 -0.013 6 0.009 1
7 灰度 0.145 8 0.061 0 -0.583 3 -0.162 2 0.766 7 0.035 2 -0.028 0 -0.136 0
8 第8分量 -0.528 1 0.106 1 -0.264 9 0.269 4 0.053 1 -0.309 5 0.554 4 0.401 5
Tab.2  GF-6 WFV tasseled cap transformation coefficients
Fig.2  Original images and tasseled cap transformation components
Fig.3  Theoretical distribution of typical ground objects on two-dimensional plane
Fig.4  Comparison of two-dimensional scatterplot distributions of tasseled cap transformation results
同步影像 缨帽变换分量 R RMSE
贵阳市 亮度 0.857 0.051 7
绿度 0.860 0.022 6
湿度 0.751 0.034 7
济南市 亮度 0.876 0.042 7
绿度 0.853 0.035 6
湿度 0.773 0.034 2
Tab.3  Synchronous image verification of GF-6 and Landsat8
卫星传感器 前2个
分量/%		 前3个
分量/%		 第3
分量/%		 参考文献
IKONOS 98.3 99.8 1.5 文献[23]
QuickBird-2 98.3 99.7 1.4 文献[12]
CBERS-02B — 98.0 — 文献[16]
HJ-1 A/B 98.0 99.9 1.9 文献[17]
GF-1 WFV2 97.3 98.9 1.6 文献[4]
GF-6 WFV 97.4 98.3 0.9 本文
Tab.4  Total variance explained by the first two or three components
序号 变换分量 蓝光 绿光 红光 近红外 红边1 红边2 紫光 黄光
1 亮度 0.273 7 -0.256 1 0.262 0 0.741 6 0.093 1 6.565 4 0.134 4 -0.484 1
2 绿度 0.221 0 -0.268 9 -0.179 2 -0.095 5 -0.529 9 -26.740 1 0.107 2 -0.072 9
3 湿度 0.346 0 -0.380 6 -0.514 6 -0.221 0 0.112 0 -0.093 8 -0.583 2 -0.265 5
4 蓝度 0.517 2 0.575 6 -0.340 1 0.247 4 -0.550 4 -6.381 9 -0.151 2 0.226 7
5 黄度 0.353 9 -0.194 6 -0.382 6 -0.238 5 0.011 1 2.337 0 0.762 7 0.068 8
6 橙度 0.470 8 0.411 1 0.520 0 -0.275 6 0.735 5 8.582 8 0.020 4 -0.252 0
7 灰度 0.077 3 -0.102 5 -0.050 0 0.443 2 0.833 4 6.620 0 -0.039 4 0.598 7
8 第8分量 0.392 3 -0.419 0 0.801 2 -0.098 0 0.118 9 17.087 9 -0.165 5 0.515 9
Tab.5  Inverse tasseled cap transformation coefficients of GF-6 WFV
Fig.5  Comparison of original images and tasseled cap transformation fusion result
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