地球科学进展

地球科学进展 ›› 2018, Vol. 33 ›› Issue (6): 641 -652. doi: 10.11867/j.issn.1001-8166.2018.06.0641

研究论文 上一篇    下一篇

GF-1 WFV2传感器数据的缨帽变换系数反演
王帅( ), 徐涵秋 *( ), 施婷婷   
  1. 1.福州大学环境与资源学院,福建 福州 350116
    2.福州大学遥感信息工程研究所,福建 福州 350116
    3.福州大学福建省水土流失遥感监测评估重点实验室,福建 福州 350116
  • 收稿日期:2017-10-27 修回日期:2018-05-16 出版日期:2018-06-20
  • 通讯作者: 徐涵秋 E-mail:wswin8@hotmail.com;hxu@fzu.edu.cn
  • 基金资助:
    *国家自然科学基金项目“星—地协同多源遥感数据不透水面辨识机理与反演模型研究”(编号:41501469);福建省测绘地理信息局项目“国产高分辨率卫星数据的交互比较研究”(编号:2017JX02)资助.

Retrieval of Tasseled Cap Transformation Coefficients for GF-1 WFV2 Sensor Data

Shuai Wang( ), Hanqiu Xu *( ), Tingting Shi   

  1. 1.College of Environment and Resources, Fuzhou University, Fuzhou 350116, China
    2.Institute of Remote Sensing Information Engineering, Fuzhou University, Fuzhou 350116, China
    3.Fujian Provinicial Key Laboratory of Remote Sensing of Soil Erosion, Fuzhou University,Fuzhou 350116, China
  • Received:2017-10-27 Revised:2018-05-16 Online:2018-06-20 Published:2018-07-23
  • Contact: Hanqiu Xu E-mail:wswin8@hotmail.com;hxu@fzu.edu.cn
  • About author:

    First author:Wang Shuai (1992-), male, Fuyang City, Anhui Province, Master student. Research areas include remote sensing of resources and environment. E-mail:wswin8@hotmail.com

  • Supported by:
    Project supported by the National Natural Science Foundation of China “Study on identification mechanism and inversion model of impervious surface for satellite ground collaborative multi-source remote sensing data”(No.41501469);The Science and Technology Development Fund of Fujian Provincial Administration of Surveying, Mapping and Geoinformation “Study on the cross comparison of domestic high-resolution satellite images” (No.2017JX02).
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针对国产GF-1 WFV2传感器,提出一种缨帽变换系数的推导方法。利用全国范围内不同地区的18幅GF-1影像和5幅同步的Landsat 8影像,选择了大量覆盖不同地物类型的典型样本并分析光谱变化特征。在此基础上,用3幅Landsat 8同步影像对湿度分量回归,然后再用施密特正交化方法依次推导出两两正交的亮度分量和绿度分量。结果显示,推导的前2个分量能解释缨帽变换总变化量的97%以上,前3个分量能解释总变化量的99%以上。针对不同区域影像的实验结果表明,各分量都具有很好的稳定性,而不同分量之间的散点图也都有相似的分布特征,说明该系数有很好的适用性。与验证用的Landsat 8同步影像对比后发现,推导的缨帽变换结果与Landsat 8相应的分量之间都有很好的相关性和一致性,尽管2种传感器波段设置及光谱响应能力不同,但相关性仍高达0.9。对GF-1 WFV1,WFV3和WFV4传感器影像的实验结果表明,针对GF-1 WFV2传感器提出的缨帽变换系数,也可以很好地应用在WFV3和WFV1传感器上,但不建议在WFV4传感器上使用。

关键词: GF-1, 缨帽变换, 亮度, 绿度, 湿度

Aiming at GF-1 WFV2 sensor, a method for deriving Tasseled Cap Transformation (TCT) coefficients was proposed in this paper. Based on 18 GF-1 images and 5 synchronous Landsat 8 images of different regions throughout the country, a large number of typical samples covering different ground types were selected, and the spectral characteristics of these samples were then analyzed. On this basis, the wetness component of the TCT was retrieved by the regression using three Landsat 8 and WFV2 synchronous images. The orthogonal brightness and greenness components were then derived with the Gram-Schmidt orthogonalization method. The results showed that the derived first two components can explain more than 97% of total variation, and the first three components can explain more than 99% of the total variation. The experimental results from different regions showed that each component had good stability, and the scatter-plots of different components also exhibited similar distribution patterns. This indicates that the derived coefficients have good applicability. Compared with the validation images, the results of TCT are well correlated with corresponding Landsat 8 components. Although these two sensors have different band settings and different spectral responses, their correlation coefficient is still as high as 0.9. The application results for GF-1 WFV1, WFV3 and WFV4 showed that the coefficients derived from WFV2 sensor can also be used for WFV3 and WFV1 sensors, but they are not recommended for WFV4 sensor.

Key words: GF-1, Tasseled Cap Transformation, Brightness, Greenness, Wetness.

中图分类号: 

表1 数据源列表
Table 1 Source lists of data
所属
区域		 地点 成像
日期		 GF-1
产品号		 Landsat 8
列/行
实验
影像
地区		 中部
地区		 驻马店、信阳 20151020 1116843
开封、商丘、亳州 20160504 1562284 123/36
驻马店、信阳、阜阳 20160908 1562295 123/37
驻马店、信阳、阜阳 20160908 1813878
东部
地区		 日照市 20160429 1552922 120/36
南京、滁州 20160429 1552923
南方
地区		 柳州、来宾 20161216 2047782
玉林、贵港 20170401 2277357
柳州、贺州 20170401 2277356
西部
地区		 乌兰乌拉湖,西北 20160721 1712329
乌兰乌拉湖,西北 20161117 1970495
东北地区 营口市 20160507 1569457
验证
影像
地区		 中部
地区		 聊城、濮阳 20160504 1562286 123/35
驻马店、信阳、阜阳 20160516 1586464
东部地区 连云港、淮安 20160429 1552914 120/37
南方地区 贺州、梧州 20161228 2093687
西部地区 乌兰乌拉湖,西南 20161117 1970493
东北地区 营口市 20160919 1839479
表1 数据源列表
Table 1 Source lists of data
所属
区域		 地点 成像
日期		 GF-1
产品号		 Landsat 8
列/行
实验
影像
地区		 中部
地区		 驻马店、信阳 20151020 1116843
开封、商丘、亳州 20160504 1562284 123/36
驻马店、信阳、阜阳 20160908 1562295 123/37
驻马店、信阳、阜阳 20160908 1813878
东部
地区		 日照市 20160429 1552922 120/36
南京、滁州 20160429 1552923
南方
地区		 柳州、来宾 20161216 2047782
玉林、贵港 20170401 2277357
柳州、贺州 20170401 2277356
西部
地区		 乌兰乌拉湖,西北 20160721 1712329
乌兰乌拉湖,西北 20161117 1970495
东北地区 营口市 20160507 1569457
验证
影像
地区		 中部
地区		 聊城、濮阳 20160504 1562286 123/35
驻马店、信阳、阜阳 20160516 1586464
东部地区 连云港、淮安 20160429 1552914 120/37
南方地区 贺州、梧州 20161228 2093687
西部地区 乌兰乌拉湖,西南 20161117 1970493
东北地区 营口市 20160919 1839479
图1 影像成像区分布
Fig.1 Distribution of imaging regions
图1 影像成像区分布
Fig.1 Distribution of imaging regions
图2 不同地物类型的光谱特征
(a)土壤光谱特征1;(b)土壤光谱特征2;(c)土壤光谱特征3;(d)水体光谱特征;(e)生长期植被光谱特征;(f)成熟期植被光谱特征
Fig.2 Spectral signature of different ground types
(a)Soil spectral signature 1;(b)Soil spectral signature 2;(c)Soil spectral signature 3;(d)Water spectral signature;(e)Spectral signature of growth vegetation;(f)Spectral signature of matured vegetation
图2 不同地物类型的光谱特征
(a)土壤光谱特征1;(b)土壤光谱特征2;(c)土壤光谱特征3;(d)水体光谱特征;(e)生长期植被光谱特征;(f)成熟期植被光谱特征
Fig.2 Spectral signature of different ground types
(a)Soil spectral signature 1;(b)Soil spectral signature 2;(c)Soil spectral signature 3;(d)Water spectral signature;(e)Spectral signature of growth vegetation;(f)Spectral signature of matured vegetation
表2 GF-1 WFV2缨帽变换系数
Table 2 TCT coefficient of GF-1 WFV2
缨帽变换分量 蓝光波段 绿光波段 红光波段 近红外波段
1. 亮度分量 0.2428 0.4347 0.4169 0.7604
2. 绿度分量 -0.2637 -0.4480 -0.5584 0.6465
3. 湿度分量 -0.0392 0.7530 -0.6542 -0.0593
4. 第4分量 -0.9327 0.2082 0.2939 0.0177
表2 GF-1 WFV2缨帽变换系数
Table 2 TCT coefficient of GF-1 WFV2
缨帽变换分量 蓝光波段 绿光波段 红光波段 近红外波段
1. 亮度分量 0.2428 0.4347 0.4169 0.7604
2. 绿度分量 -0.2637 -0.4480 -0.5584 0.6465
3. 湿度分量 -0.0392 0.7530 -0.6542 -0.0593
4. 第4分量 -0.9327 0.2082 0.2939 0.0177
图3 原始影像及缨帽变换分量图(实验影像:驻马店;验证影像:营口市、连云港、贺州市)
Fig.3 The original images and their TCT components (Test image: Zhumadian; Validation image: Yingkou, Lianyungang and Hezhou)
图3 原始影像及缨帽变换分量图(实验影像:驻马店;验证影像:营口市、连云港、贺州市)
Fig.3 The original images and their TCT components (Test image: Zhumadian; Validation image: Yingkou, Lianyungang and Hezhou)
图4 地物在不同投影平面上的理论分布特征
(a)植被平面;(b)土壤平面;(c)过渡带平面
Fig.4 Distribution characteristics of ground objects in different projection planes
(a)Plane of vegetation view;(b)Plane of soils view;(c)Plane of transition zone view
图4 地物在不同投影平面上的理论分布特征
(a)植被平面;(b)土壤平面;(c)过渡带平面
Fig.4 Distribution characteristics of ground objects in different projection planes
(a)Plane of vegetation view;(b)Plane of soils view;(c)Plane of transition zone view
表3 GF-1和Landsat 8同步影像验证
Table 3 Validation using Synchronous images between GF-1 and Landsat 8
同步影像 缨帽变换分量 相关系数 均方根误差
亮度 0.8981 0.1079
聊城市 绿度 0.8914 0.0956
湿度 0.8282 0.052
亮度 0.9588 0.098
连云港 绿度 0.9181 0.0671
湿度 0.8758 0.0598
表3 GF-1和Landsat 8同步影像验证
Table 3 Validation using Synchronous images between GF-1 and Landsat 8
同步影像 缨帽变换分量 相关系数 均方根误差
亮度 0.8981 0.1079
聊城市 绿度 0.8914 0.0956
湿度 0.8282 0.052
亮度 0.9588 0.098
连云港 绿度 0.9181 0.0671
湿度 0.8758 0.0598
表4 前2、3个分量能解释的信息变化量
Table 4 Total variance explained by the first 2 or 3 component
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 [20]
QuickBird 2 98.3 99.7 1.4 [21]
CBERS-02B / 98.0 / [25]
HJ-1 A/B 98.0 99.9 1.9 [26]
GF-1 WFV2 97.3 98.9 1.6 本文
表4 前2、3个分量能解释的信息变化量
Table 4 Total variance explained by the first 2 or 3 component
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 [20]
QuickBird 2 98.3 99.7 1.4 [21]
CBERS-02B / 98.0 / [25]
HJ-1 A/B 98.0 99.9 1.9 [26]
GF-1 WFV2 97.3 98.9 1.6 本文
图5 缨帽变换结果在不同投影平面的分布特征 (0~255拉伸)
Fig.5 The tasseled cap distribution in different projection plane (Stretched between 0~255)
图5 缨帽变换结果在不同投影平面的分布特征 (0~255拉伸)
Fig.5 The tasseled cap distribution in different projection plane (Stretched between 0~255)
图6 GF-1其他3个WFV传感器缨帽变换结果
Fig.6 Tasseled cap transformation results of the other three WFV sensors in GF-1
图6 GF-1其他3个WFV传感器缨帽变换结果
Fig.6 Tasseled cap transformation results of the other three WFV sensors in GF-1
表5 与WFV2传感器相近时相的影像对
Table 5 Images pairs between WFV2 and other sensors
地点 WFV1 WFV2 WFV3 WFV4 相差天数/d
淮安 20160503 20160429 / / 4
扬州 20160503 20160429 / / 4
淮安 / 20160429 20160425 / 4
营口 / 20160919 20160920 / 1
淮安 / 20160429 / 20160430 1
聊城 / 20160504 / 20160517 13
表5 与WFV2传感器相近时相的影像对
Table 5 Images pairs between WFV2 and other sensors
地点 WFV1 WFV2 WFV3 WFV4 相差天数/d
淮安 20160503 20160429 / / 4
扬州 20160503 20160429 / / 4
淮安 / 20160429 20160425 / 4
营口 / 20160919 20160920 / 1
淮安 / 20160429 / 20160430 1
聊城 / 20160504 / 20160517 13
表6 不同传感器之间缨帽变换的相关性统计
Table 6 Correlation statistics of tasseled cap transformation from different sensors
对比传感器 影像地区 亮度 绿度 湿度 第4分量
淮安 0.861 0.930 0.886 0.704
WFV1-WFV2 扬州 0.936 0.941 0.865 0.614
平均 0.899 0.936 0.876 0.659
淮安 0.970 0.985 0.955 0.934
WFV3-WFV2 营口 0.908 0.887 0.893 0.791
平均 0.939 0.936 0.924 0.863
淮安 0.810 0.909 0.853 0.706
WFV4-WFV2 聊城 0.751 0.907 0.854 0.665
平均 0.781 0.908 0.854 0.686
表6 不同传感器之间缨帽变换的相关性统计
Table 6 Correlation statistics of tasseled cap transformation from different sensors
对比传感器 影像地区 亮度 绿度 湿度 第4分量
淮安 0.861 0.930 0.886 0.704
WFV1-WFV2 扬州 0.936 0.941 0.865 0.614
平均 0.899 0.936 0.876 0.659
淮安 0.970 0.985 0.955 0.934
WFV3-WFV2 营口 0.908 0.887 0.893 0.791
平均 0.939 0.936 0.924 0.863
淮安 0.810 0.909 0.853 0.706
WFV4-WFV2 聊城 0.751 0.907 0.854 0.665
平均 0.781 0.908 0.854 0.686
图7 GF-1 WFV传感器观测方向
Fig.7 Observation direction of different WFV sensors in GF-1
图7 GF-1 WFV传感器观测方向
Fig.7 Observation direction of different WFV sensors in GF-1
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