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Remote Sensing for Land & Resources    2020, Vol. 32 Issue (3) : 55-62     DOI: 10.6046/gtzyyg.2020.03.08
Cloud detection based on support vector machine with image features for GF-1 data
LI Xusheng1(), LIU Yufeng2(), CHEN Donghua2,3, LIU Saisai4, LI Hu2,3
1. College of Grass Industry and Environment, Xinjiang Agricultural University, Urumqi 830052, China
2. College of Computer and Information Engineering, Chuzhou University, Chuzhou 239000, China
3. College of Geography and Tourism, Anhui Normal University, Wuhu 241000, China
4. College of Geography and Tourism, Xinjiang Normal University, Urumqi 830001, China
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In the GF-1 image data applications, applying cloud layers influences accuracy of information extraction and image utilization rate, to tackling this problem, this paper proposes a support vector machine cloud detection method combining image spectral features and texture features. For GF-1 data, the method of the gray-level co-occurrence matrix is used to extract those texture features. Spectral characteristics of clouds and ground and texture characteristics serve as feature vector, and support vector machine (SVM) is used to conduct cloud detection to GF-1 data. Studies have shown that the precision and recall of this method for all kinds of cloud detection are above 99.2% and 93.9%, and the error rate is below 1.1%, which is obviously better than the cloud detection algorithm using traditional support vector machine and maximum likelihood value, and it combines the image texture and spectral characteristics, and thus it has certain universality in theory.

Keywords cloud detection      support vector machine      feature extraction      GF-1     
:  TP79  
Corresponding Authors: LIU Yufeng     E-mail:;
Issue Date: 09 October 2020
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Xusheng LI
Yufeng LIU
Donghua CHEN
Saisai LIU
Cite this article:   
Xusheng LI,Yufeng LIU,Donghua CHEN, et al. Cloud detection based on support vector machine with image features for GF-1 data[J]. Remote Sensing for Land & Resources, 2020, 32(3): 55-62.
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参数 参数值
波段范围/μm 0.45~0.52
空间分辨率/m 8
幅宽/km 60(2台相机组合)
重访周期(侧摆时)/d 4
覆盖周期(不侧摆)/d 41
Tab.1  GF-1 satellite 8 m sensor parameters
编号 相机 中心经纬度 影像获取日期
1 PMS1 E81.2°,N44.4° 2015/07/24
2 PMS2 E80.9°,N44.6° 2015/07/04
3 PMS2 E117.5°,N29.7° 2014/01/06
4 PMS2 E117.8°,N30.8° 2014/01/06
5 PMS2 E118.0°,N31.4° 2014/01/06
6 PMS1 E118.4°,N30.0° 2016/01/14
7 PMS1 E116.4°,N30.0° 2014/02/20
8 PMS1 E116.6°,N30.5° 2013/09/19
9 PMS1 E118.0°,N32.2° 2013/07/08
10 PMS1 E117.7°,N31.9° 2018/07/12
11 PMS1 E117.7°,N30.3° 2015/08/22
Tab.2  Samplee data
Fig.1  Cloud and surface features samples
编号 相机 中心经纬度 影像获取日期
1 PMS1 E116.7°,N30.6° 2015/10/01
2 PMS2 E117.9°,N30.2° 2014/04/14
3 PMS2 E118.0°,N30.2° 2014/10/06
4 PMS1 E116.0°,N30.7° 2015/09/07
Tab.3  Validation data
Fig.2  Validation data
Fig.3  Mean values and variance of clouds and local objects
Fig.4  Texture features based on grayscale co-occurrence matrix
Fig.5  Schematic diagram of linear classifier and nonlinear classifier
Fig.6  Classifier training progress diagram
云层类型 衡量指标 最大似
层云 PR 95.5 97.9 99.2 97.5
RR 69.7 85.9 97.8 84.5
ER 9.2 3.2 1.1 4.5
卷云 PR 97.5 97.8 99.5 98.3
RR 58.1 79.8 96.3 78.1
ER 8.4 4.3 0.7 4.5
积云 PR 99.2 98.9 99.8 99.3
RR 87.6 89.5 93.9 90.3
ER 1.7 2.2 0.5 1.5
点状云 PR 99.6 99.7 99.8 99.7
RR 80.8 90.2 95.6 88.9
ER 1.2 4.2 0.6 2.0
Tab.4  Comparison of accuracy of different cloud detection methods(%)
Fig.7  Detection results of different methods
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