Journal of Geo-information Science ›› 2020, Vol. 22 ›› Issue (8): 1597-1606.doi: 10.12082/dqxxkx.2020.190385
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PU Dongchuan1,2(), WANG Guizhou1,3,4, ZHANG Zhaoming1,3,4,*(
), NIU Xuefeng2, HE Guojin1,3,4, LONG Tengfei1,3,4, YIN Ranyu1,3,4, JIANG Wei1,3,4, SUN Jiayue2
Received:
2019-07-19
Revised:
2019-11-25
Online:
2020-08-25
Published:
2020-10-25
Contact:
ZHANG Zhaoming
E-mail:pudc17@mails.jlu.edu.cn;zhangzm@radi.ac.cn
Supported by:
PU Dongchuan, WANG Guizhou, ZHANG Zhaoming, NIU Xuefeng, HE Guojin, LONG Tengfei, YIN Ranyu, JIANG Wei, SUN Jiayue. Urban Area Extraction based on Independent Component Analysis and Random Forest Algorithm[J].Journal of Geo-information Science, 2020, 22(8): 1597-1606.DOI:10.12082/dqxxkx.2020.190385
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Tab. 1
4 feature dimensions selected by experiments and their corresponding features"
序号 | 特征名称 | |
---|---|---|
a | 光谱维度 | Coastal、Blue、Green、Red、NIR、SWIR1、SWIR2 |
b | ICA维度 | ICA1、ICA2、ICA3、ICA4、ICA5、ICA6、ICA7 |
c | PCA维度 | PCA1、PCA2、PCA3、PCA4、PCA5、PCA6、PCA7 |
d | 纹理维度 | Mean.(Mean)、Var.(Variance)、Hom.(Homogeneity)、Con.(Contrast)、Dis.(Dissimilarity)、Ent.(Entropy)、ASM.(Angular Second Moment)、Cor.(Correlation) |
Tab. 2
Comparison of the accuracy of 7 classification schemes for extracting urban area"
序号 | 特征数量 | 特征名称 | 总体精度/% | Kappa系数 | 错分率/% | 漏分率/% | 运行时间/s |
---|---|---|---|---|---|---|---|
1 | 7 | 光谱特征 | 84.3 | 0.71 | 29.2 | 19.6 | 101.1 |
2 | 8 | 纹理特征 | 39.5 | 0.27 | 75.6 | 33.3 | 132.3 |
3 | 7 | PCA特征 | 81.9 | 0.62 | 25.3 | 23.9 | 97.5 |
4 | 7 | ICA特征 | 87.1 | 0.78 | 20.8 | 20.2 | 103.7 |
5 | 15 | 光谱+纹理 | 86.3 | 0.69 | 30.5 | 28.3 | 197.8 |
6 | 14 | 光谱+PCA | 89.2 | 0.79 | 23.3 | 31.5 | 164.2 |
7 | 14 | 光谱+ICA | 93.1 | 0.86 | 17.5 | 18.4 | 157.1 |
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