PROGRESS IN GEOGRAPHY ›› 2018, Vol. 37 ›› Issue (12): 1705-1712.doi: 10.18306/dlkxjz.2018.12.012
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Yachun ZHANG(), Xiaodong NA*(
), Shuying ZANG
Received:
2017-09-28
Revised:
2018-10-14
Online:
2018-12-28
Published:
2018-12-28
Contact:
Xiaodong NA
E-mail:1442117098@qq.com;naxiaodong_8341@163.com
Supported by:
Yachun ZHANG, Xiaodong NA, Shuying ZANG. Wetland high precision classification based on the HJ-1A hyperspectral image[J].PROGRESS IN GEOGRAPHY, 2018, 37(12): 1705-1712.
Fig.4
Comparison of classification results based on the sparse constrained least squares spectral unmixing algorithm (SUFCLS) method and visual interpretation of SPOT5 images(a. SPOT5 image; b. classification results based on the SUFCLS method; c, d, e, f, g, and h are the enlarged parts of the red rectangles)"
Tab.1
Correlation coefficients and root mean square error between vegetation abundance estimates and validation values retrieved by the sparse constrained least squares spectral unmixing algorithm (SUFCLS) and fully constrained least squares spectral unmixing algorithm (FCLS) methods"
解混模型 | 芦苇 | 蒲草 | 羊草 | 杂草 | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
R | RMSE | R | RMSE | R | RMSE | R | RMSE | ||||
SUFCLS | 0.874** | 0.087 | 0.832** | 0.097 | 0.826** | 0.091 | 0.821** | 0.059 | |||
FCLS | 0.618** | 0.169 | 0.602** | 0.144 | 0.597** | 0.138 | 0.457** | 0.130 |
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