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	Removing vegetation using unsupervised fully constrained least squares linear spectral mixture analysis method in soils surveying by remote sensing
	Luo, Hongxia ; Ye, Huanzhuo ; Ke, Yinghai ; Pan, Jianping ; Gong, Jianya ; Chen, Xiaoling
	2005
英文摘要	In the region covered by variable amounts of vegetation, pixel spectra received by remotely-sensed sensor with given spatial resolution are a mixture of soil and vegetation spectra, so vegetation covering on soil influences the accuracy of soils surveying by remote sensing. Mixed pixel spectra are described as a linear combination of endmember signature matrix with appropriate abundance fractions correspond to it in a linear mixture model. According to the principle of this model, abundance fractions of endmembers in every pixel were calculated using unsupervised fully constrained least squares(UFCLS) algorithm. Then the signature of vegetation correspond to its abundance fraction was eliminated, and other endmember signatures covered by vegetation were restituted by scaling their abundance fractions to sum the original pixel total and recalculating the model. After above processing, de-vegetated reflectance images were produced for soils surveying. The accuracies of paddy soils classified using these characteristic images were better than that of using the raw images, but the accuracies of zonal soils were inferior to the latter. Compared to many other image processing methods, such as K-T transformation and ratio bands, the linear spectral unmixing to removing vegetation produced slightly better overall accuracy of soil classification, so it was useful for soils surveying by remote sensing.; EI; 0
语种	英语
DOI标识	10.1117/12.577983
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/329008]
专题	地球与空间科学学院
推荐引用方式 GB/T 7714	Luo, Hongxia,Ye, Huanzhuo,Ke, Yinghai,et al. Removing vegetation using unsupervised fully constrained least squares linear spectral mixture analysis method in soils surveying by remote sensing. 2005-01-01.

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