Share Email Print
cover

Proceedings Paper

Removing vegetation using unsupervised fully constrained least squares linear spectral mixture analysis method in soils surveyed by remote sensing
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

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.

Paper Details

Date Published: 10 January 2005
PDF: 9 pages
Proc. SPIE 5657, Image Processing and Pattern Recognition in Remote Sensing II, (10 January 2005); doi: 10.1117/12.577983
Show Author Affiliations
Hongxia Luo, Southwest Normal Univ. (China)
Wuhan Univ. (China)
Huanzhuo Ye, Zhongnan Univ. of Economics and Law (China)
Yinghai Ke, Peking Univ. (China)
Jianping Pan, Wuhan Univ. (China)
Jianya Gong, Wuhan Univ. (China)
Xiaoling Chen, Wuhan Univ. (China)


Published in SPIE Proceedings Vol. 5657:
Image Processing and Pattern Recognition in Remote Sensing II
Yoshifumi Yasuoka; Stephen G. Ungar, Editor(s)

© SPIE. Terms of Use
Back to Top