Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery
Crop Yield Estimation Based on Unsupervised Linear Unmixing of Multidate Hyperspectral Imagery
复制标题
基于多数据高光谱图像无监督线性分解的作物产量估算
DOI：
10.1109/tgrs.2012.2198826
复制
发表时间：
2013
影响因子：
8.2
通讯作者：
Zhang, Liangpei
中科院分区：
文献类型：
--
作者：
Luo, Bin;Yang, Chenghai;Chanussot, Jocelyn;Zhang, Liangpei
高光谱图像包含数百个光谱波段，具有比多光谱图像更好地描述植物生物和化学属性的潜力，本文对其进行了评估，用于作物产量估计。高光谱图像中每个像素的光谱被认为是植被和裸露土壤光谱的线性组合。本文对近年来发展起来的自动提取植被和裸地光谱的线性分解方法进行了评价。然后根据提取的光谱计算植被丰度。为了减少这种不确定性的影响，获得稳健的估计结果，将同一地块上两个不同日期提取的植被丰度进行合并。利用两种高粱田的多数据高光谱影像进行了试验。结果表明，采用无监督线性分解方法得到的植被丰度的相关系数与在实验室测量植被和裸土光谱的有监督方法得到的相关系数相当。此外，不同日期提取的植被丰度组合可以提高相关性（从0.6提高到0.7）。
Hyperspectral imagery, which contains hundreds of spectral bands, has the potential to better describe the biological and chemical attributes on the plants than multispectral imagery and has been evaluated in this paper for the purpose of crop yield estimation. The spectrum of each pixel in a hyperspectral image is considered as a linear combinations of the spectra of the vegetation and the bare soil. Recently developed linear unmixing approaches are evaluated in this paper, which automatically extracts the spectra of the vegetation and bare soil from the images. The vegetation abundances are then computed based on the extracted spectra. In order to reduce the influences of this uncertainty and obtain a robust estimation results, the vegetation abundances extracted on two different dates on the same fields are then combined. The experiments are carried on the multidate hyperspectral images taken from two grain sorghum fields. The results show that the correlation coefficients between the vegetation abundances obtained by unsupervised linear unmixing approaches are as good as the results obtained by supervised methods, where the spectra of the vegetation and bare soil are measured in the laboratory. In addition, the combination of vegetation abundances extracted on different dates can improve the correlations (from 0.6 to 0.7).
登录
查看更多内容
DOI：
10.1109/jstars.2012.2194696
发表时间：
2012-04-01
影响因子：
5.5
作者：
Bioucas-Dias, Jose M.;Plaza, Antonio;Chanussot, Jocelyn
通讯作者：
Chanussot, Jocelyn
DOI：
--
发表时间：
1995-01
期刊：
--
影响因子：
--
作者：
J. Boardman;F. Kruse;R. Green
通讯作者：
J. Boardman;F. Kruse;R. Green
DOI：
10.1109/jstars.2011.2176721
发表时间：
2012-04
影响因子：
5.5
作者：
I. Dopido;A. Villa;A. Plaza;P. Gamba
通讯作者：
I. Dopido;A. Villa;A. Plaza;P. Gamba
DOI：
10.1109/icassp.2009.4959777
发表时间：
2009-04
期刊：
2009 IEEE International Conference on Acoustics, Speech and Signal Processing
影响因子：
--
作者：
Tsung-Han Chan;Chong-Yung Chi;Yu-Min Huang;Wing-Kin Ma
通讯作者：
Tsung-Han Chan;Chong-Yung Chi;Yu-Min Huang;Wing-Kin Ma
影响因子：
6.2
作者：
Chenghai Yang;J. Everitt
通讯作者：
Chenghai Yang;J. Everitt