TY - JOUR
T1 - Automated hyperspectral vegetation index retrieval from multiple correlation matrices with hypercor
AU - Aasen, Helge
AU - Gnyp, Martin Leon
AU - Miao, Yuxin
AU - Bareth, George
N1 - Publisher Copyright:
© 2014 American Society for Photogrammetry and Remote Sensing.
PY - 2014/8/1
Y1 - 2014/8/1
N2 - Hyperspectral vegetation indices have shown high potential for characterizing, classifying, monitoring, and modeling of vegetation and agricultural crops. Correlation matrices from hyperspectral vegetation indices and plant growth parameters help select important wavelength domains and identify redundant bands. We introduce the software HyperCor for automated preprocessing of narrowband hyperspectral field data and computation of correlation matrices. In addition, we propose a multi-correlation matrix strategy which combines multiple correlation matrices from different datasets and uses more information from each matrix. We apply this method to a large multi-temporal spectral library to derive vegetation indices and related regression models for rice biomass detection in the tillering, stem elongation, heading and across all growth stages. The models are calibrated with data from three consecutive years and validated with two other years. The results reveal that the multi-correlation matrix strategy can improve the model performance by 10 to 62 percent, depending on the growth stage.
AB - Hyperspectral vegetation indices have shown high potential for characterizing, classifying, monitoring, and modeling of vegetation and agricultural crops. Correlation matrices from hyperspectral vegetation indices and plant growth parameters help select important wavelength domains and identify redundant bands. We introduce the software HyperCor for automated preprocessing of narrowband hyperspectral field data and computation of correlation matrices. In addition, we propose a multi-correlation matrix strategy which combines multiple correlation matrices from different datasets and uses more information from each matrix. We apply this method to a large multi-temporal spectral library to derive vegetation indices and related regression models for rice biomass detection in the tillering, stem elongation, heading and across all growth stages. The models are calibrated with data from three consecutive years and validated with two other years. The results reveal that the multi-correlation matrix strategy can improve the model performance by 10 to 62 percent, depending on the growth stage.
UR - http://www.scopus.com/inward/record.url?scp=84904738647&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84904738647&partnerID=8YFLogxK
U2 - 10.14358/PERS.80.8.785
DO - 10.14358/PERS.80.8.785
M3 - Article
AN - SCOPUS:84904738647
SN - 0099-1112
VL - 80
SP - 785
EP - 795
JO - Photogrammetric Engineering and Remote Sensing
JF - Photogrammetric Engineering and Remote Sensing
IS - 8
ER -