Automated hyperspectral vegetation index retrieval from multiple correlation matrices with hypercor

Helge Aasen, Martin Leon Gnyp, Yuxin Miao, George Bareth

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)785-795
Number of pages11
JournalPhotogrammetric Engineering and Remote Sensing
Volume80
Issue number8
DOIs
StatePublished - Jan 1 2014

Fingerprint Dive into the research topics of 'Automated hyperspectral vegetation index retrieval from multiple correlation matrices with hypercor'. Together they form a unique fingerprint.

Cite this