Name
Title | ||
|---|---|---|
Site-Specific Plant Condition Monitoring Through Hyperspectral Alternating Least Squares Unmixing |
Abstract | ||
|---|---|---|
Alternating least squares (ALS) is a blind source separation method commonly used in chemometrics to simultaneously estimate the absorption spectrum and concentration of different components in a chemical sample. In this study, the transferability of ALS from chemometrics to agricultural remote sensing is evaluated. Due to the subpixel contribution of background components, spectral unmixing has become an indispensable processing step in the spectral analysis of agricultural areas. Yet, traditional unmixing techniques only allow estimating the subpixel cover distribution of different components, but fail to provide an estimate of pure spectral signature of the crop component. This info is, however, highly valuable, as this pure crop signature could be used to monitor the health status of trees. Here, we anticipate that ALS can provide a solution. ALS estimates both the concentration and the absorption spectra of different components in a chemical sample and this can easily be translated into estimating both the subpixel cover fraction and spectral signature of different components in a mixed image pixel. We tested the performance of ALS on binary synthetic mixtures of citrus canopy and soil spectra, as well as on a ray-tracing experiment of a virtual orchard. ALS indeed allowed to simultaneously estimate the subpixel cover distribution (RMSE=0.05), as well as the pure spectral signatures of different endmembers (RRMSE<;0.12), and considerably improved the extraction of biophysical parameters (Δ R2 up to 0.43). Thus, ALS provides a promising new image analysis tool for agricultural remote sensing. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/JSTARS.2014.2313984 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
feature extraction,geophysical image processing,remote sensing,vegetation,als transferability,absorption spectra,absorption spectrum,agricultural areas,agricultural remote sensing,binary synthetic mixtures,blind source separation method,citrus canopy,crop component,hyperspectral alternating least squares unmixing,image analysis tool,mixed image pixel,ofbiophysical parameter extraction,site-speciflc plant condition monitoring,soil spectra,subpixel cover distribution,subpixel cover fraction,tree health status,virtual orchard ray-tracing experiment,alternating least squares (als),chlorophyll,citrus orchards,hyperspectral,leaf area index,multivariate curve resolution (mcr),precision farming,spectral mixture analysis (sma),virtual reality,water | Remote sensing,Hyperspectral imaging,Condition monitoring,Alternating least squares,Mathematics | Journal |
Volume | Issue | ISSN |
7 | 8 | 1939-1404 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Laurent Tits | 1 | 57 | 10.49 |
| Ben Somers | 2 | 269 | 30.34 |
| Wouter Saeys | 3 | 78 | 11.04 |
| Pol Coppin | 4 | 147 | 20.87 |