Fusion of multispectral imagery and spectrometer data in UAV remote sensing

摘要: Abstract: High spatial resolution hyperspectral data often used in precision farming applications are not available from current satellite sensors, and difficult or expensive to acquire standard aircraft. Alternatively, farming, unmanned aerial vehicles (UAVs) emerging as lower cost more flexible means very high imagery. Miniaturized sensors have been developed for UAVs, but the associated hardware, processing software still prohibitive use by individual farmers small remote sensing firms. This study simulated image fusing multispectral camera imagery spectrometer data. We mounted a spectrometer, both being low weight, on UAV procedures their precise alignment, followed fusion of with produce estimated spectra all pixels. To align collected two time space domains, post-acquisition correlation-based global optimization method was used. Data fusion, estimate reflectance, implemented using several methods comparison. Flight crop sites, one tomatoes, other corn soybeans, were evaluate alignment procedure results. The resulted peak R2 between 0.95 0.72, respectively, test sites. corresponding these offsets taken best match given reading, modelling pixel Of approaches evaluated, principal component analysis (PCA) based models Bayesian imputation reached similar accuracy, outperformed simple spline interpolation. Mean absolute error (MAE) predicted observed 17% relative mean spectra, root squared (RMSE) 0.028. approach deriving can be applied fashion at assessment monitoring within fields.