Selection of suitable fusional band combination from Sentinel-2A and UAV imagery for agricultural applications

Abstract

ABSTRACT This study explores the fusion of Sentinel-2A (S2) and different bands of UAV imagery (Blue, Green, Red and Panchromatic (PAN)) to enhance spatial and spectral information for crop health monitoring. It assesses the quality of fused images, accuracy of the classified fused images and statistical analysis between the fused and S2 vegetation indices. Various fusion techniques are examined to select an optimal combination of bands. The quality of the fused images is evaluated using image quality assessment metrics and classification employs the Random Forest algorithm, assessing accuracy with metrics like F-score and Kappa Coefficient. Statistical analysis involves comparing vegetation indices from fused and S2 imagery. Notably, the fusion of UAV Green and Red bands with S2 imagery, using BT and PCA techniques, emerges as an effective combination for plant-level agricultural health monitoring. This research contributes to advancing precision agriculture techniques by leveraging multispectral imaging fusion for enhanced crop monitoring and management.