Analysis of the radiometric response of orange tree crown in hyperspectral uav images - Imai, N. N. [UNESP]; Moriya, E. A.S. [UNESP]; Honkavaara, E.; Miyoshi, G. T. [UNESP]; De Moraes, M. V.A. [UNESP]; Tommaselli, A. M.G. [UNESP]; Näsi, R. (Copernicus Publications, 2017)
High spatial resolution remote sensing images acquired by drones are highly relevant data source in many applications. However, strong variations of radiometric values are difficult to correct in hyperspectral images. Honkavaara et al. (2013) presented a radiometric block adjustment method in which hyperspectral images taken from remotely piloted aerial systems - RPAS were processed both geometrically and radiometrically to produce a georeferenced mosaic in which the standard Reflectance Factor for the nadir is represented. The plants crowns in permanent cultivation show complex variations since the density of shadows and the irradiance of the surface vary due to the geometry of illumination and the geometry of the arrangement of branches and leaves. An evaluation of the radiometric quality of the mosaic of an orange plantation produced using images captured by a hyperspectral imager based on a tunable Fabry-Pérot interferometer and applying the radiometric block adjustment method, was performed. A high-resolution UAV based hyperspectral survey was carried out in an orange-producing farm located in Santa Cruz do Rio Pardo, state of São Paulo, Brazil. A set of 25 narrow spectral bands with 2.5 cm of GSD images were acquired. Trend analysis was applied to the values of a sample of transects extracted from plants appearing in the mosaic. The results of these trend analysis on the pixels distributed along transects on orange tree crown showed the reflectance factor presented a slightly trend, but the coefficients of the polynomials are very small, so the quality of mosaic is good enough for many applications.
Keywords: Bidirectional reflectance distribution function (BRDF); High spectral; Hyperspectral image; Radiometric calibration; Spatial resolution remote sensing; Trend analysis; UAV