Abstract

Unmanned airborne vehicles (UAV) equipped with novel, miniaturized, 2D frame format hyper- and multispectral cameras make it possible to conduct remote sensing measurements cost-efficiently, with greater accuracy and detail. In the mapping process, the area of interest is covered by multiple, overlapping, small-format 2D images, which provide redundant information about the object. Radiometric correction of spectral image data is important for eliminating any external disturbance from the captured data. Corrections should include sensor, atmosphere and view/illumination geometry (bidirectional reflectance distribution function—BRDF) related disturbances. An additional complication is that UAV remote sensing campaigns are often carried out under difficult conditions, with varying illumination conditions and cloudiness. We have developed a global optimization approach for the radiometric correction of UAV image blocks, a radiometric block adjustment. The objective of this study was to implement and assess a combined adjustment approach, including comprehensive consideration of weighting of various observations. An empirical study was carried out using imagery captured using a hyperspectral 2D frame format camera of winter wheat crops. The dataset included four separate flights captured during a 2.5 h time period under sunny weather conditions. As outputs, we calculated orthophoto mosaics using the most nadir images and sampled multiple-view hyperspectral spectra for vegetation sample points utilizing multiple images in the dataset. The method provided an automated tool for radiometric correction, compensating for efficiently radiometric disturbances in the images. The global homogeneity factor improved from 12–16% to 4–6% with the corrections, and a reduction in disturbances could be observed in the spectra of the object points sampled from multiple overlapping images. Residuals in the grey and white reflectance panels were less than 5% of the reflectance for most of the spectral bands.

Highlights

  • Unmanned Aerial Vehicles (UAVs, drones) equipped with miniaturized multi- and hyperspectral imaging sensors offer completely new possibilities for carrying out close-range remote sensing tasks [1]

  • We have developed a radiometric block adjustment-based approach to carry out radiometric correction and to provide homogeneous reflectance data from 2D frame format hyperspectral drone image datasets [12,32]

  • To assess the impact of calibration model we considered the image mosaics

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Summary

Introduction

Unmanned Aerial Vehicles (UAVs, drones) equipped with miniaturized multi- and hyperspectral imaging sensors offer completely new possibilities for carrying out close-range remote sensing tasks [1]. Using these technologies, spectral remote sensing measurements can be made cost-efficiently, with greater accuracy and detail than ever before. Several pushbroom hyperspectral imaging sensors [2,3,4,5,6] and point spectrometers [7,8] have recently been implemented in UAVs. Hyperspectral cameras based on the 2D frame format sensors have entered the market in recent years, offering an interesting alternative for hyperspectral data capture [9,10,11,12,13,14,15]. Available sensors and products include for example the Rikola Hyperspectral Camera [16] and the Cubert UHD 185-Firefly [17].

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