Radiometric Correction of Close-Range Spectral Image Blocks Captured Using an Unmanned Aerial Vehicle with a Radiometric Block Adjustment

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.

Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment

“…Based on the 'rhythmic' canopy expansion pattern of lychee trees compared to the higher trunk-branch differentiation of avocado trees [31,32], it is likely that the different types of canopy geometric characteristic had different influences on the spectral reflectance that observed by the sensor. Studies on the radiometric correction for multi-spectral UAS imagery show that complex canopy structure produce more diffuse radiant flux within the canopy [41,58]. Therefore, we suspect that the observed reflectance of avocado trees was less representative of the actual reflected radiant energy due to the aforementioned reason, causing the lower correlation between the observed reflectance and PPC.…”

“…The R 2 values of the PPC regression with vegetation indices were all higher after reducing the brightness variation with colour balancing (Figure 9). However, to radiometrically correct the image brightness to accurate reflectance, previous result showed that effective BRDF correction, accurate sensor-information-based calibration, and simultaneous irradiance measurement are all potential procedures that can be considered [41,58]. Based on our study, we suggest that if the image is radiometrically corrected, the multi-variate linear regression of mean, standard deviation, and four Haralick texture GLCMs of NDVI could be a strong indicator of PPC over NIR brightness, as it provided the highest correlation with field-derived PPC.…”

“…Based on our study, we suggest that if the image is radiometrically corrected, the multi-variate linear regression of mean, standard deviation, and four Haralick texture GLCMs of NDVI could be a strong indicator of PPC over NIR brightness, as it provided the highest correlation with field-derived PPC. Nevertheless, radiometric correction of UAS imagery has proven to be a sophisticated process and sometimes difficult for delivering accurate results [41,58,60].…”

Real-Time and Post-Processed Georeferencing for Hyperpspectral Drone Remote Sensing

“…The final products are reflectance orthomosaics for each band. More details of the approach are given by Honkavaara et al, (2013Honkavaara et al, ( , 2018.…”

“…The objective of this study is to evaluate the direct georeferencing solution. The results are compared with post-processing data, carried out based on pipelines developed by Honkavaara et al, 2013Honkavaara et al, , 2018…”