An Integrated Solution of UAV Push-Broom Hyperspectral System Based on Geometric Correction with MSI and Radiation Correction Considering Outdoor Illumination Variation

Abstract

The unmanned aerial vehicle (UAV)-borne hyperspectral imaging system has the advantages of high spatial resolution, flexible operation, under-cloud flying, and easy cooperation with ground synchronous tests. Because this platform often flies under clouds, variations in solar illumination lead to irradiance inconsistency between different rows of hyperspectral images (HSIs). This inconsistency causes errors in radiation correction. In addition, due to the accuracy limitations of the GPS/inertial measurement unit (IMU) and irregular changes in flight platform speed and attitude, HSIs have deformation and drift, which is harmful to the geometric correction and stitching accuracy between flight strips. Consequently, radiation and geometric error limit further applications of large-scale hyperspectral data. To address the above problems, we proposed an integrated solution to acquire and correct UAV-borne hyperspectral images that consist of illumination data acquisition, radiance and geometric correction, HSI, multispectral image (MSI) registration, and multi-strip stitching. We presented an improved three-parameter empirical model based on the illumination correction factor, and it showed that the accuracy of radiation correction considering illumination variation improved, especially in some low signal-to-noise ratio (SNR) bands. In addition, the error of large-scale HSI stitching was controlled within one pixel.

The Uncertainty Assessment by the Monte Carlo Analysis of NDVI Measurements Based on Multispectral UAV Imagery

“…Also, ensuring accurate spectral calibration across different bands is essential, but can be challenging due to variations in sensor characteristics and environmental conditions [9]. Additionally, achieving precise geometric correction to align images with ground truth data or other spatial datasets can be difficult, particularly in dynamic environments with factors such as UAV motion and terrain variations [10,11].…”

Methodology and Modeling of UAV Push-Broom Hyperspectral BRDF Observation Considering Illumination Correction

“…However, there is still a slight error compared to the actual value. Latini et al [12] demonstrated the consistency of the vegetation spectral dimensions by using a UAV to collect vegetation data at 90 m altitude and modeling the comparison of the BRDF with Sentinel satellite data. Still, the number of acquired images was small, and the illumination effect was not considered.…”

“…Still, acquiring images that consider spectral accuracy, spatial resolution, and spectral resolution simultaneously is challenging. Hovering imaging is not possible when using push-broom imaging, and the UAV must be controlled to push-broom along a straight line at a uniform speed to acquire multi-angle information [12]. Push-broom imagers have high signal-to-noise ratios (SNRs) and high spectral resolutions.…”

“…Most existing research assumes the consistency of illumination radiation within a certain period and does not consider the impact of random changes in illumination [1,10,[15][16][17][18][19][20]. However, if all hyperspectral images are corrected with the same atmospheric parameters, there is still a significant error in the model parameters [12]. Currently, there are two main methods to eliminate illumination; one is to eliminate it by synchronized monitoring of equipment [21][22][23], but there is no uniform method for this elimination process.…”