Initial Performance Analysis of the At-Altitude Radiance Ratio Method for Reflectance Conversion of Hyperspectral Remote Sensing Data

Abstract

In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research.

A Scale Conversion Model Based on Deep Learning of UAV Images

“…However, measuring the reflectance of each continuous element in the field measurement is impossible, and the efficiency could be better [41]. Therefore, only the reflectance Re f (n k ) of k sampling points inside the sample area N is usually measured, and the image-scale surface reflectance of the sample area is obtained by establishing a functional relationship F between the reflectance of the sampling points and the reflectance Re f (N) of the sample area N, as shown in Equation (1). The panchromatic images obtained from UAV capture can simultaneously measure the grayscale values of each consecutive element within the sample area N. Therefore, the complex nonlinear correlation between all the sampling points in the sample area N and the sample area N is established by using the UAV images in the sample area, all the UAV images in the sampling points, and the average grey value…”

“…Scale conversion is a critical link in many remote sensing physical modelling, remote sensing product applications and quantitative description of surface parameters at the pixel scale [1][2][3][4]. The primary problem in remote sensing scale conversion is effectively converting remote sensing data and information from one scale to another and simultaneously giving the evaluation index and uncertainty of the scale conversion results [2,5,6].…”

Small Target Radiometric Performance of Drone-Based Hyperspectral Imaging Systems

“…For drone-based imaging platforms, radiometric calibration is accomplished using Lambertian targets (i.e., Empirical Line Method (ELM) [1,2]) or a physics-based approach (i.e., At-Attitude Radiance Ratio (AARR) [3,4]). Both techniques have the goal of compensating for and calibrating remote sensing imagery to surface reflectance.…”

“…A physics-based approach to reflectance calibration uses remote sensing principles to transform imagery from entrance aperture-reaching spectral radiance to surface reflectance without in-scene calibration targets. The AARR technique uses the fundamental assumption that all objects in the scene are Lambertian reflectors such that the instantaneous measurements of the downwelling spectral irradiance (with a spectroradiometer) and object spectral radiance (with HSI system) can be used to estimate surface reflectance [4]. An advantage of this technique is the ability to correct hyperspectral (HS) imagery without in-scene calibration targets.…”