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

Author:

Wang Zhuo12,Li Haiwei1ORCID,Wang Shuang13,Song Liyao4,Chen Junyu1

Affiliation:

1. Key Laboratory of Spectral Imaging Technology of CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an 710119, China

2. University of Chinese Academy of Sciences, Beijing 100049, China

3. Shaanxi Key Laboratory of Optical Remote Sensing and Intelligent Information Processing, Xi’an 710100, China

4. Institute of Artificial Intelligence and Data Science, Xi’an Technological University, Xi’an 710021, China

Abstract

The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV push-broom hyperspectral imaging is limited by the push-broom imaging method, and the multi-angle information is often difficult to obtain. In addition, the random variation of solar illumination during UAV low-altitude flight makes the irradiance between different push-broom hyperspectral rows and different airstrips inconsistent, which significantly affects the radiometric consistency of BRDF modeling and results in the difficulty of accurately portraying the three-dimensional spatial reflectance distribution in the UAV model. These problems largely impede the application of outdoor BRDF. Based on this, this paper proposes a fast multi-angle information acquisition scheme with a high-accuracy BRDF modeling method considering illumination variations, which mainly involves a lightweight system for BRDF acquisition and three improved BRDF models considering illumination corrections. We adopt multi-rectangular nested flight paths for multi-gray level targets, use multi-mode equipment to acquire spatial illumination changes and multi-angle reflectivity information in real-time, and introduce the illumination correction factor K through data coupling to improve the kernel, Hapke, and RPV models, and, overall, the accuracy of the improved model is increased by 20.83%, 11.11%, and 31.48%, respectively. The results show that our proposed method can acquire multi-angle information quickly and accurately using push-broom hyperspectral imaging, and the improved model eliminates the negative effect of illumination on BRDF modeling. This work is vital for expanding the multi-angle information acquisition pathway and high-efficiency and high-precision outdoor BRDF modeling.

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Publisher

MDPI AG

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