光谱成像技术研究室知识库

In recent years, the commercialized low-cost rotor UAV equipped with small hyperspectral camera has become an emerging way to acquire hyperspectral remote sensing data due to its advantages of convenient data acquisition and low cost, and has been widely used in environmental monitoring, precision agriculture, ocean development and other fields. Due to its light weight, the flight process of the rotor UAV is vulnerable to the influence of air flow, which leads to the poor stability of the platform and resulting in the geometric distortion of the imaging. This situation is particularly prominent in the obvious air flow areas such as the lake and the sea, which restricts the application in related fields. In this paper, the geometric processing of UAV-borne hyperspectral measured data collected at a wharf in Qiandao Lake is studied. There are two major factors lead to geometric error. One is the longitude and latitude step error, the other is the frame frequency of the navigation data is lower than the image exposure frame frequency. In this situation, this paper proposes a step fitting method to perform geometric correction and error correction for the hyperspectral airstrip data. Compared with the traditional Kalman filtering method and the global linear fitting method, the proposed method can better correct the geometric distortion of hyperspectral image caused by the low-precision GPS/INS system, which provides a foundation for the subsequent quantitative application of lightweight UAV hyperspectral camera in various fields. © 2021 SPIE