End-to-end simulation model of rover-based hyperspectral remote-sensing systems: application to VNIS

The simulation of remote-sensing hyperspectral images has various applications such as the design of future hyperspectral imaging systems, understanding of the image formation process, development and validation of data processing algorithms, and optimization of the instrument imaging mode. For incomplete understanding of the lunar surface and the wide environmental differences between earth and moon observation, imaging systems for lunar observation cannot be tested in their exact working environments before launch. In these cases, simulation of lunar hyperspectral images can be used as a powerful tool to analyse the imaging process on the moon. The VIS–NIR imaging spectrometer (VNIS) aboard the Chang’e-3 (CE-3) lunar rover is used to perform in situ mineral detection on the lunar surface, but the rover-based VNIS has some additional effects from the rover itself (e.g. the shadow caused by the rover). In this paper, a rover-based radiative transfer model has been developed, and the simulation model is able to generate realistic VNIS-like data in an automatic way under a set of user-driven instrument and illumination parameters. A realistic surface reflectance cube, as the original input for the simulation model, is provided by the interference imaging spectrometer (IIM) data of Chang’e-1 (CE-1). Several hyperspectral simulations in different illumination and observation geometries have been conducted to analyse the effects of shadow, specular irradiance, and diffuse irradiance on the imaging data. For certain illumination geometries, the simulation model can forecast image quality in different observation geometries; the model can also determine the optimal observation azimuth ranges at different solar elevation angles. Moreover, the simulation model can be used to provide test images for the rover effect elimination algorithms. These applications of the model can facilitate understanding by analysing the rover-based hyperspectral remote-sensing process and eventually obtaining high-quality images of the lunar surface.