Robust inference against fluctuations in illumination conditions remains a significant challenge in image sensing. Existing works aim to achieve illumination-invariant features by estimating optical properties such as reflectance. However, accurate estimation using RGB images, where the spectral distribution is encoded into three dimensions, is inherently difficult. To address this limitation, approaches leveraging hyperspectral (HS) images, which provide richer wavelength information than RGB images, have been developed for reflectance estimation. Nonetheless, these methods often require the presence of a gray card with known diffuse reflectance within the image. Furthermore, the absence of publicly avail- able datasets combining reflectance values with real RGB or HS images constrains advancements in this field. To overcome these challenges, we propose an intensity-based illumination and reflectance estimation grounded in the Retinex theory. This approach eliminates dependency on gray cards by being guided by objects with known reflectance within the image, enabling illumination- invariant inference across diverse datasets. Additionally, we construct an HS-reflectance dataset and validate its utility. Through experiments, we evaluate our method and analyze its impact on classification using estimated reflectances. The results demonstrate the efficacy of the HS-reflectance dataset, our method, and its potential to enhance illumination robustness in visual systems.