Real-Time Hyperspectral and Depth Fusion Calibration Method for Improved Reflectance Measures on Arbitrary Complex Surfaces

In the field of hyperspectral imaging, accurate and reliable data analysis is essential for many applications, including medicine, remote sensing, and material science. White calibration is a critical step in this process, as it accounts for deviations in the light source intensity and spectral distribution. Nevertheless, it is important to note that the specific geometry of the scene plays a crucial role in the white calibration, affecting the angle of incidence of the light and, subsequently, the measured spectral response. By taking surface normals and depth information into account during white calibration, we can ensure that subsequent hyperspectral images are accurately calibrated and comparable, leading to more robust and meaningful data analysis. For that matter, in this paper, we demonstrate a methodology to fuse hyperspectral and depth information, and how this fusion can help to correct different geometrical properties of the analyzed sample. A series of laboratory experiments were conducted on samples with different geometries and surface properties. Specifically, the error dispersion of the spectral signatures was reduced from 12 % to less than 4 %, a substantial improvement that highlights the potential of the proposed method.