Radial grid reflectance correction for hyperspectral images of fruits with rounded surfaces

Over the last two decades, the viability of hyperspectral imaging (HSI) technology as a tool for determining food quality has been established. This technology has been successfully combined with chemometric tools and image-processing techniques to enhance its effectiveness. Despite the advances in imaging technology, significant challenges related to non-uniformity caused by object geometry still need to be addressed. The inappropriate processing of non-uniform food products can significantly affect subsequent calculations, including accurate measurements and precise analysis. A popular method used to face up non-uniformity is the Lambertian surface correction, which assumes spherical surfaces of food products; however, it presents some issues with non-spherical shapes. This work proposes a generalized reflectance correction method for HSI of rounded fruits that are not necessarily spherical. The method is based on the correlation between the pixel's position and its reflectance. For proof of concept, a case study was designed considering the HSI of mango fruits (Mangifera indica L) Kent variety. For comparison, each image was corrected using both the Lambertian surface correction and the proposed method. Results show a strong correlation between the standard deviation of the spectral profiles of images and the roundness of the fruit, which is more evident in the case of the proposed radial grid correction method. Hence, the proposed radial correction method produced a more uniform texture in corrected hyperspectral images.