Adaptive Local Implicit Image Function For Arbitrary-Scale Super-Resolution

in Cultural Heritage, hyperspectral images are commonly used since they provide extended information regarding the optical properties of materials. Thus, the processing of such high-dimensional data becomes challenging from the perspective of machine learning techniques to be applied. in this paper, we propose a Rank-R tensor-based learning model to identify and classify material defects on Cultural Heritage monuments. in contrast to conventional deep learning approaches, the proposed high order tensor-based learning demonstrates greater accuracy. It also provides interpretation on how each spectral band contributes to the material classification. Experimental results on real world data of UNESCO protected areas indicate the superiority of the proposed scheme than conventional deep learning models.