Data-Driven and Model-Driven Spectral Superresolution Algorithms: Combination, Analysis and Application for Classification

In this paper, five spectral superresolution (SSR) algorithms are compared to verify the availability of SSR results as input data in classification. To enhance the spectral resolution, SSR algorithms are proposed to increase the channel number of multispectral images, which can be divided into model-driven and data-driven methods. To combine the advantage of these two types of algorithms, we proposed an optimization-inspired convolutional neural network (OCNN) by unfolding a traditional variational model. The proposed method combines data-driven training with model-driven optimization together to enhance the spectral resolution of high-resolution (HR) multispectral images (MSIs) to obtain HR hyperspectral images (HSIs). Experiments in both SSR and classification are made to show the proposed method is of efficiency and superiority.