3D-CNNHSR: A 3-Dimensional Convolutional Neural Network for Hyperspectral Super-Resolution

Hyperspectral images can easily discriminate different materials due to their fine spectral resolution. However, obtaining a hyperspectral image (HSI) with a high spatial resolution is still a challenge as we are limited by the high computing requirements. The spatial resolution of HSI can be enhanced by utilizing Deep Learning (DL) based Super-resolution (SR). A 3D-CNNHSR model is developed in the present investigation for 3D spatial super-resolution for HSI, without losing the spectral content. The 3D-CNNHSR model was tested for the Hyperion HSI. The pre-processing of the HSI was done before applying the SR model so that the full advantage of hyperspectral data can be utilized with minimizing the errors. The key innovation of the present investigation is that it used 3D convolution as it simultaneously applies convolution in both the spatial and spectral dimensions and captures spatial-spectral features. By clustering contiguous spectral content together, a cube is formed and by convolving the cube with the 3D kernel a 3D convolution is realized. The 3D-CNNHSR model was compared with a 2D-CNN model, additionally, the assessment was based on higher-resolution data from the Sentinel-2 satellite. Based on the evaluation metrics it was observed that the 3D-CNNHSR model yields better results for the SR of HSI with efficient computational speed, which is significantly less than previous studies.