PCA-CNN Hybrid Approach for Hyperspectral Pansharpening

This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks from multispectral to hyperspectral pansharpening. Thus, it focuses on the fusion of a single high-resolution panchromatic band with a low-resolution hyperspectral data cube. This is achieved by means of a decorrelation transform, following the principal component analysis approach, which enables the compression of a significant portion of the hyperspectral image energy into a few bands. Afterwards, a suitably adapted pansharpening network designed for four spectral bands is used to super-resolve only the principal components. Experiments demonstrate high performance in both quantitative and qualitative evaluations, favorably comparing against state-of-the-art methods.