PCA-CNN Hybrid Approach for Hyperspectral Pansharpening

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