Contributo in atti di convegno, 2023, ENG, 10.1109/IGARSS52108.2023.10282928

An Unsupervised CNN-Based Hyperspectral Pansharpening Method

G. Guarino, M. Ciotola, Vivone G, G. Poggi, G. Scarpa

a. University Federico II, (I), Naples, 80125, Italy b. National Research Council-IMAA, (I), Tito, 85050, Italy c. University Parthenope, (I), Naples, 80133, Italy

This work proposes a simple yet effective method to adapt unsupervised convolutional neural networks for pansharpening of multispectral images to the problem of hyperspectral image pansharpening, i.e., the fusion of a single high-resolution panchromatic band with a low-resolution hyperspectral data cube. This is achieved by means of a PCA transformation which allows to compact the most of the HS image energy in a few bands, which are then suitably super-resolved using a pansharpening network designed for few spectral bands. Our experiments show very encouraging results which compare favorably against the state-of-the-art methods.

IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2023, pp. 5982–5985, Pasadena, CA, USA, 16 July 2023through 21 July 2023

Keywords

convolutional neural network, data fusion, hyperspectral image, pansharpening, Super-resolution

CNR authors

Vivone Gemine

CNR institutes

IMAA – Istituto di metodologie per l'analisi ambientale