AMHF-Net: A Multispectral and Hyperspectral Image Fusion Network for Arbitrary-Band Hyperspectral Images

Hyperspectral and multispectral image fusion serves as an effective approach to obtain high-resolution hyperspectral images at low cost. Current deep learning based hyperspectral image fusion methods often focus on fusing images with a fixed number of spectral bands, making them difficult to adapt to remote sensing images with varying spectral bands. To address this limitation, we propose a novel network framework named Arbitrary-band Multispectral and Hyperspectral images Fusion Network (AMHF-Net). Specifically, our method propose an Implicit Spectral Basis Generation block (ISBG) to learn continuous spectral basis vectors that are adaptively sampled according to the wavelength of different bands, thereby generating appropriate spectral basis matrices. We also design an Arbitrary-band Feature Extraction block (AFE) to effectively extract features from hyperspectral images with arbitrary numbers of spectral bands. Furthermore, we develop a Spectral Information Compensation block (SIC) to enhance spectral information acquisition. Extensive experimental results demonstrate that our approach not only achieves comparable performance with state-of-the-art methods with fixed number of bands, but also exhibits superior adaptability to process data with arbitrary numbers and positions of spectral bands.