Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism
This addresses the need for adaptable models in remote sensing and imaging applications, representing a novel method rather than an incremental improvement.
The paper tackles the problem of limited transferability in multispectral and hyperspectral image fusion models by proposing a universal framework with spectral-band and fusion-scale agnosticism, achieving state-of-the-art performance and generalization to unseen sensors and scales.
Current deep learning models for Multispectral and Hyperspectral Image Fusion (MS/HS fusion) are typically designed for fixed spectral bands and spatial scales, which limits their transferability across diverse sensors. To address this, we propose SSA, a universal framework for MS/HS fusion with spectral-band and fusion-scale agnosticism. Specifically, we introduce Matryoshka Kernel (MK), a novel operator that enables a single model to adapt to arbitrary numbers of spectral channels. Meanwhile, we build SSA upon an Implicit Neural Representation (INR) backbone that models the HS signal as a continuous function, enabling reconstruction at arbitrary spatial resolutions. Together, these two forms of agnosticism enable a single MS/HS fusion model that generalizes effectively to unseen sensors and spatial scales. Extensive experiments demonstrate that our single model achieves state-of-the-art performance while generalizing well to unseen sensors and scales, paving the way toward future HS foundation models.