Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism

2026-02-02

AI Summary

This work proposes SSA, a universal framework for hyperspectral image fusion that overcomes the limitations of existing deep learning methods, which are typically constrained by fixed spectral bands and spatial scales and thus struggle to generalize across different sensors. SSA is the first model to simultaneously achieve spectral band agnosticism and fusion scale invariance. It integrates a Matryoshka Kernel with an Implicit Neural Representation (INR) backbone, enabling adaptive processing of arbitrary numbers of spectral channels and spatial resolutions through continuous signal modeling. Experimental results demonstrate that a single SSA model achieves state-of-the-art performance across multiple datasets and exhibits exceptional generalization capabilities on unseen sensors and scales.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Computer Vision: Representation Learning for Vision
• Machine Learning: Multimodal Learning

Application Category

• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

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.

Problem

• hyperspectral image fusion
• spectral-band agnosticism
• fusion-scale agnosticism
• transferability
• sensor generalization

Innovation

• spectral-band agnosticism
• fusion-scale agnosticism
• Matryoshka Kernel
• Implicit Neural Representation
• hyperspectral image fusion