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

• URL: http://arxiv.org/abs/2602.01681v1
• Date: Mon, 02 Feb 2026 05:48:53 GMT
• Title: Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism
• Authors: Yu-Jie Liang, Zihan Cao, Liang-Jian Deng, Yang Yang, Malu Zhang,
• Abstract summary: Current deep learning models for Multispectral and Hyperspectral Image Fusion (MS/HS fusion) are typically designed for fixed spectral bands and spatial scales.<n>We propose SSA, a universal framework for MS/HS fusion with spectral-band and fusion-scale agnosticism.<n>Our single model achieves state-of-the-art performance while generalizing well to unseen sensors and scales.
• Score: 42.31159916095528
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Universal Pansharpening Foundation Model [67.10467574892282]
  Pansharpening generates the high-resolution multi-spectral (MS) image by integrating spatial details from a texture-rich panchromatic (PAN) image and spectral attributes from a low-resolution MS image.<n>We present FoundPS, a universal pansharpening foundation model for satellite-agnostic and scene-robust fusion.
  arXiv  Detail & Related papers  (2026-03-04T08:30:15Z)
• Any-Optical-Model: A Universal Foundation Model for Optical Remote Sensing [24.03278912134978]
  We propose Any Optical Model (AOM) to accommodate arbitrary band compositions, sensor types, and resolution scales.<n>AOM consistently achieves state-of-the-art (SOTA) performance under challenging conditions such as band missing, cross sensor, and cross resolution settings.
  arXiv  Detail & Related papers  (2025-12-19T04:21:01Z)
• A Fusion-Guided Inception Network for Hyperspectral Image Super-Resolution [4.487807378174191]
  We propose a single-image super-resolution model called the Fusion-Guided Inception Network (FGIN)<n>Specifically, we first employ a spectral-spatial fusion module to effectively integrate spectral and spatial information.<n>An Inception-like hierarchical feature extraction strategy is used to capture multiscale spatial dependencies.<n>To further enhance reconstruction quality, we incorporate an optimized upsampling module that combines bilinear with depthwise separable convolutions.
  arXiv  Detail & Related papers  (2025-05-06T11:15:59Z)
• CARL: Camera-Agnostic Representation Learning for Spectral Image Analysis [69.02751635551724]
  Spectral imaging offers promising applications across diverse domains, including medicine and urban scene understanding.<n> variability in channel dimensionality and captured wavelengths among spectral cameras impede the development of AI-driven methodologies.<n>We introduce CARL, a model for Camera-Agnostic Representation Learning across RGB, multispectral, and hyperspectral imaging modalities.
  arXiv  Detail & Related papers  (2025-04-27T13:06:40Z)
• Unleashing Correlation and Continuity for Hyperspectral Reconstruction from RGB Images [64.80875911446937]
  We propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images.<n>For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module.<n>For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module.
  arXiv  Detail & Related papers  (2025-01-02T15:14:40Z)
• HyperSIGMA: Hyperspectral Intelligence Comprehension Foundation Model [88.13261547704444]
  Hyper SIGMA is a vision transformer-based foundation model that unifies HSI interpretation across tasks and scenes.<n>In addition, we construct a large-scale hyperspectral dataset, HyperGlobal-450K, for pre-training, which contains about 450K hyperspectral images.
  arXiv  Detail & Related papers  (2024-06-17T13:22:58Z)
• SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening [14.293042131263924]
  We introduce a spatial-spectral integrated diffusion model for the remote sensing pansharpening task, called SSDiff. SSDiff considers the pansharpening process as the fusion process of spatial and spectral components from the perspective of subspace decomposition.
  arXiv  Detail & Related papers  (2024-04-17T16:30:56Z)
• Physics-Inspired Degradation Models for Hyperspectral Image Fusion [61.743696362028246]
  Most fusion methods solely focus on the fusion algorithm itself and overlook the degradation models. We propose physics-inspired degradation models (PIDM) to model the degradation of LR-HSI and HR-MSI. Our proposed PIDM can boost the fusion performance of existing fusion methods in practical scenarios.
  arXiv  Detail & Related papers  (2024-02-04T09:07:28Z)
• SSIF: Learning Continuous Image Representation for Spatial-Spectral Super-Resolution [73.46167948298041]
  We propose a neural implicit model that represents an image as a function of both continuous pixel coordinates in the spatial domain and continuous wavelengths in the spectral domain. We show that SSIF generalizes well to both unseen spatial resolutions and spectral resolutions. It can generate high-resolution images that improve the performance of downstream tasks by 1.7%-7%.
  arXiv  Detail & Related papers  (2023-09-30T15:23:30Z)
• Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency [21.233354336608205]
  We propose an unsupervised HSI and MSI fusion model based on the cycle consistency, called CycFusion. The CycFusion learns the domain transformation between low spatial resolution HSI (LrHSI) and high spatial resolution MSI (HrMSI) Experiments conducted on several datasets show that our proposed model outperforms all compared unsupervised fusion methods.
  arXiv  Detail & Related papers  (2023-07-07T06:47:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.