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    This study introduces a novel hyperspectral (HS) pansharpening method (HPDP) that fuses low-resolution HS images with high-resolution panchromatic (PAN) images. The method enhances spatial and spectral quality, offering superior performance for diverse datasets.

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    Area of Science:

    • Remote Sensing
    • Image Processing
    • Computer Vision

    Background:

    • Hyperspectral (HS) imaging offers rich spectral information but often suffers from low spatial resolution due to technical and budget limitations.
    • Existing pansharpening methods struggle to effectively fuse HS and Panchromatic (PAN) data while preserving both spectral fidelity and spatial detail.
    • Deep learning approaches for HS pansharpening face challenges related to sensor variability and limited training data.

    Purpose of the Study:

    • To propose a novel hyperspectral (HS) pansharpening method named HPDP (Hyperspectral Pansharpening with Deep Priors).
    • To improve the fusion of low-resolution (LR) HS images with high-resolution (HR) PAN images, enhancing both spatial and spectral quality.
    • To develop a robust deep learning framework (HFNet) that overcomes data set sensitivity and training sample limitations.

    Main Methods:

    • Redefinition of the spectral response function (SRF) using the larger eigenvalue of the structure tensor (ST) matrix.
    • Introduction of HFNet for capturing high-frequency residual mapping between upsampled HS and PAN images.
    • Integration of deep priors derived from structural transformation into a Sylvester equation for HR HS image estimation.

    Main Results:

    • The proposed HPDP method demonstrates superior pansharpening performance across various datasets.
    • HPDP ensures higher quality in both spatial and spectral domains compared to existing methods.
    • HFNet, trained in the high-frequency domain using multispectral (MS) images, shows reduced sensitivity to different sensor data.

    Conclusions:

    • The HPDP method effectively fuses LR HS and HR PAN images, yielding significant improvements in image quality.
    • The novel SRF definition and HFNet architecture contribute to the enhanced performance and robustness of the proposed method.
    • The approach addresses key limitations in deep learning-based HS pansharpening, paving the way for more reliable applications.