Robust Hyperspectral Image Panshapring via Sparse Spatial-Spectral Representation

TL;DR

This paper presents S$^{3}$RNet, a novel hyperspectral image pansharpening framework that combines sparse spatial-spectral representation with multi-branch fusion and attention mechanisms to improve reconstruction quality.

Contribution

The paper introduces S$^{3}$RNet, featuring a multi-branch fusion network with attention and dense aggregation blocks for enhanced hyperspectral image pansharpening.

Findings

Achieves state-of-the-art performance on multiple metrics.

Effectively suppresses noise and redundancy.

Maintains high reconstruction quality under challenging conditions.

Abstract

High-resolution hyperspectral imaging plays a crucial role in various remote sensing applications, yet its acquisition often faces fundamental limitations due to hardware constraints. This paper introduces S$^{3}$RNet, a novel framework for hyperspectral image pansharpening that effectively combines low-resolution hyperspectral images (LRHSI) with high-resolution multispectral images (HRMSI) through sparse spatial-spectral representation. The core of S$^{3}$RNet is the Multi-Branch Fusion Network (MBFN), which employs parallel branches to capture complementary features at different spatial and spectral scales. Unlike traditional approaches that treat all features equally, our Spatial-Spectral Attention Weight Block (SSAWB) dynamically adjusts feature weights to maintain sparse representation while suppressing noise and redundancy. To enhance feature propagation, we incorporate the Dense Feature Aggregation Block (DFAB), which efficiently aggregates inputted features through dense connectivity patterns. This integrated design enables S$^{3}$RNet to selectively emphasize the most informative features from differnt scale while maintaining computational efficiency. Comprehensive experiments demonstrate that S$^{3}$RNet achieves state-of-the-art performance across multiple evaluation metrics, showing particular strength in maintaining high reconstruction quality even under challenging noise conditions. The code will be made publicly available.