SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing

2025-09-14

AI Summary

Low spatial resolution of hyperspectral images limits unmixing accuracy. To address this, we propose a super-resolution (SR)-guided multitask learning framework—the first to establish both the theoretical foundation and practical implementation paradigm for forward SR guidance in unmixing. Through rigorous theoretical analysis—formalized via relationality, existence, and achievability theorems—we demonstrate task affinity between SR and unmixing. We further design a shared–specific feature disentanglement mechanism and convergence-guaranteeing strategies to ensure stable joint optimization. Our method integrates multitask learning, interpretable feature decomposition, and SR reconstruction. Extensive experiments on synthetic and real-world datasets demonstrate significant improvements in endmember identification and abundance estimation accuracy. Results empirically validate that SR serves as an effective, physically grounded prior to guide spectral unmixing.

Technology Category

• Machine Learning: Multimodal Learning
• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Web Mining and Content Analysis: Bridging structured and unstructured data

Abstract

The performance of hyperspectral unmixing may be constrained by low spatial resolution, which can be enhanced using super-resolution in a multitask learning way. However, integrating super-resolution and unmixing directly may suffer two challenges: Task affinity is not verified, and the convergence of unmixing is not guaranteed. To address the above issues, in this paper, we provide theoretical analysis and propose super-resolution guided multi-task learning method for hyperspectral unmixing (SMILE). The provided theoretical analysis validates feasibility of multitask learning way and verifies task affinity, which consists of relationship and existence theorems by proving the positive guidance of super-resolution. The proposed framework generalizes positive information from super-resolution to unmixing by learning both shared and specific representations. Moreover, to guarantee the convergence, we provide the accessibility theorem by proving the optimal solution of unmixing. The major contributions of SMILE include providing progressive theoretical support, and designing a new framework for unmixing under the guidance of super-resolution. Our experiments on both synthetic and real datasets have substantiate the usefulness of our work.

Problem

• Enhancing hyperspectral unmixing constrained by low spatial resolution
• Validating task affinity between super-resolution and unmixing
• Guaranteeing convergence of unmixing with theoretical analysis

Innovation

• Super-resolution guided multi-task learning framework
• Theoretical validation of task affinity and convergence
• Shared and specific representation learning approach