用超分辨率引导多任务学习，提升高光谱解混精度
SMILE: A Super-resolution Guided Multi-task Learning Method for Hyperspectral Unmixing
- 通过理论分析证明超分辨率与解混任务的正向关联性
- 共享与特定表示联合学习，实现信息有效迁移
- 保障解混收敛性，适合遥感图像精细分析场景
高光谱解混性能常受低空间分辨率限制，可通过多任务学习结合超分辨率来提升。然而，直接融合超分辨率与解混面临两大挑战：任务亲和性未验证，且解混收敛性无法保证。为此，本文提出超分辨率引导的多任务学习方法（SMILE），并提供理论分析。理论证明了多任务学习的可行性，验证了任务间的正向引导关系（包含关系定理与存在定理）。所提框架通过学习共享与特定表示，将超分辨率中的正向信息迁移至解混过程。此外，通过可访问性定理证明解混最优解的存在性，确保收敛。实验在合成与真实数据集上均验证了方法有效性。
原文摘要 · Abstract (English)
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.
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