发表机构
School of Mathematics and Computational Science, Xiangtan University（湘潭大学数学与计算科学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出可解释分阶段高光谱解混框架I-HyperSU，在固定端元先验下通过软丰度松弛和结构化残差细化，显著降低重建误差并保持丰度精度。
AI 中文摘要
高光谱解混从连续光谱观测中将混合像素分解为物质端元及其丰度。在模块化感知流程中，端元通常首先被识别，然后在丰度估计阶段被视为固定值。当这种固定端元先验不准确时，由光照变化、传感器伪影或物质边界引起的空间结构化失配可能被错误地归入丰度变量，导致分解不稳定。本研究提出了一种在固定端元先验下的可解释分阶段高光谱解混框架（I-HyperSU），该框架明确分解为固定端元矩阵 $\mathbf{A}$、丰度块 $\mathbf{X}$ 和结构化残差细化块 $\mathbf{S}$。X块使用FISTA算法进行丰度估计，并施加非负性和稀疏性增强，以及一个近似强制和为一约束的软惩罚。S块联合应用低秩SVD结构化正则化和轻量级深度图像先验（DIP）来细化结构化残差。这种分阶段设计使得丰度与残差分量之间的交互透明且可解释。在Samson、Urban和Jasper Ridge数据集上的实验表明，在固定且不完美的端元先验下，软丰度松弛始终优于硬单纯形投影。在默认的N-FINDR端元先验下，与固定$\mathbf{A}$的UCLS基线相比，所提出的框架将联合重建误差降低了61.7%至69.5%，同时保持丰度RMSE几乎不变，表明残差细化分支解释了结构化模型失配，而不会降低丰度估计质量。例如，在Urban数据集上，重建SAM从仅X模型的$5.99^\circ$降至完整模型的$1.92^\circ$。
英文摘要
Hyperspectral unmixing decomposes mixed pixels into material endmembers and their abundances from contiguous spectral observations. In modular sensing pipelines, endmembers are often first identified and then treated as fixed during abundance estimation. When this fixed endmember prior is inaccurate, spatially structured mismatch arising from illumination changes, sensor artifacts, or material boundaries may be incorrectly captured by the abundance variables, leading to unstable decompositions. This study presents an interpretable stage-wise hyperspectral unmixing framework (I-HyperSU) under fixed endmember priors, which is explicitly decomposed into a fixed endmember matrix $\mathbf{A}$, an abundance block $\mathbf{X}$, and a structural residual refinement block $\mathbf{S}$. The X-block estimates abundances using FISTA with nonnegativity and sparsity enhancement, and a soft penalty that approximately enforces sum-to-one constraints. The S-block jointly applies low-rank SVD structural regularization and a lightweight deep image prior (DIP) to refine structured residuals. This staged design makes the interaction between abundance and residual components transparent and interpretable. Experiments on Samson, Urban, and Jasper Ridge datasets demonstrate that, under fixed and imperfect endmember priors, soft abundance relaxation consistently outperforms hard simplex projection. Under the default N-FINDR endmember prior, the proposed framework reduces the joint reconstruction error by 61.7\%--69.5\% compared with a fixed-$\mathbf{A}$ UCLS baseline, while keeping the abundance RMSE nearly unchanged, indicating that the residual refinement branch accounts for structured model mismatch without degrading the abundance estimates. For example, on Urban, the reconstruction SAM decreases from $5.99^\circ$ for the X-only model to $1.92^\circ$ for the full model.
Comments16 pages.Accepted to 23rd Pacific Rim International Conference on Artificial Intelligence (PRICAI 2026)
