利用几何结构提升光谱混合精度，显著优于现有半监督方法。
MiSiSUn: Minimum Simplex Semisupervised Unmixing
- 基于典型分析模型引入单纯形体积惩罚，融合数据几何特性
- 在多种噪声与混合比例下，性能提升1~3 dB以上
- 开源实现，适合遥感图像解混研究者使用
本文提出一种名为最小单纯形半监督解混（MiSiSUn）的几何型半监督解混方法。首次将数据几何特性引入基于光谱库的解混，采用基于典型分析类线性模型的单纯形体积风格惩罚项。实验在两个模拟数据集上进行，涵盖不同混合比例、空间结构及输入噪声水平。结果表明，MiSiSUn 显著优于当前最先进的半监督解混方法，性能提升范围为1 dB至超过3 dB。该方法还应用于真实数据集，视觉解释与地质图高度一致。代码基于 PyTorch 实现，开源可获取（https://github.com/BehnoodRasti/MiSiSUn），并提供专用 Python 包，包含实验中所有方法以保障可复现性。
原文摘要 · Abstract (English)
This paper proposes a semisupervised geometric unmixing approach called minimum simplex semisupervised unmixing (MiSiSUn). The geometry of the data was incorporated for the first time into library-based unmixing using a simplex-volume-flavored penalty based on an archetypal analysis-type linear model. The experimental results were performed on two simulated datasets considering different levels of mixing ratios and spatial instruction at varying input noise. MiSiSUn considerably outperforms state-of-the-art semisupervised unmixing methods. The improvements vary from 1 dB to over 3 dB in different scenarios. The proposed method was also applied to a real dataset where visual interpretation is close to the geological map. MiSiSUn was implemented using PyTorch, which is open-source and available at https://github.com/BehnoodRasti/MiSiSUn. Moreover, we provide a dedicated Python package for Semisupervised Unmixing, which is open-source and includes all the methods used in the experiments for the sake of reproducibility.
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