标题： 用于提高解混性能的高光谱图像尺度校正的几何建模预处理算法 显示英文标题

标题： Preprocessing Algorithm Leveraging Geometric Modeling for Scale Correction in Hyperspectral Images for Improved Unmixing Performance

摘要： 光谱变化显著影响高光谱解混算法的准确性和收敛性。 许多方法解决了复杂的光谱变化；然而，由于地形、光照和阴影导致的观测像素特征的大规模畸变仍然是一个主要挑战。 这些变化通常会降低解混性能并使模型拟合更加复杂。 因此，纠正这些变化可以在实际的GIS应用中带来显著优势。 在本文中，我们提出了一种新的预处理算法，在解混之前校正尺度引起的光谱变化。 通过估计和校正像素特征的尺度畸变，该算法生成具有最小尺度畸变的像素特征。 由于这些尺度畸变（阻碍了许多解混方法的性能）在所提出方法提供的输出中被大大减少，解混算法的丰度估计得到了显著改善。 我们提出了一个严格的数学框架来描述和校正尺度变化，并提供了所提出算法的广泛实验验证。 此外，在两个合成和两个真实高光谱数据集上，评估了该算法对一系列最先进的解混方法的影响。 所提出的预处理步骤始终提高了这些算法的性能，在误差减少方面达到了约50%，即使是对处理光谱变化专门设计的算法也是如此。 这表明尺度校正作为一种补充步骤，有助于现有方法实现更精确的解混。 该算法的通用性、一致的影响和显著的影响突显了其作为实际高光谱解混流程中的关键组件的潜力。 该算法的实现代码将在发表后公开。 显示英文摘要

摘要： Spectral variability significantly impacts the accuracy and convergence of hyperspectral unmixing algorithms. Many methods address complex spectral variability; yet large-scale distortions to the scale of the observed pixel signatures due to topography, illumination, and shadowing remain a major challenge. These variations often degrade unmixing performance and complicate model fitting. Because of this, correcting these variations can offer significant advantages in real-world GIS applications. In this paper, we propose a novel preprocessing algorithm that corrects scale-induced spectral variability prior to unmixing. By estimating and correcting these distortions to the scale of the pixel signatures, the algorithm produces pixel signatures with minimal distortions in scale. Since these distortions in scale (which hinder the performance of many unmixing methods) are greatly minimized in the output provided by the proposed method, the abundance estimation of the unmixing algorithms is significantly improved. We present a rigorous mathematical framework to describe and correct for scale variability and provide extensive experimental validation of the proposed algorithm. Furthermore, the algorithm's impact is evaluated across a wide range of state-of-the-art unmixing methods on two synthetic and two real hyperspectral datasets. The proposed preprocessing step consistently improves the performance of these algorithms, achieving error reductions of around 50%, even for algorithms specifically designed to handle spectral variability. This demonstrates that scale correction acts as a complementary step, facilitating more accurate unmixing with existing methods. The algorithm's generality, consistent impact, and significant influence highlight its potential as a key component in practical hyperspectral unmixing pipelines. The implementation code will be made publicly available upon publication.