论文标题
集成基于物理和数据驱动的模型，用于高光谱图像不变
Integration of Physics-Based and Data-Driven Models for Hyperspectral Image Unmixing
论文作者
论文摘要
频谱解混合是高光谱数据处理中最重要的定量分析任务之一。常规物理模型的特征是明确的解释。但是，它们可能不适合分析具有未知复杂物理特征的场景。近年来，数据驱动的方法已经迅速发展，特别是深度学习方法，因为它们在建模复杂和非线性系统中具有较高的能力。只需将这些方法作为黑盒传递以进行不混聚，可能会导致物理能力和泛化能力低。本文回顾了高光谱的构造作品，这些作品通过深度神经网络结构设计，先验的设计和损失设计来整合基于物理模型和数据驱动方法的优势。这些方法中的大多数源自常见的数学优化框架，并将良好的解释性与高精度相结合。
Spectral unmixing is one of the most important quantitative analysis tasks in hyperspectral data processing. Conventional physics-based models are characterized by clear interpretation. However they may not be suitable for analyzing scenes with unknown complex physical characteristics. Data-driven methods have developed rapidly in recent years, in particular deep learning methods because they possess superior capability in modeling complex and nonlinear systems. Simply transferring these methods as black-boxes to conduct unmixing may lead to low physical interpretability and generalization ability. This article reviews hyperspectral unmixing works that integrate advantages of both physics-based models and data-driven methods by means of deep neural network structures design, prior design and loss design. Most of these methods derive from a common mathematical optimization framework, and combine good interpretability with high accuracy.
