基于核的非线性光谱解混与字典修剪

摘要：光谱解混通过将观察到的像素光谱分解为组成光谱特征及其相关分数的集合来提取子像素信息。考虑到线性解混 模型的限制，非线性解混算法在复杂场景中找到了应用。基于核的算法是非线性解混的重要候选者，因为它们不需要特 定的模型假设并且具有中等计算复杂度。在本文中，我们重点研究线性混合和非线性波动模型。我们提出了一种两步基 于核的解混算法来解决使用大型光谱库作为候选端元或稀疏混合的情况。引入稀疏诱导正则化来执行端元选择，然后修 剪候选库以提供更准确的结果。使用合成和真实数据（特别是实验室创建的标记数据）的实验结果表明，与最先进的方 法相比，所提出的算法是有效的。

关键词：高光谱图像；光谱解混; 非线性解混; 核模型; 稀疏模型

期刊：remote sensing

DOI：10.3390/rs11050529

设备：GaiaField

Kernel-Based Nonlinear Spectral Unmixing with Dictionary Pruning

Abstract：Spectral unmixing extracts subpixel information by decomposing observed pixel spectra into a collection of constituent spectra signatures and their associated fractions. Considering the restriction of linear unmixing model, nonlinear unmixing algorithms find their applications in complex scenes. Kernel-based algorithms serve as important candidates for nonlinear unmixing as they do not require specific model assumption and have moderate computational complexity. In this paper we focus on the linear mixture and nonlinear fluctuation model. We propose a two-step kernel-based unmixing algorithm to address the case where a large spectral library is used as the candidate endmembers or the sparse mixture case.The sparsity-inducing regularization is introduced to performtheendmemberselectionandthecandidatelibraryisthenprunedtoprovidemoreaccurate results. Experimental results with synthetic and real data, particularly those laboratory-created labeled, show the effectiveness of the proposed algorithm compared with state-of-art methods.

Keywords：hyperspectral images; spectral unmixing; nonlinear unmixing; kernel model; sparse model