通过最佳邻域重建进行高光谱波段选择

摘要：波段选择是高光谱图像降维中最重要的技术之一。与传统的特征选择问题不同，波段选择的一个重要特点是相邻 波段（（即指数相近的波段）之间通常具有很强的相关性。为了充分利用这些先验信息，提出了一种称为最佳邻域重构（（ONR） 的新型波段选择方法。在 ONR 中，波段选择被视为组合优化问题。它通过评估波段组合重建原始数据的能力来评估波 段组合，并应用降噪器来最小化噪声波段的影响。ONR 不使用一些近似算法，而是利用优化目标所依赖的递归关系以有 效的方式获得最优解。此外，我们开发了一种参数选择方法来自动确定 ONR 的参数，确保它适用于不同的数据集。在 实验中，我们将 ONR 与一些最先进的方法在六个 HSI 数据集上进行了比较。结果表明，在大多数情况下，ONR 比其 他方法更有效、更稳健。

关键词：字典学习、高光谱波段选择、最小二乘、稀疏表示。

期刊：Transactions on Geoscience and Remote Sensing- IEEE

DOI：10.1109/TGRS.2020.2987955

设备：GaiaField

Hyperspectral Band Selection via Optimal Neighborhood Reconstruction

Abstract：Band selection is one of the most important technique in the reduction of hyperspectral image (HSI). Different from traditional feature selection problem, an important characteristic of it is that there is usually strong correlation between neighboring bands, that is, bands with close indexes. Aiming to fully exploit this prior information, a novel band selection method called optimal neighborhood reconstruction (ONR) is proposed. In ONR, band selection is considered as a combinatorial optimization problem. It evaluates a band combination by assessing its ability to reconstruct the original data, and applies a noise reducer to minimize the influence of noisy bands. Instead of using some approximate algorithms, ONR exploits a recurrence relation that underlies the optimization target to obtain the optimal solution in an efficient way. Besides, we develop a parameter selection approach to automatically determine the parameter of ONR, ensuring it is adaptable to different data sets. In experiments, ONR is compared with some state-of-the-art methods on six HSI data sets. The results demonstrate that ONR is more effective and robust than the others in most of the cases.

Keywords：Dictionary learning, hyperspectral band selection, least square, sparse representation.