A band selection algorithm named space and information comprehensive evaluation model (SICEM) is proposed in this paper, which reconstitutes the hyperspectral imagery by building an optimal subset to replace the original spectrum. SICEM reduces the dimensions while keeping the vital information of an image, and these are accomplished through two phases. Specifically, the improved fast density peaks clustering (I-FDPC) algorithm is employed to pick out the scattered bands in geometric space to generate a candidate set at first. Then, we conduct pruning in through iterative information analysis until the target set is built. In this phase, we need to calculate comprehensive information score (CIS) for every member in after assigning weights to the amount of information (AoI) and correlation. In each iteration, the band with highest score is selected into , and the ones highly related to it will be removed out of via a threshold. Compared with the four state-of-the-art unsupervised algorithms on real-world HSI datasets (IndianP and PaviaU), we find that SICEM has strong ability to form an optimal reduced-dimension combination with low correlation and rich information and it performs well in discrete band distribution, accuracy, consistency, and stability.

中文翻译：

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SICEM：基于空间和信息分析的高光谱图像重建波段组合生成方法


本文提出了一种名为空间与信息综合评价模型（SICEM）的波段选择算法，通过构建最优子集来替换原始光谱来重构高光谱图像。 SICEM 在保留图像重要信息的同时减小了尺寸，这些是通过两个阶段完成的。具体而言，首先采用改进的快速密度峰聚类（I-FDPC）算法挑选出几何空间中的分散带以生成候选集。然后，通过迭代信息分析进行剪枝，直至构建目标集。在这个阶段，我们需要在为信息量（AoI）和相关性分配权重后，计算每个成员的综合信息得分（CIS）。在每次迭代中，得分最高的乐队被选入，与它高度相关的将通过阈值被剔除。与真实 HSI 数据集上的四种最先进的无监督算法（IndianP 和 PaviaU）相比，我们发现 SICEM 具有很强的形成相关性低、信息丰富的最优降维组合的能力，并且表现良好离散带分布、准确性、一致性和稳定性。