Abstract Band selection plays an important role in hyperspectral imaging for reducing the data and improving the efficiency of data acquisition and analysis whilst significantly lowering the cost of the imaging system. Without the category labels, it is challenging to select an effective and low-redundancy band subset. In this paper, a new unsupervised band selection algorithm is proposed based on a new band search criterion and an improved Determinantal Point Processes (DPP). First, to preserve the original information of hyperspectral image, a novel band search criterion is designed for searching the bands with high information entropy and low noise. Unfortunately, finding the optimal solution based on the search criteria to select a low-redundancy band subset is a NP-hard problem. To solve this problem, we consider the correlation of bands from both original hyperspectral image and its spatial information to construct a double-graph model to describe the relationship between spectral bands. Besides, an improved DPP algorithm is proposed for the approximate search of a low-redundancy band subset from the double-graph model. Experiment results on several well-known datasets show that the proposed optical band selection algorithm achieves better performance than many other state-of-the-art methods.

中文翻译：

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MIMN-DPP：无监督高光谱波段选择的最大信息和最小噪声决定点过程

摘要 波段选择在高光谱成像中起着重要的作用，可以减少数据，提高数据采集和分析的效率，同时显着降低成像系统的成本。没有类别标签，选择有效且低冗余的波段子集是一项挑战。在本文中，基于新的频带搜索准则和改进的行列式点过程（DPP），提出了一种新的无监督频带选择算法。首先，为了保留高光谱图像的原始信息，设计了一种新颖的波段搜索准则，用于搜索信息熵高、噪声低的波段。不幸的是，根据搜索标准找到最优解来选择低冗余频带子集是一个 NP 难题。为了解决这个问题，我们考虑原始高光谱图像波段的相关性及其空间信息，构建双图模型来描述光谱波段之间的关系。此外，提出了一种改进的DPP算法，用于从双图模型中近似搜索低冗余频带子集。在几个众所周知的数据集上的实验结果表明，所提出的光波段选择算法比许多其他最先进的方法获得了更好的性能。