Hyperspectral imaging (HSI) is a powerful earth observation technology that captures and processes information across a wide spectrum of wavelengths. Hyperspectral imaging provides comprehensive and detailed spectral data that is invaluable for a wide range of reconstruction problems. However due to complexity in analysis it often becomes difficult to handle this data. To address the challenge of handling large number of bands in reconstructing high quality HSI, we propose to form groups of bands. In this position paper we propose a method of selecting diverse bands using determinantal point processes in correlated bands. To address the issue of overlapping bands that may arise from grouping, we use spectral angle mapper analysis. This analysis can be fed to any Machine learning model to enable detailed analysis and monitoring with high precision and accuracy.

翻译：高光谱成像（HSI）是一种强大的地球观测技术，它通过捕获和处理宽波长范围内的光谱信息来获取数据。高光谱成像能够提供全面而详细的光谱数据，这对于各类重建问题具有不可估量的价值。然而，由于其分析的复杂性，处理这类数据往往变得十分困难。为了应对在重建高质量高光谱图像时处理大量波段的挑战，我们提出对波段进行分组。在本立场论文中，我们提出了一种在相关波段中利用行列式点过程来选择多样性波段的方法。为了解决分组可能产生的波段重叠问题，我们采用了光谱角制图分析。该分析结果可输入任何机器学习模型，从而实现高精度、高准确度的详细分析与监测。