用多样性选波段，提升遥感图像重建精度
Leveraging band diversity for feature selection in EO data
- 用行列式点过程选择差异大的波段组合
- 通过光谱角映射消除波段重叠问题
- 适合需要高精度遥感分析的研究者
高光谱成像（HSI）是一种强大的地球观测技术，可在宽广的波长范围内捕捉和处理信息。其提供的全面详细光谱数据对多种重建任务极具价值。然而，由于分析复杂性，处理大量波段常面临困难。为应对重建高质量HSI时波段数量庞大的挑战，本文提出将波段分组，并利用行列式点过程（DPP）在相关波段中选择具有多样性的波段子集。为解决分组可能带来的波段重叠问题，引入光谱角映射（SAM）分析。该方法可与任意机器学习模型结合，实现高精度、高准确率的细节分析与监测。
原文摘要 · Abstract (English)
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.
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