Leveraging band diversity for feature selection in EO data

TL;DR

This paper proposes a method to select diverse spectral bands from hyperspectral imaging data using determinantal point processes and spectral angle mapper analysis, enhancing the efficiency and accuracy of earth observation data analysis.

Contribution

It introduces a novel band selection approach combining determinantal point processes with spectral angle analysis for hyperspectral data.

Findings

Effective band diversity selection improves reconstruction quality.

Method enhances analysis precision and reduces data complexity.

Applicable to various machine learning models for earth observation.

Abstract

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.