Leveraging band diversity for feature selection in EO data

2025-02-07

AI Summary

High-dimensional hyperspectral imaging (HSI) data suffer from severe band redundancy and high inter-band correlation, leading to the curse of dimensionality and degraded reconstruction accuracy. To address this, we propose a diversity-driven band grouping selection method based on Determinantal Point Processes (DPP), marking the first application of DPP to Earth observation (EO) band selection. We further integrate Spectral Angle Mapper (SAM) to quantify spectral similarity among bands, effectively mitigating group overlap and enabling physically interpretable, discriminative feature subset construction. Experiments demonstrate that our method achieves substantial dimensionality reduction—averaging over 60% band compression—while preserving critical spectral discriminability. Consequently, image reconstruction PSNR improves by 2.1–3.8 dB, and downstream classification and anomaly detection accuracy increases by 3.2–5.7%. This work establishes a novel paradigm for lightweight, high-accuracy intelligent remote sensing analysis.

Technology Category

• Machine Learning: Dimensionality Reduction/Feature Selection
• Computer Vision: Remote Sensing / Geospatial AI
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs

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.

Problem

• Select diverse bands in hyperspectral imaging
• Group bands to handle large data complexity
• Use spectral angle mapper for overlapping bands

Innovation

• Grouping bands for HSI data
• Determinantal point processes for band selection
• Spectral angle mapper for overlapping bands