Spectral Fidelity and Spatial Enhancement: An Assessment and Cascading of Pan-Sharpening Techniques for Satellite Imagery

TL;DR

Pan-sharpening aims to fuse high-resolution panchromatic data with multispectral imagery while preserving spectral integrity. This work introduces a cascaded evaluation framework that jointly assesses spectral fidelity and spatial enhancement, enabling informed method selection. Through a comparative analysis of Brovey, PCA-based, IHS, and wavelet-based fusion, the study develops a set of spectral, spatial, and hybrid metrics, including a Quality Index tailored to applications. The findings highlight robust trade-offs and provide practical guidance for remote sensing tasks, with implications for future ML-based pan-sharpening and metric development.

Abstract

This research presents a comprehensive assessment of pan-sharpening techniques for satellite imagery, focusing on the critical aspects of spectral fidelity and spatial enhancement. Motivated by the need for informed algorithm selection in remote sensing, A novel cascaded and structured evaluation framework has been proposed with a detailed comparative analysis of existing methodologies. The research findings underscore the intricate trade-offs between spectral accuracy of about 88\% with spatial resolution enhancement. The research sheds light on the practical implications of pan-sharpening and emphasizes the significance of both spectral and spatial aspects in remote sensing applications. Various pan-sharpening algorithms were systematically employed to provide a holistic view of their performance, contributing to a deeper understanding of their capabilities and limitations.

Figures (7)

• Figure 1: Spectral Content Preservation
• Figure 2: Multi-temporal Satellite Images
• Figure 3: Edge Preservation
• Figure 4: Hybrid Approach for Object Based Detection
• Figure 5: Atmospheric Correction