Paper Contents
Abstract
The rapid urbanization of Lagos State has increased the need for accurate and high-resolution spatial data to support urban planning and environmental monitoring. However, the limitations of existing satellite imagery in terms of spatial and spectral resolution often reduce classification accuracy and the utility of remote sensing outputs. This study performs a comparative analysis of five statistical image fusion techniquesIntensity Hue Saturation (IHS), Brovey Transformation (BT), ESRI Pan Sharpening Transformation (ET), Simple Mean Transformation (SM), and Gram-Schmidt (GS)for enhancing the mapping and modelling of urban spatial features in Lagos State. Using Landsat 8, Sentinel-2, and QuickBird imagery, the study evaluated the performance of these techniques based on gradient, deviation, and correlation coefficient metrics. The Simple Mean transformation demonstrated superior performance, with enhanced spectral fidelity and higher spatial resolution, as evidenced by a correlation coefficient of 0.9539 and a standard error of 0.0461. The study concludes that Simple Mean fusion offers the most reliable enhancement technique for accurate urban spatial feature extraction and recommends its adoption for urban remote sensing applications.
Copyright
Copyright © 2025 Adeniran, M.A., Ossai, E.N., Igbokwe, J.I. This is an open access article distributed under the Creative Commons Attribution License.
