2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 21 , Issue 1 , PP: 336-343, 2026 | Cite this article as | XML | Html | PDF | Full Length Article
Asmaa Sadiq 1 *
Doi: https://doi.org/10.54216/FPA.210123
Remotely sensed images have played a valuable role in several applications such as image classification, feature extraction, land cover monitoring, and others; thus, the need for high-resolution satellite images has become necessary and essential. In order to produce images with very high spectral and spatial resolution, the pan-sharpening techniques—, which are regarded as a subset of data fusion techniques—combine the color information of the multispectral image from the same scene with the distinct geometric features of the panchromatic image. This work conducts a comparative analysis of four pansharpening methods (Gram, HIS, Brovey, and PC) specifically applied to Landsat 7 images, providing a thorough evaluation across multiple performance metrics. Also we introduce and apply performance metrics that not only measure quantitative accuracy (like RMSE and RASE) but also assess the preservation of spatial details, offering a more holistic evaluation of pansharpening techniques. The qualitative and quantitative results indicate that both GS and IHS techniques have accurate performance.
Pansharpening , Component substitution , Principal Component Analysis , Gram-Schmidt , Brovey , HIS , Landsat 7 images
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