Image fusion for improving spatial resolution of multispectral satellite images

Ünlüsoy, Deniz
In this study, four different image fusion techniques have been applied to high spectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain fused images with increased spatial resolution, while preserving spectral information as much as possible. These techniques are intensity-hue-saturation (IHS) transform, principle component analysis (PCA), Brovey transform (BT), and Wavelet transform (WT) image fusion. Images used in the study belong to Çankırı region, and are obtained from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite. MATLAB is used to build a GUI to apply and to present the results of the image fusion algorithms. Subjective (visual) and objective evaluation of the fused images have been performed to evaluate the success of the approaches. The objective evaluation methods include correlation coefficient (CC), root mean squared error (RMSE), relative global dimensional synthesis error (ERGAS), and high-pass correlation coefficient (HPCC). For visual evaluation, sulphate index (SI) and mafic index (MI) specific to thermal infrared (TIR) and hydroxyl index “a” (OHIa) specific to shortwave infrared (SWIR) bands are used. The results indicate that the IHS transformation provides the highest performance in increasing lower spatial resolutions of TIR and SWIR bands of the ASTER images to higher spatial resolution of visible near infrared (VNIR) bands, at the expense of some loss of spectral information. PCA and BT methods, on the other hand, perform better with respect to preservation of spectral information, while being less successful in increasing spatial resolution. WT performs next to IHS transformation for improving spatial resolution and comes after PCA and BT methods with respect to the preservation of spectral information.


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Predictive applications of human eye visualization so called saliency map computational models become more attractive in image processing studies. Saliency map highlights regions that are distinctive from their surrounding in the images in interest. In this study, various computational models for salient region detection are investigated on remotely sensed images. The computational methods considered are Itti-Koch, Graph-Based Visual Saliency, Saliency Detection by Combining Simple Priors, Frequency-tuned S...
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Image formation model for photon sieves
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Citation Formats
D. Ünlüsoy, “Image fusion for improving spatial resolution of multispectral satellite images,” M.S. - Master of Science, Middle East Technical University, 2013.