Spectral information analysis of image fusion data for remote sensing applications

Abstract

Fusion of images with different spatial resolutions has the capability of improving visualization and spatial resolution and enhancing structural/textural information of the involved images, while preserving the spectral information in multi-spectral (MS) images. In this paper, various fusion methods have been examined in the data fusion of GeoEye-1 and QuickBird imagery, followed by subsequent image control. The effectiveness of five techniques, the Gram-Schmidt (GS), high-pass filtering (HPF), modified intensity-hue-saturation (M-IHS), fast Fourier transform (FFT)-enhanced IHS (FFT-E) and wavelet principal component analysis (W-PCA), has been evaluated through visual inspection, histogram analysis and correlation analysis. Also, image quality information is assessed by means of global spectral information (relative dimensionless global error, relative average spectral error), spectral distortion (peak signal-to-noise ratio), spectral (bias, root means square error) and spatial information (mean, standard deviation) of the fused images. In addition, the extraction of object boundary is tested and evaluated using Canny edge detection. The results show that most of the image fusion techniques preserve spectral information of original image, but occasionally with some spectral distortion. It has been found that the GS method, followed by HPF, yields the best information quality in the fused image, suitable for improving visual interpretation and data quality from the viewpoint of remote sensing applications.