Multi-sensors, Multi-resolution Data Fusion Using Principal Components Analysis and Histogram Specification Techniques

Image data fusion is the process of setting together information gathered by different heterogeneous sensors, mounted on different platforms. This paper presents an effective multi-resolution image data fusion methodology, which is based on utilizing the Principal Component Analysis “PCA” and Histogram Specification “HS” techniques. The first principal component “PCA1” involves much of the variability in the spectral data; while the reminder PCAs contain the remaining variability in a descend order. In this research, the low resolution multispectral are, firstly, resized (i.e. enlarged) into the high resolution “panchromatic” image size, then transformed into several PCAs. The high resolution panchromatic image should be normalized to have the same number of gray levels as the PCA1. The renormalized panchromatic image, then, replace the PCA1 of the lowresolutionmultispectral image in the PCA transformed domain. The preliminary high-resolution-multispectral images are produced by inversely transform the modified PCA’s file. The final high-resolutionmultispectral bands are created by pushing the histogram of each created image bands toward the histogram of its corresponding original multispectral band, utilizing the histogram-specification method.