Computer Science – Computer Vision and Pattern Recognition
Scientific paper
2011-08-12
International Journal of Artificial Intelligence and Knowledge Discovery Vol.1, Issue 3, July, 2011
Computer Science
Computer Vision and Pattern Recognition
Keywords: Data Fusion, Resolution Enhancement, Statistical fusion, Correlation Modeling, Matching, pixel based fusion
Scientific paper
There are many image fusion methods that can be used to produce high-resolution mutlispectral images from a high-resolution panchromatic (PAN) image and low-resolution multispectral (MS) of remote sensed images. This paper attempts to undertake the study of image fusion techniques with different Statistical techniques for image fusion as Local Mean Matching (LMM), Local Mean and Variance Matching (LMVM), Regression variable substitution (RVS), Local Correlation Modeling (LCM) and they are compared with one another so as to choose the best technique, that can be applied on multi-resolution satellite images. This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including Standard Deviation (SD), Entropy(En), Correlation Coefficient (CC), Signal-to Noise Ratio (SNR), Normalization Root Mean Square Error (NRMSE) and Deviation Index (DI) to estimate the quality and degree of information improvement of a fused image quantitatively.
Al-Wassai Firouz Abdullah
Al-Zaky Ali A.
Kalyankar Namdeo V.
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