Multisensor Images Fusion Based on Feature-Level

Details Multisensor Images Fusion Based on Feature-Level Multisensor Images Fusion Based on Feature-Level

2011-08-20

arxiv.org/abs/1108.4098v1

International Journal of Advanced Research in Computer Science,Volume 2, No. 4, July-August 2011

Computer Science

Computer Vision and Pattern Recognition

Keywords: Image fusion, Feature, Edge Fusion, Segment Fusion, IHS, PCA

Scientific paper

Until now, of highest relevance for remote sensing data processing and analysis have been techniques for pixel level image fusion. So, This paper attempts to undertake the study of Feature-Level based image fusion. For this purpose, feature based fusion techniques, which are usually based on empirical or heuristic rules, are employed. Hence, in this paper we consider feature extraction (FE) for fusion. It aims at finding a transformation of the original space that would produce such new features, which preserve or improve as much as possible. This study introduces three different types of Image fusion techniques including Principal Component Analysis based Feature Fusion (PCA), Segment Fusion (SF) and Edge fusion (EF). This paper also devotes to concentrate on the analytical techniques for evaluating the quality of image fusion (F) by using various methods including (SD), (En), (CC), (SNR), (NRMSE) and (DI) to estimate the quality and degree of information improvement of a fused image quantitatively.

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