Multi-Resource Data Fusion and Identification for Targets

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Abstract:

Multisource image fusion technology is widely applied in a variety of fields such as remote sensing, computer vision, medical diagnosis and military surveillance. In most instances, multi-spectral and panchromatic images can provide more complementary information for feature extraction and target recognition. Algorithms based on gradient for image fusion only consider high-frequency information changes of the images, and neglect the richness of high-frequency information. To solve this problem, a new self-adaptive rule and algorithm based on gradient and energy is proposed in this paper by analyzing the internal relations between the statistics and the content of the images. The rule and algorithm takes into account the changes and richness of high-frequency information, and guarantees the richness of image information. Experiments show that the fusion rule and algorithm can significantly preserve all useful information from primitive images and effectively recognize targets with higher recognition accuracy.

Keywords: DATA FUSION; ENERGY; GRADIENT; LOCAL STATISTICS

Document Type: Research Article

DOI: http://dx.doi.org/10.1166/asl.2012.2298

Publication date: March 1, 2012

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  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
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