Region based image fusion using SVM

Yang Liu; Jian Cheng; Hanqing Lu

doi:10.1117/12.749598

15 November 2007 Region based image fusion using SVM

Yang Liu, Jian Cheng, Hanqing Lu

Proceedings Volume 6787, MIPPR 2007: Multispectral Image Processing; 67871E (2007) https://doi.org/10.1117/12.749598
Event: International Symposium on Multispectral Image Processing and Pattern Recognition, 2007, Wuhan, China

Abstract

This paper presents a novel fusion approach using PCA merger based on multiscale decomposition (MSD), combined with region segmentation and support vector machine (SVM), the result is a high spatial resolution multispectral image from a high resolution panchromatic (Pan) image and low resolution multispectral (Ms) images. Principal components analysis (PCA) fusion technique is one of typical fusion methods, and PCA merger based on MSD had been proposed which can obtain better performance. As we know that, in pixel fusion level, the original images are fused as internal region regardless of the contents of images, but in this paper, we perform region segmentation after MSD, because the homogeneous regions have similar features such as color, texture and intensity. Traditionally, various fusion rules can be applied after MSD according to different conditions, however, the crucial problem is which fusion rule should be adopted under given condition, hence we use the SVM to combine the most fusion rules so that can avoid some drawbacks using single fusion rule. To validate our approach, we compare it with several typical fusion approaches, and the best result is obtained using our approach.

Citation Download Citation

Yang Liu, Jian Cheng, and Hanqing Lu "Region based image fusion using SVM", Proc. SPIE 6787, MIPPR 2007: Multispectral Image Processing, 67871E (15 November 2007); https://doi.org/10.1117/12.749598

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KEYWORDS

Image fusion

Image segmentation

Principal component analysis

Multispectral imaging

Wavelets

Image quality

Image resolution

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