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Results and Discussions

Chapter

Abstract

This chapter provides the consolidated results of various techniques for visualization-oriented fusion of hyperspectral images presented in Chaps. 3, 5–7 of this monograph. These fusion techniques together span a wide variety of image processing methodologies. We began with a signal processing approach, and derived the \(\upalpha \)-mattes for fusion using a bilateral filter.The second technique dealt with fusion as an estimation problem, and provided a solution using the Bayesian framework. We also explored the concept of matte-less fusion in Chap. 6 where there was no explicit calculation of fusion weights. Lastly, we posed fusion as an optimization problem where some of the desired characteristics of the fused output image had driven the process of fusion. Combining the results from all these solutions enables us to compare and analyze their performances. We also consider some of the other recently developed techniques of hyperspectral image fusion for comparison along with the techniques discussed in this monograph. While it is possible to obtain either grayscale or RGB results of the fusion using any of the fusion techniques from the monograph, most of the performance measures have been defined for the grayscale images. Thus, we discuss the quantitative assessment of the resultant grayscale images of all the techniques considered in this chapter.

Keywords

Hyperspectral Image Fusion Technique Relative Bias Hyperspectral Data Bilateral Filter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Electrical EngineeringIndian Institute of Technology BombayPowai, MumbaiIndia

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