Paper Info
Title
A fast approach for fusion of hyperspectral images through redundancy elimination
Abstract
The fusion of hyperspectral images is an important area in research and applications. Several fusion techniques have been developed in the literature for visualization of hyper-spectral data. The amount of computation needed for such techniques is directly related to the volume of the data. Most of these techniques involve a significant amount of computation due to high volume of the data, making the fusion processes slow. We analyze the statistical characteristics of this data in order to develop a technique for faster fusion. The image bands in the hyperspectral data represent the response of the scene collected over contiguous narrow bands of wavelength. The adjacent bands being captured over neighboring wavelength bands, these images exhibit a very high degree of similarity. The fusion of these adjacent image bands, thus adds a very little amount of additional information. We exploit this redundancy in the data to provide a novel scheme for rapid visualization. We propose a scheme for the selection of a subset of images from a hyper-spectral image cube that can produce fusion results with a very small amount of degradation in the quality compared to the quality of the result using the same technique of fusion applied over the entire data.
Year
DOI
Venue
2010
10.1145/1924559.1924627
ICVGIP
Keywords
Field
DocType
redundancy elimination,fusion result,small amount,entire data,hyperspectral data,faster fusion,hyper-spectral image cube,significant amount,fast approach,hyper-spectral data,hyperspectral image,adjacent image band,fusion technique
Computer vision,Pattern recognition,Image fusion,Computer science,Visualization,Fusion,Hyperspectral imaging,Redundancy (engineering),Artificial intelligence,Wavelength,Cube,Computation
Conference
Citations 
PageRank 
References 
5
0.53
8
Authors
2
Name
Order
Citations
PageRank
Ketan Kotwal1653.49
Subhasis Chaudhuri21384133.18