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Recent advances in techniques for hyperspectral image processing
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DOI：10.1016/j.rse.2007.07.028.png)
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Abstract
En 中文
Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than 30 years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspectral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spatial and spectral information. Performance of the discussed techniques is evaluated in different analysis scenarios. To satisfy time-critical constraints in specific applications, we also develop efficient parallel implementations of some of the discussed algorithms. Combined, these parts provide an excellent snapshot of the state-of-the-art in those areas. and offer a thoughtful perspective on future potentials and emerging challenges in the design of robust hyperspectral imaging algorithms. (C) 2009 Elsevier Inc. All rights reserved.
Keywords:
Classification
Hyperspectral imaging
Kernel methods
Support vector machines
Markov random fields
Mathematical morphology
Spatial/spectral processing
Spectral mixture analysis
Endmember extraction
Parallel processing
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