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Sensors 2009, 9(1), 196-218; doi:10.3390/s90100196

Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines

Department of Technology of Computers and Communications, University of Extremadura / Escuela Politécnica de Cáceres, Avenida de la Universidad s/n, E-10071 Cáceres, Spain
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Received: 11 December 2008 / Revised: 7 January 2009 / Accepted: 7 January 2009 / Published: 8 January 2009
(This article belongs to the Section Remote Sensors)
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Abstract

Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA’s Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets.
Keywords: Hyperspectral imaging; remote sensing; morphological profiles; spatial-spectral classification; vector ordering; land-cover classification; support vector machine (SVM) Hyperspectral imaging; remote sensing; morphological profiles; spatial-spectral classification; vector ordering; land-cover classification; support vector machine (SVM)
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Plaza, J.; Plaza, A.J.; Barra, C. Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines. Sensors 2009, 9, 196-218.

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