Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines
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 availabilit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2009-01-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1424-8220/9/1/196/ |
| _version_ | 1828371949628162048 |
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| author | Javier Plaza Cristina Barra Antonio J. Plaza |
| author_facet | Javier Plaza Cristina Barra Antonio J. Plaza |
| author_sort | Javier Plaza |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-14T06:56:19Z |
| format | Article |
| id | doaj.art-a20e35d2cb75467c89f980d1b5413a7c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-14T06:56:19Z |
| publishDate | 2009-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-a20e35d2cb75467c89f980d1b5413a7c2022-12-22T02:06:53ZengMDPI AGSensors1424-82202009-01-019119621810.3390/s90100196Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector MachinesJavier PlazaCristina BarraAntonio J. PlazaHyperspectral 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.http://www.mdpi.com/1424-8220/9/1/196/Hyperspectral imagingremote sensingmorphological profilesspatial-spectral classificationvector orderingland-cover classificationsupport vector machine (SVM) |
| spellingShingle | Javier Plaza Cristina Barra Antonio J. Plaza Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines Sensors Hyperspectral imaging remote sensing morphological profiles spatial-spectral classification vector ordering land-cover classification support vector machine (SVM) |
| title | Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines |
| title_full | Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines |
| title_fullStr | Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines |
| title_full_unstemmed | Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines |
| title_short | Multi-Channel Morphological Profiles for Classification of Hyperspectral Images Using Support Vector Machines |
| title_sort | multi channel morphological profiles for classification of hyperspectral images using support vector machines |
| topic | Hyperspectral imaging remote sensing morphological profiles spatial-spectral classification vector ordering land-cover classification support vector machine (SVM) |
| url | http://www.mdpi.com/1424-8220/9/1/196/ |
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