Advances in Spectral-Spatial Classification of Hyperspectral Images

Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel...

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Published in:	Proceedings of the IEEE Vol. 101; no. 3; pp. 652 - 675
Main Authors:	Fauvel, Mathieu, Tarabalka, Yuliya, Benediktsson, Jon Atli, Chanussot, Jocelyn, Tilton, James C.
Format:	Journal Article
Language:	English
Published:	New York IEEE 01.03.2013 The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Institute of Electrical and Electronics Engineers
Subjects:	Classification Classification algorithms Computer Science Construction Feature extraction hyperspectral image Hyperspectral imaging Image contrast Image Processing Image segmentation Kernel kernel methods mathematical morphology morphological neighborhood Nearest neighbor searches Pixels Remote sensing segmentation Spatial resolution Spectra Spectral analysis spectral-spatial classifier Strategy spectral-spatial classifier morphological neighborhood hyperspectral image kernel method mathematical morphology segmentation classification
ISSN:	0018-9219, 1558-2256
Online Access:	Get full text
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Abstract	Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
AbstractList	Recent Advances in Spectral-Spatial Classification of Hyperspectral Images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes characteristics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods. Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both spatial and spectral information. Spatial information is extracted at the object (set of pixels) level rather than at the conventional pixel level. Mathematical morphology is first used to derive the morphological profile of the image, which includes character- istics about the size, orientation, and contrast of the spatial structures present in the image. Then, the morphological neighborhood is defined and used to derive additional features for classification. Classification is performed with support vector machines (SVMs) using the available spectral information and the extracted spatial information. Spatial postprocessing is next investigated to build more homogeneous and spatially consistent thematic maps. To that end, three presegmentation techniques are applied to define regions that are used to regularize the preliminary pixel-wise thematic map. Finally, a multiple-classifier (MC) system is defined to produce relevant markers that are exploited to segment the hyperspectral image with the minimum spanning forest algorithm. Experimental results conducted on three real hyperspectral images with different spatial and spectral resolutions and corresponding to various contexts are presented. They highlight the importance of spectral-spatial strategies for the accurate classification of hyperspectral images and validate the proposed methods.
Author	Benediktsson, Jon Atli Chanussot, Jocelyn Tilton, James C. Fauvel, Mathieu Tarabalka, Yuliya
Author_xml	– sequence: 1 givenname: Mathieu surname: Fauvel fullname: Fauvel, Mathieu email: mathieu.fauvel@ensat.fr organization: DYNAFOR lab, INRA and the University of Toulouse, France – sequence: 2 givenname: Yuliya surname: Tarabalka fullname: Tarabalka, Yuliya organization: AYIN, INRIA, Sophia Antipolis, France – sequence: 3 givenname: Jon Atli surname: Benediktsson fullname: Benediktsson, Jon Atli organization: Faculty of Electrical and Computer Engineering, University of Iceland, Iceland – sequence: 4 givenname: Jocelyn surname: Chanussot fullname: Chanussot, Jocelyn organization: GIPSA-lab, Grenoble Institute of Technology , France – sequence: 5 givenname: James C. surname: Tilton fullname: Tilton, James C. organization: NASA Goddard Space Flight Center, USA
BackLink	https://inria.hal.science/hal-00737075$$DView record in HAL
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Snippet	Recent advances in spectral-spatial classification of hyperspectral images are presented in this paper. Several techniques are investigated for combining both... Recent Advances in Spectral-Spatial Classification of Hyperspectral Images are presented in this paper. Several techniques are investigated for combining both...
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SubjectTerms	Classification Classification algorithms Computer Science Construction Feature extraction hyperspectral image Hyperspectral imaging Image contrast Image Processing Image segmentation Kernel kernel methods mathematical morphology morphological neighborhood Nearest neighbor searches Pixels Remote sensing segmentation Spatial resolution Spectra Spectral analysis spectral-spatial classifier Strategy
Title	Advances in Spectral-Spatial Classification of Hyperspectral Images
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