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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Bibliographic Details
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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Summary: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.
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ISSN:0018-9219
1558-2256
DOI:10.1109/JPROC.2012.2197589