Proceedings of the IEEE· 2012Q1
Advances in Spectral-Spatial Classification of Hyperspectral Images
- 1,271citations
- Q1SCImago
- 2012year
Short summary
A novel spectral-spatial classification method for hyperspectral images extracts spatial information at the object level using mathematical morphology, enhancing classification accuracy with SVMs and postprocessing techniques.
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Key points
- Spatial information is extracted at the object level, not pixel level, using mathematical morphology to capture structural characteristics.
- Support Vector Machines (SVMs) are used for classification, integrating both spectral data and extracted spatial features.
- Spatial postprocessing techniques, including pre-segmentation and multiple-classifier systems, are applied to regularize and refine preliminary classification maps.
- Experimental results on diverse hyperspectral images validate the effectiveness of the proposed spectral-spatial strategies.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Proceedings of the IEEE, 2012 · DOI ↗
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Field: Media Technology
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