Spatial/spectral area-wise analysis for the classification of hyperspectral data
| Autor: | Véronique Achard, Alexandre Alakian, Guillaume Roussel, Jean-Claude Fort |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Segmentation-based object categorization
business.industry Hyperspectral imaging Scale-space segmentation Pattern recognition Mixture model computer.software_genre ComputingMethodologies_PATTERNRECOGNITION Geography Region growing Computer Science::Computer Vision and Pattern Recognition Principal component analysis Segmentation Data mining Artificial intelligence business computer Spatial analysis |
| Zdroj: | SPIE Proceedings. |
| ISSN: | 0277-786X |
| Popis: | In this paper, we propose an innovative classification method dedicated to hyperspectral images which uses both spectral information (Principal Component Analysis bands, Minimum Noise Fraction bands) and spatial information (textural features and segmentation). The process includes a segmentation as a pre-processing step, a spatial/spectral features calculation step and finally an area-wise classification. The segmentation, a region growing method, is processed according to a criterion called J-image which avoids the risks of over-segmentation by considering the homogeneity of an area at a textural level as well as a spectral level. Then several textural and spectral features are calculated for each area of the segmentation map and these areas are classified with a hierarchical ascendant classification. The method has been applied on several data sets and compared to the Gaussian Mixture Model classification. The JSEG classification process finally appeared to gives equivalent, and most of the time more accurate classification results. |
| Databáze: | OpenAIRE |
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