Spatial/spectral area-wise analysis for the classification of hyperspectral data

Guillaume Roussel; Véronique Achard; Alexandre Alakian; Jean-Claude Fort

doi:10.1117/12.898415

26 October 2011 Spatial/spectral area-wise analysis for the classification of hyperspectral data

Guillaume Roussel, Véronique Achard, Alexandre Alakian, Jean-Claude Fort

Proceedings Volume 8180, Image and Signal Processing for Remote Sensing XVII; 81800R (2011) https://doi.org/10.1117/12.898415
Event: SPIE Remote Sensing, 2011, Prague, Czech Republic

Abstract

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.

Citation Download Citation

Guillaume Roussel, Véronique Achard, Alexandre Alakian, and Jean-Claude Fort "Spatial/spectral area-wise analysis for the classification of hyperspectral data", Proc. SPIE 8180, Image and Signal Processing for Remote Sensing XVII, 81800R (26 October 2011); https://doi.org/10.1117/12.898415

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KEYWORDS

Image segmentation

Image classification

Image processing

Data modeling

Hyperspectral imaging

Image processing algorithms and systems

Principal component analysis

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