A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images
Résumé
A general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper. The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS. The present approach is decomposed into several sequential steps. First, a dimensionality-reduction stage is performed using the factor-correspondence analysis method. In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced. Then, a spectral classification produces a spectral pre-segmentation of the image. Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification. The pdf of the contours is finally segmented by a WS controlled by markers from a regularization of the initial classification.
Mots clés
- Machine learning
- Pre-segmentation
- Multi-spectral
- Multispectral images
- Correspondence analysis
- Eigenvectors
- Monte Carlo Simulation
- Probability density function (pdf)
- Remote sensing images
- Spectral information
- Mathematical Morphology
- Probabilistic Watershed
- Hyperspectral image segmentation
- Spectral classification
- Spectral segmentation
- Stochastic Watershed
- multispectral image segmentation
- Spatial informations
- probability density function of contours
- spatio-spectral segmentation
- Morphological segmentation
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