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
Morphological segmentation
spatio-spectral segmentation
probability density function of contours
Spatial informations
multispectral image segmentation
Stochastic Watershed
Spectral segmentation
Spectral classification
Hyperspectral image segmentation
Probabilistic Watershed
Mathematical Morphology
Spectral information
Remote sensing images
Probability density function (pdf)
Monte Carlo Simulation
Eigenvectors
Correspondence analysis
Multispectral images
Multi-spectral
Pre-segmentation
Machine learning
Origine : Fichiers produits par l'(les) auteur(s)