A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images

A general framework of spatio-spectral segmentation for multispectral images is introduced in this paper. The method is based on classification-driven stochastic watershed by Monte Carlo simulations, and it gives more regular and reliable contours than standard watershed. The present approach is decomposed into several sequential steps. First, a dimensionality reduction stage is performed using 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 watershed approach driven by the spectral classification. The pdf of contours is finally segmented by a watershed controlled by markers coming from a regularization of the initial classification.