@article {Noyel:2010:0143-1161:5895,
title = "A new spatio-spectral morphological segmentation for multi-spectral remote-sensing images",
journal = "International Journal of Remote Sensing",
parent_itemid = "infobike://tandf/tres",
publishercode ="tandf",
year = "2010",
volume = "31",
number = "22",
publication date ="2010-07-01T00:00:00",
pages = "5895-5920",
itemtype = "ARTICLE",
issn = "0143-1161",
eissn = "1366-5901",
url = "https://www.ingentaconnect.com/content/tandf/tres/2010/00000031/00000022/art00012",
doi = "doi:10.1080/01431161.2010.512314",
author = "Noyel, G. and Angulo, J. and Jeulin, D.",
abstract = "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.",
}