On the accuracy of snow cover segmentation in optical satellite images

Author

Luca, D. ; Seidel, K. ; Datcu, M.

Author_Institution

Inst. for Commun. Technol., Swiss Fed. Inst. of Technol., Zurich, Switzerland

Volume

1

fYear

1997

fDate

3-8 Aug 1997

Firstpage

411

Abstract

The authors make a comparison of the state of the art algorithms for snow areas segmentation in optical satellite images. The comparison address the accuracy of the “forward model” used and the informational theoretical aspects characterising the detection/segmentation algorithms. They also, comparatively, introduce and a new approach: the segmentation of the snow cover as ill-posed inverse problem and its solution in the frame of the Bayesian inference

Keywords

Bayes methods; geophysical signal processing; hydrological techniques; image segmentation; inverse problems; remote sensing; snow; Bayes method; Bayesian inference; accuracy; algorithm; forward model; hydrology; ill-posed inverse problem; image segmentation; land surface; measurement technique; optical imaging; optical satellite image; remote sensing; snow cover; snowcover; terrain mapping; Bayesian methods; Data mining; Image analysis; Image segmentation; Inference algorithms; Inverse problems; Layout; Optical sensors; Remote sensing; Snow;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing, 1997. IGARSS '97. Remote Sensing - A Scientific Vision for Sustainable Development., 1997 IEEE International

Print_ISBN

0-7803-3836-7

Type

conf

DOI

10.1109/IGARSS.1997.615900

Filename

615900