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Proceedings Paper

Multisources classification: application to temporal refinement of forest cover using SPOT and ERS/SAR data
Author(s): Laurent Peytavin; F. Dansaert; C. Rhin
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Paper Abstract

This work aimed at developing new multi-sources multi-scales classification techniques bringing solutions when dealing with time series of SPOT and ERS/SAR data. The developed multi-sources classification is a contextual classification based on Markov Random Fields (used as regularization models) and on simple or neural network driven multi-scales relaxation process. The main advantage is that it manages the absence or the distortion of optical classification parameters due to partial cloud cover in SPOT data. The results showed that the multi-sources multi-scales regularization induced a nearly total recovery of the forest hidden by clouds. Compared to a mono-source classification on ERS data the classification precision is improved. Compared to a monosource classification on SPOT data the results are significantly improved as soon as the local cloud cover rate is more than 7%. Therefore, when applied to time series of SPOT, LANDSAT TM and ERS/SAR data on the same area this process is able to provide an along time refined measurement of the forest cover, far less biased by noisy SPOT data. This is at least proved for wide-ranging environmental phenomena classification.

Paper Details

Date Published: 17 November 1995
PDF: 10 pages
Proc. SPIE 2579, Image and Signal Processing for Remote Sensing II, (17 November 1995); doi: 10.1117/12.226858
Show Author Affiliations
Laurent Peytavin, Matra Cap Systemes (France)
F. Dansaert, Matra Cap Systemes (France)
C. Rhin, Matra Cap Systemes (France)

Published in SPIE Proceedings Vol. 2579:
Image and Signal Processing for Remote Sensing II
Jacky Desachy, Editor(s)

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