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Proceedings Paper

Use of satellite SAR for monitoring rain forest
Author(s): Christopher John Oliver; Kevin O. Grover; Sidnei Sant'Anna; Corina da Costa Freitas
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Paper Abstract

In this paper we compare the capability of Landsat TM optical imagery, JERS L-band and Radarsat C-band SAR for classifying rain forest into forest and not forest categories. In each case, simulated annealing provides the global optimum segmentation of the underlying variable. For the optical image the information is carried by the brightness, for JERS1 by the mean intensity and for Radarsat by the scene texture, where texture can be optimally measured in terms of the normalized log. We demonstrate that JERS1 and Radarsat provide similar classification into forest and not forest categories, when Landsat TM Band 5 imagery is adopted as the reference. Most of the discrepancies arise in regions of regeneration, where the physical difference between the imaging mechanisms of the three sensors has greatest impact.

Paper Details

Date Published: 10 December 1999
PDF: 9 pages
Proc. SPIE 3869, SAR Image Analysis, Modeling, and Techniques II, (10 December 1999); doi: 10.1117/12.373160
Show Author Affiliations
Christopher John Oliver, Defence Evaluation and Research Agency Malvern (United Kingdom)
Kevin O. Grover, Defence Evaluation and Research Agency Malvern (United States)
Sidnei Sant'Anna, Instituto Nacional de Pesquisas Espaciais (Brazil)
Corina da Costa Freitas, Instituto Nacional de Pesquisas Espaciais (Brazil)


Published in SPIE Proceedings Vol. 3869:
SAR Image Analysis, Modeling, and Techniques II
Francesco Posa, Editor(s)

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