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Proceedings Paper

Synergistic combination technique for SAR image classification
Author(s): Bhaskar Burman
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Paper Abstract

Classification of earth terrain from satellite radar imagery represents an important and continually developing application of microwave remote sensing. The basic objective of this paper is to derive more information, through combining, than is present in any individual element of input data. Multispectral data has been used to provide complementary information so as to utilize a single SAR data for the purpose of land-cover classification. More recently neural networks have been applied to a number of image classification problems and have shown considerable success in exceeding the performance of conventional algorithms. In this work, a comparison study has been carried out between a conventional Maximum Likelihood (ML) classifier and a neural network (back-error-propagation) classifier in terms of classification accuracy. The results reveal that the combination of SAR and MSS data of the same scene produced better classification accuracy than either alone and the neural network classification has an edge over the conventional classification scheme.

Paper Details

Date Published: 2 July 1998
PDF: 12 pages
Proc. SPIE 3372, Algorithms for Multispectral and Hyperspectral Imagery IV, (2 July 1998); doi: 10.1117/12.312594
Show Author Affiliations
Bhaskar Burman, Defence Electronics Applications Lab. (India)


Published in SPIE Proceedings Vol. 3372:
Algorithms for Multispectral and Hyperspectral Imagery IV
Sylvia S. Shen; Michael R. Descour, Editor(s)

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