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Improved radiometric normalization for land cover change detection: an automated relative correction with artificial neural network

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3 Author(s)
Velloso, M.L.F. ; Dept. of Electron. Eng., Rio de Janeiro State Univ., Brazil ; de Souzal, F.J. ; Simoes, M.

Digital change detection methods have been broadly divided into either pre-classification spectral change detection or post-classification change detection. Since all spectral change detection methods are based on pixel-wise plus operations or scene-wise plus pixel-wise operations, accuracy in image registration and scene-to-scene radiometric normalization is more critical for these methods than for other methods. A wide range of algorithms has been developed to adjust linear models. This paper proposes an automated radiometric normalization process that uses an artificial neural network to adjust a non-linear mapping to minimize the effects of the influences of radiometric differences on image interpretation and classification.

Published in:

Geoscience and Remote Sensing Symposium, 2002. IGARSS '02. 2002 IEEE International  (Volume:6 )

Date of Conference:

24-28 June 2002