Non-parametric smoothing for relative radiometric correction on remotely sensed data

Maria Luiza F. VellosoFlávio J. de Souza

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 automatically extracts the training dataset and uses a non-parametric smoother to adjust a non-linear mapping to minimize the effects of the influences of radiometric differences on image interpretation and classification.

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