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

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.