Abstract
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 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 in order to minimize the effects of the influences of radiometric differences on image interpretation and classification. In order to investigate how the proposed normalization improves the performance classification, and assess the effectiveness of this technique, we carried out classification experiments on three image sets, and compare their results.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.