AN IMPROVED DESTRIPING METHOD FOR REMOTE SENSING IMAGES

Zhiping Dan, Xing Wei, Shuifa Sun, and Gang Zhou

Keywords

Agricultural application, stripe noise, higherorder partial differential, unidirectional total variation, SplitBregman

Abstract

Aiming at the problem of the stripe noises of agricultural remote sensing (RS) images, an improved destriping method high order unidirectional total variation with split-bregman iteration (HOUTVSBI) for agricultural RS images is proposed by a higher-order partial differential model. The new method is based on the unidirectional total variation (UTV) model. It could highly performance remove the stripe noises, and effectively suppress the “ripple phenomenon followed by UTV model. The experiments are compared with other traditional algorithms on the satellite images and their simulation images with two kinds of imaging systems. The results show that our method has good adaptability for removing all kinds of periodic and non-periodic random stripe noises and could provide better practicability.

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