Open AccessA NOVEL REMOVAL METHOD FOR DENSE STRIPES IN REMOTE SENSING IMAGES
Author(s) -
Xinxin Liu,
Huanfeng Shen,
Qiangqiang Yuan,
Liangpei Zhang,
Qiuming Cheng
Publication year - 2016
Publication title -
isprs annals of the photogrammetry, remote sensing and spatial information sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.356
H-Index - 38
eISSN - 2194-9042
pISSN - 2196-6346
DOI - 10.5194/isprsannals-iii-6-57-2016
Subject(s) - computer science , noise (video) , process (computing) , property (philosophy) , constraint (computer aided design) , construct (python library) , artificial intelligence , computer vision , quality (philosophy) , image (mathematics) , data mining , algorithm , remote sensing , mathematics , geology , philosophy , geometry , epistemology , operating system , programming language
In remote sensing images, the common existing stripe noise always severely affects the imaging quality and limits the related subsequent application, especially when it is with high density. To well process the dense striped data and ensure a reliable solution, we construct a statistical property based constraint in our proposed model and use it to control the whole destriping process. The alternating direction method of multipliers (ADMM) is applied in this work to solve and accelerate the model optimization. Experimental results on real data with different kinds of dense stripe noise demonstrate the effectiveness of the proposed method in terms of both qualitative and quantitative perspectives.