Next Article in Journal
Assessment of IMERG Precipitation Estimates over Europe
Next Article in Special Issue
An Improved Mapping with Super-Resolved Multispectral Images for Geostationary Satellites
Previous Article in Journal
Retrieval of the Fraction of Radiation Absorbed by Photosynthetic Components (FAPARgreen) for Forest Using a Triple-Source Leaf-Wood-Soil Layer Approach
Previous Article in Special Issue
Fusion of Various Band Selection Methods for Hyperspectral Imagery
Article

Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition

1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS), Wuhan University, Wuhan 430079, China
2
School of Computer, Hubei University of Technology, Wuhan 430068, China
3
Collaborative Innovation Center of Geospatial Technology, 129 Luoyu Road, Wuhan 430079, China
4
School of Resource and Environment Sciences, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2472; https://doi.org/10.3390/rs11212472
Received: 28 August 2019 / Revised: 21 October 2019 / Accepted: 21 October 2019 / Published: 23 October 2019
(This article belongs to the Special Issue Quality Improvement of Remote Sensing Images)
Stripes are common in remote sensing imaging systems equipped with multichannel time delay integration charge-coupled devices (TDI CCDs) and have different scale characteristics depending on their causes. Large-scale stripes appearing between channels are difficult to process by most current methods. The framework of column-by-column nonuniformity correction (CCNUC) is introduced to eliminate large-scale stripes. However, the worst problem of CCNUC is the unavoidable cumulative error, which will cause an overall color cast. To eliminate large-scale stripes and suppress the cumulative error, we proposed a destriping method via unidirectional multiscale decomposition (DUMD). The striped image was decomposed by constructing a unidirectional pyramid and making difference maps layer by layer. The highest layer of the pyramid was processed by CCNUC to eliminate large-scale stripes, and multiple cumulative error suppression measures were performed to reduce overall color cast. The difference maps of the pyramid were processed by a designed filter to eliminate small-scale stripes. Experiments showed that DUMD had good destriping performance and was robust with respect to different terrains. View Full-Text
Keywords: destriping; column-by-column nonuniformity correction (CCNUC); cumulative error; unidirectional multiscale decomposition destriping; column-by-column nonuniformity correction (CCNUC); cumulative error; unidirectional multiscale decomposition
Show Figures

Graphical abstract

MDPI and ACS Style

He, L.; Wang, M.; Chang, X.; Zhang, Z.; Feng, X. Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition. Remote Sens. 2019, 11, 2472. https://doi.org/10.3390/rs11212472

AMA Style

He L, Wang M, Chang X, Zhang Z, Feng X. Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition. Remote Sensing. 2019; 11(21):2472. https://doi.org/10.3390/rs11212472

Chicago/Turabian Style

He, Luxiao, Mi Wang, Xueli Chang, Zhiqi Zhang, and Xiaoxiao Feng. 2019. "Removal of Large-Scale Stripes Via Unidirectional Multiscale Decomposition" Remote Sensing 11, no. 21: 2472. https://doi.org/10.3390/rs11212472

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop