Title
Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images
Abstract
Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications.
Year
DOI
Venue
2020
10.1109/TGRS.2019.2947599
IEEE Transactions on Geoscience and Remote Sensing
Keywords
DocType
Volume
Destriping,hyperspectral remote sensing image,multiplicative noise,radiometric normalization
Journal
58
Issue
ISSN
Citations 
4
0196-2892
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Hao Cui123.62
Peng Jia202.70
Guo Zhang3010.48
Yong-hua Jiang4225.41
litao li551.87
Jingyin Wang611.71
Xiao-Yun Hao700.34