Data Restoration and Denoising of Remote Sensing Data

Dear Colleagues,

With very diverse available sensors and platforms, remote sensing is increasingly used in a number of applications with very high societal impact. However, the quality of the data remains a critical issue and can significantly impact the actual performances of the implemented processing algorithms. The noise corrupting the data can have very different causes, depending on the modality of acquisition (radar, optical, hyperspectral, thermal, LiDAR), the characteristics of the sensors and the used platform, from UAVs to satellites. It is, hence, usually highly desirable to reduce the noise before considering specific processing. The Special Issue, focused on noise reduction for remote sensing data, aims at gathering a variety of contributions, embracing this problem in its diversity. As a matter of fact, different natures of noise require different strategies, from physical modelling, sensor design, to advanced statistics and information processing. Contributions are welcome (but are not limited to) for the following topics:

• speckle reduction in active imaging systems (SAR, PolSAR, InSAR, LiDAR, etc.)
• Image restoration and enhancement
• atmospheric correction, sensor calibration, destriping, correction of the smile effect in optical and hypspectral imaging, shadow removal, dehazing
• advanced signal and image processing strategies for noise reduction
• inpainting for the correction of missing/corrupted data

We look forward to hearing from you and receiving your contribution.

Kind regards,

Prof. Jocelyn Chanussot
Dr. Giorgio Licciardi
Dr. Samuel Foucher
Prof. KunShan Chen
Guest Editors

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