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Noise Filtering in High-Resolution Satellite Images Using Composite Multiresolution Transforms

  • Rizwan Ahmed AnsariEmail author
  • Krishna Mohan Buddhiraju
Original Article
  • 3 Downloads

Abstract

This contribution proposes a multiresolution analysis (MRA)-based composite technique for image restoration by noise filtering in satellite images. Multiresolution techniques provide a coarse–fine and scale-invariant decomposition of images for analysis and interpretation. MRA methods effectively handle the noise because of their multiscale feature. This study presents a scheme based on the combination of wavelet-, contourlet- and curvelet-based transforms as effective tool for noise filtering in satellite images. The proposed method is applied to the problem of restoring an image from noisy data and effects of denoising are compared. Several comparison experiments with state-of-the-art noise filtering schemes are conducted. The composite approach of curvelet and wavelet is found to be more effective than the others based on the set of evaluation measures like peak signal–noise ratio, mean-squared error, edge-enhancing index and mean–standard deviation ratio across edges. The results are illustrated using high-resolution satellite data, such as Quickbird and Worldview-2 images. Such high-resolution images are more likely to be noisy due to the short observation time over the target in contrast to images from low-resolution sensors.

Keywords

Image restoration Noise filtering Multiresolution analysis Wavelet Curvelet Contourlet 

Zusammenfassung

Rauschfilterung in hochaufgelösten Satellitenbildern mittels zusammengesetzter Multiresolution-Transformationen. In diesem Beitrag wird eine auf Multiresolution Analysis (MRA) basierende Verbundtechnik für die Bildwiederherstellung durch Rauschfilterung in Satellitenbildern vorgeschlagen. Multiresolution-Techniken ermöglichen eine grobe bis feine und maßstabsunabhängige Zerlegung von Bildern zu deren Analyse und Interpretation. MRA-Methoden behandeln das Rauschen aufgrund ihres Multiskalenansatzes effektiv. Diese Studie stellt ein Schema vor, das auf der Kombination von Wavelet-, Contourlet- und Curvelet-Transformationen als effektive Werkzeuge für die Rauschfilterung in Satellitenbildern beruht. Das vorgeschlagene Verfahren wird auf das Problem der Wiederherstellung eines Bildes aus verrauschten Daten angewendet. Die unterschiedlichen Auswirkungen der Verfahren zur Entrauschung werden verglichen. Darüber hinaus werden verschiedene Vergleichsexperimente mit gängigen Rauschfilteransätzen durchgeführt. In den Vergleichen wird der zusammengesetzte Ansatz aus Curvelet- und Wavelet-Transformationen auf Basis der verwendeten Bewertungsmaßstäbe als besonders wirksam identifiziert. Die Ergebnisse werden am Beispiel von Satellitenbildern mit hoher Auflösung, konkret mit Quickbird- und Worldview-2-Bildern, veranschaulicht. Aufgrund der kurzen Beobachtungszeit über dem Ziel sind diese Daten deutlich rauschanfälliger im Vergleich zu Bildern mit niedriger Auflösung.

Notes

Acknowledgements

The authors would like to thank the anonymous reviewers and the editor for careful reading and valuable suggestions to improve the manuscript.

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Copyright information

© Deutsche Gesellschaft für Photogrammetrie, Fernerkundung und Geoinformation (DGPF) e.V. 2019

Authors and Affiliations

  1. 1.Satellite Image Processing Lab (SIP LAB), Centre of Studies in Resources Engineering (CSRE)Indian Institute of Technology BombayMumbaiIndia

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