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Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion

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Mathematical Models for Remote Sensing Image Processing

Part of the book series: Signals and Communication Technology ((SCT))

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Abstract

Recent advances in remote sensing technology have led to an increased availability of a multitude of satellite and airborne data sources, with increasing resolution. The term resolution here includes spatial and spectral resolutions. Additionally, at lower altitudes, airplanes and Unmanned Aerial Vehicles (UAVs) can deliver very high-resolution data from targeted locations. Remote sensing acquisitions employ both passive (optical and thermal range, multispectral, and hyperspectral) and active devices such as Synthetic Aperture Radar (SAR) and Light Detection and Ranging (LiDAR). Diverse information of the Earth’s surface can be obtained from these multiple imaging sources. Optical and SAR characterize the surface of the ground, LiDAR provides the elevation, while multispectral and hyperspectral sensors reveal the material composition. These multisource remote sensing images, once combined/fused together, provide a more comprehensive interpretation of land cover/use (urban and climatic changes), natural disasters (floods, hurricanes, and earthquakes), and potential exploitation (oil fields and minerals). However, automatic interpretation of remote sensing data remains challenging. Two fundamental problems in data fusion of multisource remote sensing images are (1) differences in resolution hamper the ability to fastly interpret multisource remote sensing images and (2) there is no clear methodology yet on combining the diverse information of different data sources. In this chapter, we will introduce our recent solutions for these two problems, with an introduction on signal-level fusion (hyperspectral image pansharpening) first, followed by feature-level fusion (graph-based fusion model for multisource data classification).

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Notes

  1. 1.

    http://www.grss-ieee.org/community/technical-committees/data-fusion/2014-ieee-grss-data-fusion-contest/.

  2. 2.

    http://www.grss-ieee.org/community/technical-committees/data-fusion/2013-ieee-grss-data-fusion-contest/.

  3. 3.

    With faster algorithms (e.g., K-D trees) than direct nearest neighbours searching, the complexity can be reduced. More details on efficient representation and search techniques for large data sets can be found in Chap. 2.

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Acknowledgements

The authors would like to thank Telops Inc. (Québec, Canada), the Hyperspectral Image Analysis group and the NSF Funded Center for Airborne Laser Mapping (NCALM) at the University of Houston, and Prof. Paolo Gamba from the University of Pavia (Italy) for providing the data sets used in this Chapter. This work was supported by the Fund for Scientific Research in Flanders (FWO) project G037115N “Data Fusion for Image Analysis in Remote Sensing.” Wenzhi Liao is a postdoctoral fellow of the Research Foundation Flanders (FWO-Vlaanderen) and acknowledges its support.

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  1. Department of Telecommunications and Information Processing, Ghent University, Sint-Pietersnieuwstrat 41, 9000, Ghent, Belgium

    Wenzhi Liao & Wilfried Philips

  2. GIPSA-Lab, Grenoble Institute of Technology, 38031, Grenoble, France

    Jocelyn Chanussot

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  1. Wenzhi Liao
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  1. University of Genoa, Genoa, Italy

    Gabriele Moser

  2. Université Côte d’Azur, Inria, Sophia Antipolis Cedex, France

    Josiane Zerubia

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Liao, W., Chanussot, J., Philips, W. (2018). Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion. In: Moser, G., Zerubia, J. (eds) Mathematical Models for Remote Sensing Image Processing. Signals and Communication Technology. Springer, Cham. https://doi.org/10.1007/978-3-319-66330-2_6

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