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

Wenzhi Liao, Jocelyn Chanussot, Wilfried Philips

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

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).
Original languageEnglish
Title of host publicationMathematical Models for Remote Sensing Image Processing
EditorsGabriele Moser, Josiane Zerubia
Place of PublicationCham
PublisherSpringer
Pages243-275
Number of pages33
ISBN (Print)9783319663302
DOIs
Publication statusPublished - 28 Nov 2017

Publication series

NameSignals and Communication Technology
PublisherSpringer

Keywords

  • remote sensing
  • data fusion
  • hyperspectral pansharpening
  • LiDAR

Cite this

Liao, W., Chanussot, J., & Philips, W. (2017). Remote Sensing Data Fusion: Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion. In G. Moser, & J. Zerubia (Eds.), Mathematical Models for Remote Sensing Image Processing (pp. 243-275). (Signals and Communication Technology ). Springer. https://doi.org/10.1007/978-3-319-66330-2_6