TY - CHAP
T1 - Remote Sensing Data Fusion
T2 - Guided Filter-Based Hyperspectral Pansharpening and Graph-Based Feature-Level Fusion
AU - Liao, Wenzhi
AU - Chanussot, Jocelyn
AU - Philips, Wilfried
PY - 2017/11/28
Y1 - 2017/11/28
N2 - 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).
AB - 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).
KW - remote sensing
KW - data fusion
KW - hyperspectral pansharpening
KW - LiDAR
UR - http://hdl.handle.net/1854/LU-8553078
U2 - 10.1007/978-3-319-66330-2_6
DO - 10.1007/978-3-319-66330-2_6
M3 - Chapter
SN - 9783319663302
T3 - Signals and Communication Technology
SP - 243
EP - 275
BT - Mathematical Models for Remote Sensing Image Processing
A2 - Moser, Gabriele
A2 - Zerubia, Josiane
PB - Springer
CY - Cham
ER -