User: Guest  Login
Title:

Hyperspectral and {LiDAR} {Data} {Fusion} {Using} {Extinction} {Profiles} and {Deep} {Convolutional} {Neural} {Network}

Author(s):
Ghamisi, P.; Höfle, B.; Zhu, X. X.
Abstract:
This paper proposes a novel framework for the fusion of hyperspectral and light detection and ranging-derived rasterized data using extinction profiles (EPs) and deep learning. In order to extract spatial and elevation information from both the sources, EPs that include different attributes (e.g., height, area, volume, diagonal of the bounding box, and standard deviation) are taken into account. Then, the derived features are fused via either feature stacking or graph-based feature fusion. Final...     »
Keywords:
Sensors, Laser radar, feature extraction, Data Mining, hyperspectral imaging, Convolutional neural network (CNN), deep learning, extinction profile (EP), graph-based feature fusion (GBFF), hyperspectral, light detection and ranging (LiDAR), random forest (RF), support vector machines (SVMs)
Journal title:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Year:
2016
Journal volume:
PP
Journal issue:
99
Pages contribution:
1--14
Fulltext / DOI:
doi:10.1109/JSTARS.2016.2634863
Print-ISSN:
1939-1404
Notes:
00000
 BibTeX