Benutzer: Gast  Login
Autor(en):
Ghamisi, P.; Höfle, B.; Zhu, X. X. 
Titel:
Hyperspectral and {LiDAR} {Data} {Fusion} {Using} {Extinction} {Profiles} and {Deep} {Convolutional} {Neural} {Network} 
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...    »
 
Stichworte:
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) 
Zeitschriftentitel:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 
Jahr:
2016 
Band / Volume:
PP 
Heft / Issue:
99 
Seitenangaben Beitrag:
1--14 
Print-ISSN:
1939-1404 
Hinweise:
00000