Generation of synthetic infrared remote-sensing scenes of wildland fire

2009 ◽  
Vol 18 (3) ◽  
pp. 302 ◽  
Author(s):  
Zhen Wang ◽  
Anthony Vodacek ◽  
Janice Coen

We describe a method for generating synthetic infrared remote-sensing scenes of wildland fire. These synthetic scenes are an important step in data assimilation, which is defined as the process of incorporating new data into an executing model. In our case, this is a fire propagation model. The scenes are built using the surface output of fire position from a fire propagation code and prior knowledge of fire physics and behavior to estimate the shape of the flame. The scene radiance is then estimated by employing a physics-based ray-tracing model called DIRSIG to render the radiation that would reach a sensor on an airborne platform. Values of the Fire Radiated Energy calculated from the synthetic radiance scene compare well with previously published values, providing validation of the method.

Frequenz ◽  
2012 ◽  
Vol 66 (7-8) ◽  
Author(s):  
Malgorzata Janson ◽  
Juan Pontes ◽  
Thomas Fügen ◽  
Thomas Zwick

AbstractThis paper presents a computationally effective approach for including dense multipath components in ray tracing simulations of ultra wideband (UWB) channels. Through a combination of a standard ray tracing model with a simple geometric-stochastic model realistic scenario-specific simulations are possible. The frequency and direction selectivity of the channel are reproduced accurately by the model. The structure and parameters of the stochastic part of the model are derived from measurements in the FCC-UWB frequency range. Compared to conventional ray tracing simulations the proposed model reduces considerably the differences between simulated and measured channel characteristics.


Author(s):  
Masataka YAMAGUCHI ◽  
Hirokazu NONAKA ◽  
Yoshio HATADA ◽  
Yoshihiro UTSUNOMIYA ◽  
Kunimitsu INOUCHI ◽  
...  

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3848
Author(s):  
Wei Cui ◽  
Meng Yao ◽  
Yuanjie Hao ◽  
Ziwei Wang ◽  
Xin He ◽  
...  

Pixel-based semantic segmentation models fail to effectively express geographic objects and their topological relationships. Therefore, in semantic segmentation of remote sensing images, these models fail to avoid salt-and-pepper effects and cannot achieve high accuracy either. To solve these problems, object-based models such as graph neural networks (GNNs) are considered. However, traditional GNNs directly use similarity or spatial correlations between nodes to aggregate nodes’ information, which rely too much on the contextual information of the sample. The contextual information of the sample is often distorted, which results in a reduction in the node classification accuracy. To solve this problem, a knowledge and geo-object-based graph convolutional network (KGGCN) is proposed. The KGGCN uses superpixel blocks as nodes of the graph network and combines prior knowledge with spatial correlations during information aggregation. By incorporating the prior knowledge obtained from all samples of the study area, the receptive field of the node is extended from its sample context to the study area. Thus, the distortion of the sample context is overcome effectively. Experiments demonstrate that our model is improved by 3.7% compared with the baseline model named Cluster GCN and 4.1% compared with U-Net.


1997 ◽  
Vol 69 (2) ◽  
pp. 118-129 ◽  
Author(s):  
Arjun S. Bangalore ◽  
Gary W. Small ◽  
Roger J. Combs ◽  
Robert B. Knapp ◽  
Robert T. Kroutil ◽  
...  

2021 ◽  
Vol 131 ◽  
pp. 126389
Author(s):  
Mengjie Hou ◽  
Fei Tian ◽  
S. Ortega-Farias ◽  
C. Riveros-Burgos ◽  
Tong Zhang ◽  
...  

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