High-Resolution Optical Reflectometry Network End-to-End Data Link Evaluation System

2021 ◽  
Author(s):  
Keyword(s):  
High Resolution ◽  
Evaluation System ◽  
Data Link ◽  
Optical Reflectometry ◽  
End To End

scholarly journals End-to-end anytime solution for appliance recognition based on high-resolution current sensing with few-shot learning

2020 ◽  
Vol 11 ◽  
pp. 100263
Author(s):  
Radu-Casian Mihailescu ◽  
David Hurtig ◽  
Charlie Olsson
Keyword(s):  
High Resolution ◽  
Current Sensing ◽  
End To End

Transport Canada's Air Traffic Services Simulation and Evaluation Program

1978 ◽  
Vol 7 (1) ◽  
pp. 69-77
Author(s):  
Trevor G. S. Paine
Keyword(s):  
Evaluation System ◽  
Economic Benefits ◽  
Air Traffic ◽  
Way Of Life ◽  
Data Link ◽  
Evaluation Program ◽  
Control Capability ◽  
Simulation And Evaluation ◽  
Integrated Communications ◽  
Simulation Systems

The Canadian Government, through its Department of Transport (Transport Canada) Air Traffic Services organization is responsible for a variety of ATC programs of which the Simulation and Evaluation program is fast attaining its potential as an experimental tool. Simulation and Evaluation has been a way of life in aviation for many many years; in ATC however, it is perhaps only in the last ten years that computer-aided simulators have become an essential part of the service. Since commissioning the Canadian ATC Simulation and Evaluation System in 1976, not only has it been effective and reliable in providing objective experimental results, it has also provided economic benefits to our operations. As a training aid it is yet to attain its full worth. Being the newest of ATC Simulation Systems, the Canadian system incorporates several computers, an integrated communications control capability, an elaborate software package, modern digital radar displays and data link features.

scholarly journals End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

2019 ◽  
Vol 11 (11) ◽  
pp. 1382 ◽  
Author(s):  
Daifeng Peng ◽  
Yongjun Zhang ◽  
Haiyan Guan
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Change Detection ◽  
Land Surface ◽  
Satellite Image ◽  
Feature Representation ◽  
Observation Data ◽  
Feature Maps ◽  
High Resolution Satellite Images ◽  
End To End

Change detection (CD) is essential to the accurate understanding of land surface changes using available Earth observation data. Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community. However, most existing deep learning-based CD methods are implemented by either generating difference images using deep features or learning change relations between pixel patches, which leads to error accumulation problems since many intermediate processing steps are needed to obtain final change maps. To address the above-mentioned issues, a novel end-to-end CD method is proposed based on an effective encoder-decoder architecture for semantic segmentation named UNet++, where change maps could be learned from scratch using available annotated datasets. Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial accuracy. Then, the fusion strategy of multiple side outputs is adopted to combine change maps from different semantic levels, thereby generating a final change map with high accuracy. The effectiveness and reliability of our proposed CD method are verified on very-high-resolution (VHR) satellite image datasets. Extensive experimental results have shown that our proposed approach outperforms the other state-of-the-art CD methods.

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