Surface and Buried Landmine Scene Generation and Validation Using the Digital Imaging and Remote Sensing Image Generation (DIRSIG) Model

2000 ◽  
Author(s):  
Erin D. Peterson ◽  
Scott D. Brown ◽  
Timothy J. Hattenberger ◽  
John R. Schott
Keyword(s):  
Remote Sensing ◽  
Digital Imaging ◽  
Remote Sensing Image ◽  
Image Generation

Digital imaging and remote sensing image generator (DIRSIG) as applied to NVESD sensor performance modeling

2016 ◽  
Author(s):  
Kimberly E. Kolb ◽  
Hee-sue S. Choi ◽  
Balvinder Kaur ◽  
Jeffrey T. Olson ◽  
Clayton F. Hill ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Digital Imaging ◽  
Performance Modeling ◽  
Remote Sensing Image ◽  
Sensor Performance ◽  
Image Generator

Remote Sensing Image Generation From Audio

2020 ◽  
pp. 1-5
Author(s):  
Zhiyuan Zheng ◽  
Jun Chen ◽  
Xiangtao Zheng ◽  
Xiaoqiang Lu
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Generation

Research on Remote Sensing Image Generation Modeling in the Visible Regions

2011 ◽  
Vol 40 (7) ◽  
pp. 1106-1111
Author(s):  
PENG Xiaodong ◽  
LIU Bo ◽  
MENG Xin
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Image Generation

scholarly journals A joint manifold leaning-based framework for heterogeneous upstream data fusion

2018 ◽  
Vol 12 (4) ◽  
pp. 311-332 ◽  
Author(s):  
Dan Shen ◽  
Erik Blasch ◽  
Peter Zulch ◽  
Marcello Distasio ◽  
Ruixin Niu ◽  
...  
Keyword(s):  
Radio Frequency ◽  
Target Tracking ◽  
Manifold Learning ◽  
Digital Imaging ◽  
Remote Sensing Image ◽  
Sensor Data ◽  
Image Generation ◽  
Doppler Data ◽  
Improved Accuracy ◽  
Target Locations

A joint manifold learning fusion (JMLF) approach is proposed for nonlinear or mixed sensor modalities with large streams of data. The multimodal sensor data are stacked to form joint manifolds, from which the embedded low intrinsic dimensionalities are discovered for moving targets. The intrinsic low dimensionalities are mapped to resolve the target locations. The JMLF framework is tested on digital imaging and remote sensing image generation scenes with mid-wave infrared (WMIR) data augmented with distributed radio-frequency (RF) Doppler data. Eight manifold learning methods are explored to train the system with the neighborhood preserving embedding showing promise for robust target tracking using video–radio-frequency fusion. The JMLF method shows a 93% improved accuracy as compared to a standard target tracking (e.g., Kalman-filter based) approach.

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