Synthetic Aperture Radar Image Processing Techniques Development and Evaluation.

1977 ◽  
Author(s):  
E. J. Dragavon ◽  
M. L. Hershberger ◽  
P. J. Whitt
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Image Processing Techniques ◽  
Processing Techniques ◽  
Aperture Radar

One-shot learning technique regression of reconfigurable learning network for generative modeling in interconnected imaging infrastructure

2019 ◽  
pp. 002072091989590
Author(s):  
Ankush Rai ◽  
R. Jagadeesh Kannan
Keyword(s):  
Image Processing ◽  
Synthetic Aperture Radar ◽  
Target Recognition ◽  
Automatic Target Recognition ◽  
Object Identification ◽  
Synthetic Aperture Radar Image ◽  
Radar Image ◽  
Synthetic Aperture ◽  
Learning Network ◽  
Aperture Radar

Learning visual models of object classes conventionally require hundreds or a large number of training samples. Conventional gradient-based approaches for target recognition require lot of data to be trained on and require exhaustive training with high computational expense. Hence, when a new condition or untrained data is encountered, such systems inadequately misconfigure newly learned feature sets in the trained model. This misconfigures the structure of re-learned features and is then carried out in subsequent recognition stages. Thus, a development in this scenario with low training time will allow us to fend of this disadvantage. This study presents a new automatic target recognition framework that gives the enhanced performance of target-recognition system when several imaging sensors are connected with one another. This is in contrast with traditional automatic target recognition frameworks, which utilizes one-on-one computational model over synthetic-aperture radar image-processing systems. The work comprises of a learning-based classifications strategy when dealing with sharing of learned parameters over the network to discern critical changes in target-recognition performance by utilizing a novel one-shot learning-based reconfigurable learning network for image processing platform. This upgrades the networked connected CCTV and multiview synthetic-aperture radar image object identification and recognition process.

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