Bandwidth Compression Of Synthetic Aperture Radar Imagery By Quantization Of Raw Radar Data

1977 ◽  
Author(s):  
Richard G. Lipes ◽  
Stanley A. Butman
Keyword(s):  
Synthetic Aperture Radar ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery ◽  
Bandwidth Compression ◽  
Aperture Radar

scholarly journals Distance-based edge detection on synthetic aperture radar imagery

2021 ◽  
Author(s):  
ADC Nascimento ◽  
KF Silva ◽  
Alejandro Frery
Keyword(s):  
Synthetic Aperture Radar ◽  
Asymptotic Properties ◽  
Speckle Noise ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Edge Detectors ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery ◽  
Aperture Radar

Synthetic aperture radar is an efficient remote sensing tool by producing high spacial resolution images. But, synthetic aperture radar data suffer speckle noise effect that difficult their processing (for example, making boundary detection). We propose and assess edge detectors for synthetic aperture radar imagery based on stochastic distances between models.These edge detectors stem from generalized divergences with good asymptotic properties. Results reveal that divergence-based detectors can outperform the likelihood-based counterpart.

scholarly journals Distance-based edge detection on synthetic aperture radar imagery

2021 ◽  
Author(s):  
ADC Nascimento ◽  
KF Silva ◽  
Alejandro Frery
Keyword(s):  
Synthetic Aperture Radar ◽  
Asymptotic Properties ◽  
Speckle Noise ◽  
Radar Data ◽  
Synthetic Aperture ◽  
Synthetic Aperture Radar Data ◽  
Edge Detectors ◽  
Radar Imagery ◽  
Synthetic Aperture Radar Imagery ◽  
Aperture Radar

Synthetic aperture radar is an efficient remote sensing tool by producing high spacial resolution images. But, synthetic aperture radar data suffer speckle noise effect that difficult their processing (for example, making boundary detection). We propose and assess edge detectors for synthetic aperture radar imagery based on stochastic distances between models.These edge detectors stem from generalized divergences with good asymptotic properties. Results reveal that divergence-based detectors can outperform the likelihood-based counterpart.

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