Distance-based edge detection on synthetic aperture radar imagery

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.