Abstract
Edge detection in Synthetic Aperture Radar (SAR) images has been a challenging task due to the speckle noise. Ratio-based edge detectors are robust operators for SAR images that provide constant false alarm rates, but they are only optimal for step edges. Edge detectors developed by the phase congruency model provide the identification of different types of edge features, but they suffer from speckle noise. By combining the advantages of the two edge detectors, we propose a SAR phase congruency detector (SAR-PC). Firstly, an improved local energy model for SAR images is obtained by replacing the convolution of raw image and the quadrature filters by the ratio responses. Secondly, a new noise level is estimated for the multiplicative noise. Substituting the SAR local energy and the new noise level into the phase congruency model, SAR-PC is derived. Edge response corresponds to the max moment of SAR-PC. We compare the proposed detector with the ratio-based edge detectors and the phase congruency edge detectors. Receiver Operating Characteristic (ROC) curves and visual effects are used to evaluate the performance. Experimental results of simulated images and real-world images show that the proposed edge detector is robust to speckle noise and it provides a consecutive edge response.
Highlights
Synthetic Aperture Radar (SAR) images have been widely used because they are all-time, all-weather, and high-resolution
The SAR local energy model has theoretically solved the problem of speckle noise; we propose a new estimation of noise level that is adapted to the calculation of SAR local energy
Detector, if it is used directly, we name it the Phase Congruency (PC) detector and if it is used after taking the logarithm of the raw image, we name it the Log-PC detector
Summary
Synthetic Aperture Radar (SAR) images have been widely used because they are all-time, all-weather, and high-resolution. As the fundamental task of image processing, edge detection from SAR images is becoming increasingly important due to the many applications of SAR images. Many studies have been conducted to extract edges from optical images. SAR images are usually corrupted by strong speckle noise. The speckle is generally modelled as a multiplicative noise [1]. Due to the multiplicative property of speckle, optimal edge detectors for optical images produce numerous false edge pixels. It is important and difficult to detect robust edge response for SAR imagery. Additive noise exists in SAR images, but it is a relatively negligible effect in the edge detection task [2]
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