Comparing Edge Detection Methods based on Stochastic Entropies and Distances for PolSAR Imagery

TL;DR

This paper compares seven edge detection methods for PolSAR images using stochastic entropies and distances, evaluating their accuracy and computational efficiency on real data, and finds certain methods outperform others.

Contribution

It introduces a comprehensive comparison of edge detection techniques based on stochastic measures for PolSAR imagery, highlighting the most effective approaches.

Findings

Bhattacharyya distance method outperforms others

Entropy-based methods show higher accuracy

Detection methods are influenced by image parameters

Abstract

Polarimetric synthetic aperture radar (PolSAR) has achieved a prominent position as a remote imaging method. However, PolSAR images are contaminated by speckle noise due to the coherent illumination employed during the data acquisition. This noise provides a granular aspect to the image, making its processing and analysis (such as in edge detection) hard tasks. This paper discusses seven methods for edge detection in multilook PolSAR images. In all methods, the basic idea consists in detecting transition points in the finest possible strip of data which spans two regions. The edge is contoured using the transitions points and a B-spline curve. Four stochastic distances, two differences of entropies, and the maximum likelihood criterion were used under the scaled complex Wishart distribution; the first six stem from the h-phi class of measures. The performance of the discussed detection methods was quantified and analyzed by the computational time and probability of correct edge detection, with respect to the number of looks, the backscatter matrix as a whole, the SPAN, the covariance an the spatial resolution. The detection procedures were applied to three real PolSAR images. Results provide evidence that the methods based on the Bhattacharyya distance and the difference of Shannon entropies outperform the other techniques.