Abstract
Abstract. The detection of icebergs in the open sea, as well as its evolution in displacement and shrinking, is vital for navigation, the study of the evolution of Polar regions, and the Earth climate change, among others. In order to carry out these studies, it is necessary to delimit accurately the icebergs in satellite images, mainly of the Synthetic Aperture Radar (SAR) type. The Adaptive Canny method has shown to be efficient for the detection of edges of objects in SAR images, according to recent publications and conferences. These studies were only carried out for images that had approximately half of each backscatter, without considering that the dimension of the objects can affect the edge detection process. Here, we present the results of the efficiency of the Adaptive Canny method as the size of the object, from which it is intended to extract the contour, decreases. A systematic analysis of the behavior of the method has been performed with objects of variated dimensions, through a Monte Carlo type experiment with synthetic images, where the contours of the figures were extracted with the Adaptive Canny method and compared with the Ground Truth (GT). Then, the method was tested on real images of the Antarctic Ocean, with blocks of ice of different sizes to contrast the results with those obtained with synthetic images.
Highlights
The study of icebergs in the open sea is very important in the current scientific community, including their detection, monitoring, and modeling (Marino, 2018, Shui, Fan, 2018, Nunziata et al, 2018, Rupa et al, 2018, Li et al, 2019, Zakharov et al, 2019)
The Adaptive Canny method was presented in (Nemer Pelliza et al, 2019) where, through artificial intelligence techniques, a set of functions for the calculation of these parameters to be used in Synthetic Aperture Radar (SAR) images are obtained, showing a high rate of efficiency in the process
We present the background of this work, including the characteristics of SAR images, ocean ice, and icebergs
Summary
The study of icebergs in the open sea is very important in the current scientific community, including their detection, monitoring, and modeling (Marino, 2018, Shui, Fan, 2018, Nunziata et al, 2018, Rupa et al, 2018, Li et al, 2019, Zakharov et al, 2019). One of the distinguishing features of SAR images is the presence of speckled multiplicative noise that hinders the process if traditional methods for optical images are used. Due to the complexity of the noise characteristic of these images, several works address the edege detection problem in SAR images using different techniques, including artificial intelligence (Barbat et al, 2019, Li et al, 2019, Nemer et al, 2016, Nemer Pelliza et al, 2019). The Adaptive Canny method was presented in (Nemer Pelliza et al, 2019) where, through artificial intelligence techniques, a set of functions for the calculation of these parameters to be used in SAR images are obtained, showing a high rate of efficiency in the process. The last section analyzes the results, provides conclusions, and proposes future works
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