Automatic Detection of the Ice Edge in SAR Imagery Using Curvelet Transform and Active Contour

Abstract

A novel method based on the curvelet transform and active contour method to automatically detect the ice edge in Synthetic Aperture Radar (SAR) imagery is proposed. The method utilizes the location of high curvelet coefficients to determine regions in the image likely to contain the ice edge. Using an ice edge from passive microwave sea ice concentration for initialization, these regions are then joined using the active contour method to obtain the final ice edge. The method is evaluated on four dual polarization SAR scenes of the Labrador sea. Through comparison of the ice edge with that from image analysis charts, it is demonstrated that the proposed method can detect the ice edge effectively in SAR images. This is particularly relevant when the marginal ice zone is diffuse or the ice is thin, and using the definition of ice edge from the passive microwave ice concentration would underestimate the ice edge location. It is expected that the method may be useful for operations in marginal ice zones, such as offshore drilling, where a high resolution estimate of the ice edge location is required. It could also be useful as a first guess for an ice analyst, or for the assimilation of SAR data.

Sea Ice Concentration Estimation during Freeze-Up from SAR Imagery Using a Convolutional Neural Network

“…Many of these studies use 'engineered' or hand-crafted features, which are features designed and selected to carry out a specific task. Examples include, the HH autocorrelation, normalized polarization difference and cross-polarization ratio all of which have been used in ice concentration estimation [3,4], grey level co-occurrence matrix features, Gabor filters and Markov random fields, which have been used to classify imagery into ice type and ice/water [5][6][7][8], and curvelet features used to locate the ice edge [9]. One of the challenges with using a set of engineered features to automatically extract information from SAR imagery is the difficulty of developing a set of robust features that can be applied to different geographic regions and seasons and for different imaging geometries.…”

Satellite SAR Data-based Sea Ice Classification: An Overview

“…TerraSAR-X (VV)/texture~8 3% for three ice classes and OW; estimated using test datasets from 3 scenes Ressel et al, 2015 [52] TerraSAR-X (HH, VV)/co-pol ratio, polarimetric features~9 5% for three ice classes and OW; estimated using test datasets from 3 scenes Ressel et al, 2016 [45] ALOS-2 and S1(HH, HV)/co-and cross-pol ratios, incidence angle, autocorrelation (texture) S1: 87.23% and 89.33%; ALOS-2: 84.17% and 85.2% for three classes compared with manual ice charts and the AMSR2 data; estimated using independent 12 S1 and 13 ALOS-2 scenes respectively Hong and Yang 2018 [95] Numerous attempts have been made to develop an automatic algorithm for ice/ocean discrimination using SAR data [28,31,48,90,96]. Based on the early studies in [80,86], a novel method of automatic detection of the ice edge in dual-polarized RS-2 SAR images was proposed to employ the curvelet transform and active contour method [97]. Comparison of the ice edge with that from manually analyzed SAR images demonstrates the effective detection of the ice edge in SAR images by the proposed method.…”

Improved Retrieval of Ice and Open Water From Sequential RADARSAT-2 Images

“…Therefore, the proposed approach might not be optimal for other operational applications such as ice edge detection. Perhaps, other algorithms based on curvelet transform [20] or image classification [9] might be more efficient to accurately locate the ice edge. Also, use of other verification metrics such as one described in [21] might be more suitable for marginal ice zones.…”