Automatic Detection of Small Icebergs in Fast Ice Using Satellite Wide-Swath SAR Images

Abstract

Automatic detection of icebergs in satellite images is regarded a useful tool to provide information necessary for safety in Arctic shipping or operations over large ocean areas in near-real time. In this work, we investigated the feasibility of automatic iceberg detection in Sentinel-1 Extra Wide Swath (EWS) SAR images which follow the preferred image mode in operational ice charting. As test region, we selected the Barents Sea where the size of many icebergs is on the order of the spatial resolution of the EWS-mode. We tested a new approach for a detection scheme. It is based on a combination of a filter for enhancing the contrast between icebergs and background, subsequent blob detection, and final application of a Constant False Alarm Rate (CFAR) algorithm. The filter relies mainly on the HV-polarized intensity which often reveals a larger difference between icebergs and sea ice or open water. The blob detector identifies locations of potential icebergs and thus shortens computation time. The final detection is performed on the identified blobs using the CFAR algorithm. About 2000 icebergs captured in fast ice were visually identified in Sentinel-2 Multi Spectral Imager (MSI) data and exploited for an assessment of the detection scheme performance using confusion matrices. For our performance tests, we used four Sentinel-1 EWS images. For judging the effect of spatial resolution, we carried out an additional test with one Sentinel-1 Interferometric Wide Swath (IWS) mode image. Our results show that only 8-22 percent of the icebergs could be detected in the EWS images, and over 90 percent of all detections were false alarms. In IWS mode, the number of correctly identified icebergs increased to 38 percent. However, we obtained a larger number of false alarms in the IWS image than in the corresponding EWS image. We identified two problems for iceberg detection: 1) with the given frequency-polarization combination, not all icebergs are strong scatterers at HV-polarization, and (2) icebergs and deformation structures present on fast ice can often not be distinguished since both may reveal equally strong responses at HV-polarization.

Deep Learning for Detecting and Classifying Ocean Objects: Application of YoloV3 for Iceberg–Ship Discrimination

“…It could be argued that too many objects are not labelled, leaving them out of training, but in opposition to this, one could say that too many small objects are included, and these are not of great importance. In Figure 11, training objects are also seen to be located within the large piece of floating ice in the right side of the image, this also raises the question of icebergs being present in other types of ice, such as in this study [23], or if such large pieces of floating ice should be in a class of their own or maybe not be included at all. Since ships are clearly defined objects, populating a SAR dataset with these does not face the same issues as the icebergs.…”

Retrieval of Ocean Wave Heights From Spaceborne SAR in the Arctic Ocean With a Neural Network

“…Figure 1 presents an example of the spatial coverages of the EW data acquired by S1A and S1B within six days in 2019. Additionally, the EW and IW data acquired in the Arctic are generally in polarization combination of co‐polarization and cross‐polarization, dedicated for sea ice monitoring (e.g., Hong & Yang, 2018; Li, Sun, et al., 2020; Soldal et al., 2019). With a spatial resolution of 40 m, S1 EW images can generally yield good observations of ocean waves, as illustrated in Figure 2.…”

Comparison of Target Detectors to Identify Icebergs in Quad-Polarimetric L-Band Synthetic Aperture Radar Data

“…The complex nature of icebergs and sea ice unfortunately does not allow for the discrimination of icebergs with respect to other large sea ice features such as hummocks. A new detector (iDPolRaD) devised by Marino et al [13] has tried to address the problem of detecting small icebergs (<120 m) embedded within sea ice [19].…”

“…However, detectors can still identify icebergs at sizes of approximately 1-4 pixels. Several papers [19,27,28] highlight the ability to detect icebergs of medium to large size (120 m or larger). Nevertheless, as pointed out in Soldal et al [19], work is still required to improve the detection ability for icebergs smaller than 120 m. These icebergs are mostly calving off glacier tongues and ice sheets in the Arctic, and their smaller masses can go undetected due to factors such as the sea state and meteorological conditions [29,30].…”