Conference Agenda

Accurate discrimination of ocean targets using satellite images is crucial for marine safety, environmental monitoring, dark vessel detection, and search and rescue operations. Artificial intelligence technologies are rapidly advancing as state-of-the-art solutions for computer vision problems, including satellite imagery target classification. This research assesses the capability of machine learning (ML) for ocean target discrimination using SAR images. Unlike other studies focusing on binary iceberg-ship classification, this paper goes a step further to investigate the opportunity for multi-class discrimination between icebergs, ships, and false alarms, both within and outside sea ice. The proposed approach enables the fully automated elimination of false alarms while accurately classifying icebergs and ships. As part of a research initiative, the first large dataset of ocean targets was compiled and utilized to train an ML model. The targets were detected in RADARSAT Constellation Mission (RCM) images over Canadian waters. During the evaluation phase, the model achieved classification accuracies of 93% for binary classification and 95% for three-class discrimination. The robustness of the fully automated approach was further validated through an additional test, yielding an overall accuracy of 91%. Moreover, the system exhibited high reliability in reducing false alarms, correctly identifying 96% of them. The implementation of the developed algorithms significantly enhances the efficiency of target detection and classification processes, thereby reducing the workload of human analysts. Such advancements are especially significant in light of the rapidly increasing volume of satellite data and the growing demand for automated, scalable solutions in maritime surveillance.

Synthetic aperture radar (SAR) is the primary data source for operational sea ice monitoring, providing coverage independent of illumination or weather conditions. However, annotation scarcity and the domain gap between sea ice and land based scenes hinder the direct reuse of existing pretrained models. Recent studies \cite{Allen2023,Wang2025} point toward self-supervised learning (SSL) as a way to leverage abundant unlabeled SAR imagery. In particular, masked autoencoders (MAE) \cite{He_2022_CVPR} have shown promise in remote sensing contexts by reconstructing masked inputs and learning transferable representations. We investigate whether MAE pre-training is sufficient to yield a foundation model transferable across multiple downstream sea ice tasks: concentration (SIC), stage of development (SOD), and floe size (FLOE).

Since the 1980s, the Canadian Ice Service (CIS) has provided iceberg information for navigation in the North Atlantic. Following the breakup of the Milne Ice Shelf on Northern Ellesmere Island in 2020 and increasing risk to ships navigating bergy waters in the Canadian Arctic Archipelago and Beaufort Sea, CIS has initiated two projects with the goal of improving their operational iceberg monitoring program. The first combines RCM imagery and in-situ observations to evaluate the applicability of existing automated detection and modelling methods for monitoring icebergs and ice islands drifting in open water in the western Arctic. The second explores the use of high-resolution RCM imagery (5M and 16M) for emergency response iceberg monitoring.

Synthetic Aperture Radar (SAR) image segmentation underpins target detection, land cover classification, and environmental monitoring, yet remains challenging due to speckle, non-Gaussian backscatter statistics, and outliers. This paper presents a comparative evaluation of mixture-model–based segmentation tailored to SAR, with a focus on Radarsat Constellation Mission (RCM) imagery. We propose a segmentation algorithm that selects one of three statistical mixture models—Rayleigh, Gamma, or Lognormal—to model SAR backscatter and produce soft (posterior) segmentations, followed by posterior thresholding and optional MRF‑ICM post‑processing to enhance spatial coherence and suppress speckle-induced errors. We compare against traditional threshold-based methods (CFAR, multi-threshold Otsu) and conventional mixture-model labeling that designates the largest-scale component as the target.

On RCM data, the Rayleigh Mixture Model (RMM) is the strongest: at target pixels, the posterior probability of the largest-mean component is typically very close to 1, allowing a single Rayleigh component to capture the main body of the iceberg reliably. Unlike threshold-based baselines that yield hard segmentations, our Mixture Model (MM) approach outputs soft posteriors, enabling principled HH/HV fusion and downstream machine learning (ML). These results underscore the promise of RMM for robust iceberg detection; future work will integrate Rayleigh-based posterior features with lightweight ML classifiers to further improve performance across sensors and conditions.

Sea ice on the Newfoundland and Labrador shelf can create major risks for ships and offshore structures. This study uses Ice Profiling Sonar and upward looking ADCP data from three moorings on the Northeast Newfoundland Shelf to examine the cross sectional morphology of important sea ice features. The data were converted from time series to spatial draft profiles using measured ice drift. From these profiles, level ice, keel features, and floes were extracted and compared across the three locations.

The results show that level ice and keels form clearly different morphological populations. Keels are generally deeper, narrower, rougher, and more peaked, while level ice is wider, smoother, lower in relief, and more rectangular in cross section. Maximum draft, mean draft, width, relief range, aspect ratio, rectangularity, and roughness provide the clearest separation between the two classes. The study also examines floe size to better understand how local ice features form. Small floes contain a higher proportion of keel features, while medium, big, and vast floes are more strongly dominated by level ice, although this pattern varies by site. NENS3 shows a higher keel fraction across floe size classes than NENS2, suggesting stronger and more persistent deformation. These findings provide new regional information for sea ice characterization and ice interaction studies.