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

Abstract

Synthetic aperture radar (SAR) plays a remarkable role in ocean surveillance, with capabilities of detecting oil spills, icebergs, and marine traffic both at daytime and at night, regardless of clouds and extreme weather conditions. The detection of ocean objects using SAR relies on well-established methods, mostly adaptive thresholding algorithms. In most waters, the dominant ocean objects are ships, whereas in arctic waters the vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Since these objects can look very much alike in SAR images, the determination of what objects actually are still relies on manual detection and human interpretation. With the increasing interest in the arctic regions for marine transportation, it is crucial to develop novel approaches for automatic monitoring of the traffic in these waters with satellite data. Hence, this study aims at proposing a deep learning model based on YoloV3 for discriminating icebergs and ships, which could be used for mapping ocean objects ahead of a journey. Using dual-polarization Sentinel-1 data, we pilot-tested our approach on a case study in Greenland. Our findings reveal that our approach is capable of training a deep learning model with reliable detection accuracy. Our methodical approach along with the choice of data and classifiers can be of great importance to climate change researchers, shipping industries and biodiversity analysts. The main difficulties were faced in the creation of training data in the Arctic waters and we concluded that future work must focus on issues regarding training data.

Aggregate Channel Features and Fast Rcnn for Ship, Iceberg Classification

“…But tiny YOLO v3 needs small amount of memory even though there is a tradeoff between accuracy and memory. In the study [13] they used conventional YOLO which is based on Darknet-53. Three level pyramids were utilized for feature map creation.…”

Ocean Remote Sensing Techniques and Applications: A Review (Part II)

“…Various RS systems have been so far applied to identify and track icebergs [10,[25][26][27][28][29][30][31][32][33]. Optical, SAR, scatterometer, altimeter, and HF radar systems have been widely used for iceberg studies.…”

“…For example, developing SAR systems with higher penetration capability (e.g., L-band SAR systems) can considerably facilitate iceberg detection and relevant parameter estimation [29]. Finally, the availability of a huge volume of RS data requires more sophisticated data mining and processing algorithms (e.g., Deep Learning (DL)) and big data processing platforms (e.g., Google Earth Engine (GEE)) to exploit the full potential of RS data for iceberg studies [30][31][32].…”

Survey on Deep Learning-Based Marine Object Detection

“…In arctic waters, a vast majority of objects are icebergs drifting in the ocean and can be mistaken for ships in terms of navigation and ocean surveillance. Hass and Jokar Arsanjani [98] presented a YOLOv3-based deep learning model that uses SAR images to discriminate icebergs and ships, which could be used for mapping ocean objects ahead of a journey.…”