A Computational Framework for Iceberg and Ship Discrimination: Case Study on Kaggle Competition

Abstract

Iceberg and ship identification in satellite synthetic aperture radar (SAR) data plays an important role in offering an operational iceberg surveillance program. Here, the identification aims to detect ocean SAR targets and then categorize these targets into iceberg, ship, or unknown. Although the adaptive threshold techniques have achieved promising results on the ship and iceberg detection in SAR images, the discrimination between these two target classes is still very challenging for operational scenarios. This study presents a computational framework for iceberg and ship discrimination based on an ensemble of various deep learning and machine learning algorithms. On one hand, latest deep neural networks - namely, DenseNet and ResNet - are deployed in this study for end-to-end feature exaction and image classification directly on original SAR images. On the other hand, handcrafted features are extracted on de-speckled SAR images, followed by classification using advanced machine learning algorithms - namely, XGBoost and LightGBM. The outcomes from both sides are then combined through min-max median stacking approach to classify the given SAR images into iceberg and ship categories. The proposed framework has recently been deployed as the key kernel for the “Statoil/C-CORE Iceberg Classifier Challenge” organized by Kaggle. The performance is promising as our final scores were ranked 26 and 39 out of 3343 teams on public and private leaderboards, respectively. We hope that by sharing the solutions, we can further promote research interests in the field of iceberg and ship identification.