Aggregate Channel Features and Fast Rcnn for Ship, Iceberg Classification

Abstract

Detection and classification of Iceberg and ship in synthetic aperture radar (SAR) image plays a vital role in marine surveillance systems. Even though available adaptive thresholding methods give satisfying results on this problem, still those methods face complexity on classification of ship and icebergs. The research proposed aims to locate and classify both ship and iceberg in a given SAR image with the help of deep learning and non-deep learning methods. The non-deep learning method, Aggregate Channel Features Detector (ACF), was used to extract region proposals from the huge SAR images. The deep learning object detector, Fast R-CNN detects objects accurately from the result of ACF by avoiding unwanted regions and concentrates only on accurate classification of ship and iceberg. The proposed method gives better performance in terms of accuracy, recall, precision and F1 score rather than other conventional methods and reduces the complexity of detector.