Abstract
Ship category classification in high-resolution aerial images has attracted great interest in applications such as maritime security, naval construction, and port management. However, the applications of previous methods were mainly limited by the following issues: (i) The existing ship category classification methods were mainly to classify on accurately-cropped image patches. This is unsatisfactory for the results of the existing methods in practical applications, because the location of the ship in the patch obtained by the object detection varies greatly. (ii) The factors such as target scale variations and class imbalance have a great influence on the performance of ship category classification. Aiming at the issues above, we propose a novel ship detection and category classification framework. The category classification is based on accurate location. The detection network can generate more precise rotated bounding boxes in large-scale aerial images by introducing a novel Sequence Local Context (SLC) module. Besides, three different ship category classification networks are proposed to eliminate the effect of scale variations, and the Spatial Transform Crop (STC) operation is used to get aligned image patches. Whatever the problem of insufficient samples or class imbalance have, the Proposals Simulation Generator (PSG) is considered to handle this properly. Most remarkably, the state-of-the-art performance of our framework is demonstrated by experiments based on the 19-class ship dataset HRSC2016 and our multiclass warship dataset.
Highlights
Ship detection and recognition in high-resolution aerial images are challenging tasks, which play an important role in many related applications, e.g., maritime security, naval construction, and port management
We achieve state-of-the-art performances on two real-world datasets for ship detection and classification in high-resolution aerial images
In most object detection tasks, the ground-truth of the object is described by the horizontal bounding box with (x, y, w, h), where (x, y) is the center of the bounding box, and the width w and height h represent the long side and the short side of the horizontal bounding box, respectively [33]
Summary
Ship detection and recognition in high-resolution aerial images are challenging tasks, which play an important role in many related applications, e.g., maritime security, naval construction, and port management. Many ship classification methods have been proposed, they only roughly identify the ship as warship, container ship, oil tanker, etc., on the accurate image patch. Existing research on ship detection and classification can be divided into two aspects. One is the location problem, which finds the ships in the aerial images and expresses the location in some way. The other is the classification issue, which assigns the ships to different categories
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