Locality-Aware Rotated Ship Detection in High-Resolution Remote Sensing Imagery Based on Multi-Scale Convolutional Network

TL;DR

This paper introduces a multi-scale CNN framework for detecting ships in high-resolution remote sensing images, addressing challenges like scale variation and background clutter, and achieves state-of-the-art results.

Contribution

The paper presents a novel locality-aware rotated ship detection framework with a multi-scale CNN and a new high-resolution ship dataset, improving detection accuracy.

Findings

Achieves state-of-the-art detection performance on public and new datasets.

Introduces a locality-aware score alignment to improve localization accuracy.

Builds a new high-resolution ship detection dataset with 2499 images.

Abstract

Ship detection has been an active and vital topic in the field of remote sensing for a decade, but it is still a challenging problem due to the large scale variations, the high aspect ratios, the intensive arrangement, and the background clutter disturbance. In this letter, we propose a locality-aware rotated ship detection (LARSD) framework based on a multi-scale convolutional neural network (CNN) to tackle these issues. The proposed framework applies a UNet-like multi-scale CNN to generate multi-scale feature maps with high-level semantic information in high resolution. Then, a rotated anchor-based regression is applied for directly predicting the probability, the edge distances, and the angle of ships. Finally, a locality-aware score alignment is proposed to fix the mismatch between classification results and location results caused by the independence of each subnet. Furthermore, to enlarge the datasets of ship detection, we build a new high-resolution ship detection (HRSD) dataset, where 2499 images and 9269 instances were collected from Google Earth with different resolutions. Experiments based on public dataset HRSC2016 and our HRSD dataset demonstrate that our detection method achieves state-of-the-art performance.