A Public Dataset for Fine-Grained Ship Classification in Optical Remote Sensing Images

Yanghua Di,Haopeng Zhang,Zhiguo Jiang

doi:10.3390/rs13040747

Abstract

Fine-grained visual categorization (FGVC) is an important and challenging problem due to large intra-class differences and small inter-class differences caused by deformation, illumination, angles, etc. Although major advances have been achieved in natural images in the past few years due to the release of popular datasets such as the CUB-200-2011, Stanford Cars and Aircraft datasets, fine-grained ship classification in remote sensing images has been rarely studied because of relative scarcity of publicly available datasets. In this paper, we investigate a large amount of remote sensing image data of sea ships and determine most common 42 categories for fine-grained visual categorization. Based our previous DSCR dataset, a dataset for ship classification in remote sensing images, we collect more remote sensing images containing warships and civilian ships of various scales from Google Earth and other popular remote sensing image datasets including DOTA, HRSC2016, NWPU VHR-10, We call our dataset FGSCR-42, meaning a dataset for Fine-Grained Ship Classification in Remote sensing images with 42 categories. The whole dataset of FGSCR-42 contains 9320 images of most common types of ships. We evaluate popular object classification algorithms and fine-grained visual categorization algorithms to build a benchmark. Our FGSCR-42 dataset is publicly available at our webpages.

Highlights

Fine-grained categorization attracts extensive attention in computer vision field
In order to achieve better performance in the fine-grained ship classification in remote sensing images, we investigate a large amount of remote sensing image data of sea ships with repeated comparisons and confirmations and eventually determine most common
We presented FGSCR-42, a new large public dataset for fine-grained ship classification, which has a wide variety of categories of most warships and civil vessels in remote sensing images

Summary

Introduction

Fine-grained categorization attracts extensive attention in computer vision field. The task is relatively challenging due to large intra-class differences and small inter-class differences compared with general classification tasks. The fine-grained image recognition should pay more attention to marginal visual differences within subordinate categories. Due to the relatively small inter-class differences, fine-grained ship classification is challenging and of great significance in understanding remote sensing images. With the rapid development of optical satellites, the outstanding advantages of optical images in ship reconnaissance, especially in the ship classification, have attracted the tremendous attention of marine monitoring departments and scholars. Driven by the success of deep-learning-based algorithms for extracting deep features, many researchers have pursued approaches based on fine-tuning networks for object detection based on remote sensing image datasets, e.g., DOTA [1]

Objectives

Methods

Results

Conclusion