ShipRSImageNet

ShipRSImageNet is a large-scale fine-grainted dataset for ship detection in high-resolution optical remote sensing images. The dataset contains 3,435 images from various sensors, satellite platforms, locations, and seasons. Each image is around 930×930 pixels and contains ships with different scales, orientations, and aspect ratios. The images are annotated by experts in satellite image interpretation, categorized into 50 object categories images. The fully annotated ShipRSImageNet contains 17,573 ship instances. There are five critical contributions of the proposed ShipRSImageNet dataset compared with other existing remote sensing image datasets. Images are collected from various remote sensors cover- ing multiple ports worldwide and have large variations in size, spatial resolution, image quality, orientation, and environment. Ships are hierarchically classified into four levels and 50 ship categories. The number of images, ship instances, and ship cate- gories is larger than that in other publicly available ship datasets. Besides, the number is still increasing. We simultaneously use both horizontal and oriented bounding boxes, and polygons to annotate images, providing detailed information about direction, background, sea environment, and location of targets. We have benchmarked several state-of-the-art object detection algorithms on ShipRSImageNet, which can be used as a baseline for future ship detection methods.