Remote Sensing Super-resolution Object Detection (RSSOD) Dataset

Published: 8 November 2021| Version 1 | DOI: 10.17632/b268jv86tf.1
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Description

A new benchmark dataset RSSOD, for object detection of small objects in remote sensing images, with a high overlap of classes. Total 3 sets of annotations, 5-class, 4, class, 2-class, and Single-Class. Format: YOLO and COCO. If this dataset helped you in your research please cite: 1- Bashir, Syed Muhammad Arsalan; Wang, Yi; Khan, Mahrukh; Ullah, Qudrat; Wang, Rui; Song, Yilin; Guo, Zhe; Niu, Yilong (2021), “Remote Sensing Super-resolution Object Detection (RSSOD)”, Mendeley Data, V1, DOI: 10.17632/b268jv86tf.1 2- Wang, Yi; Bashir, Syed Muhammad Arsalan; Khan, Mahrukh; Ullah, Qudrat; Wang, Rui; Song, Yilin; Guo, Zhe; Niu, Yilong (2021), “Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art ”, arXiv. https://arxiv.org/abs/2111.03260

Files

Steps to reproduce

Use MATLAB "imresize ( )" function with bicubic interpolation to generate LR images with different scale factors. The annotations files are in YOLO and COCO format.

Institutions

Northwestern Polytechnical University

Categories

Computer Vision, Remote Sensing, Object Detection

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