Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark
Ke Li, Gang Wan, Gong Cheng, Liqiu Meng, Junwei Han

TL;DR
This paper reviews recent deep learning methods for object detection in optical remote sensing images, highlights dataset limitations, and introduces a large-scale benchmark dataset called DIOR to advance research in this field.
Contribution
The paper provides a comprehensive survey and introduces the DIOR benchmark dataset, addressing existing dataset shortcomings and facilitating future deep learning research in remote sensing object detection.
Findings
Existing datasets are limited in size and diversity.
The DIOR dataset contains over 23,000 images and nearly 200,000 object instances.
State-of-the-art methods are evaluated on DIOR to establish baselines.
Abstract
Substantial efforts have been devoted more recently to presenting various methods for object detection in optical remote sensing images. However, the current survey of datasets and deep learning based methods for object detection in optical remote sensing images is not adequate. Moreover, most of the existing datasets have some shortcomings, for example, the numbers of images and object categories are small scale, and the image diversity and variations are insufficient. These limitations greatly affect the development of deep learning based object detection methods. In the paper, we provide a comprehensive review of the recent deep learning based object detection progress in both the computer vision and earth observation communities. Then, we propose a large-scale, publicly available benchmark for object DetectIon in Optical Remote sensing images, which we name as DIOR. The dataset…
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