Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark

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 contains 23463 images and 192472 instances, covering 20 object classes. The proposed DIOR dataset 1) is large-scale on the object categories, on the object instance number, and on the total image number; 2) has a large range of object size variations, not only in terms of spatial resolutions, but also in the aspect of inter- and intra-class size variability across objects; 3) holds big variations as the images are obtained with different imaging conditions, weathers, seasons, and image quality; and 4) has high inter-class similarity and intra-class diversity. The proposed benchmark can help the researchers to develop and validate their data-driven methods. Finally, we evaluate several state-of-the-art approaches on our DIOR dataset to establish a baseline for future research.