Abstract
Automatic interpretation of remote sensing images is a fundamental but challenging problem in the field of aerial and satellite image analysis. It plays a vital role in a wide range of applications and is receiving significant attention in recent years. Even though many great progress has been made in this field, the detection of multi-scale objects, especially small objects in high-resolution satellite (HRS) and drone images, has not been adequately explored. As a result, detection performance both in terms of detection speed and accuracy turns out to be poor. To address this problem, we propose a convolutional neural network (CNN)-based single-stage object detector for the real-time and accurate recognition of remote sensing images. Our model predicts bounding boxes and corresponding class probabilities directly from images in a single assessment. This will result in a real-time object detection of images without compromising accuracy.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ren S, He K, Girshick R, Sun J (2017) Faster RCNN: towards real-time object detection with region proposal networks. In: IEEE transactions on pattern analysis and machine intelligence
Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) SSD: single shot multibox detector. In: European conference on computer vision
Redmon J, Divvala S, Girshick R, Farhadi A (2016) You Only Look Once: unified, real-time object detection. In: IEEE conference on computer vision and pattern recognition
Bochkovskiy A, Wang CY, Liao H (2020) Yolov4: optimal speed and accuracy of object detection. ar**v:2004.10934
Lin T-Y, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollar P, Zitnick CL (2014) Microsoft COCO: common objects in context. In: European conference on computer vision
Redmon J, Farhadi A (2016) YOLOv3: an incremental improvement. ar**v:1804.02767
Wang C, Liao HYM, Wu Y, Chen P, Hsieh J, Yeh H (2020) CSPNet: a new backbone that can enhance learning capability of CNN. In: IEEE/CVF conference on computer vision and pattern recognition workshops
Russakovsky O, Deng J, Su H, Krause J, Satheesh S, Ma S, Huang Z, Karpathy A, Khosla A, Bernstein M, Berg AC, Fei-Fei L (2015) ImageNet large scale visual recognition challenge. Int J Comp Vis
Huang Z, Wang J (2019) DC-SPP-YOLO: dense connection and spatial pyramid pooling based YOLO for object detection. ar**v:1903.08589
Misra D (2020) Mish: a self regularized non-monotonic activation function. ar**v:1908.086
Zhu P, Wen L, Dawei D, Bian X, Qinghua H, Ling H (2020) Vision meets drones: past, present and future, ar**v:2001.06303
Zhang H, Wu C, Zhang Z, Zhu Y, Zhang Z, Lin H, Sun Y, He T, Mueller J, Manmatha R, Li M. A. Smola.: ResNeSt: split-attention networks. ar**v:2004.08955
Sun K, Zhao Y, Jiang B, Cheng T, **ao B, Liu D, Mu Y, Wang X, Liu W, Wang J. High-resolution representations for labeling pixels and regions. arxiv.1908.07919
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kuriakose, M., Hrishikesh, P.S., Puthussery, D., Jiji, C.V. (2023). Small Object Detection in Remote Sensing Images. In: Priyadarsini, R., Sundararajan, T. (eds) Advances in Small Satellite Technologies. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-19-7474-8_6
Download citation
DOI: https://doi.org/10.1007/978-981-19-7474-8_6
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-7473-1
Online ISBN: 978-981-19-7474-8
eBook Packages: EngineeringEngineering (R0)