CASIA OpenIR  > 模式识别国家重点实验室  > 先进数据分析与学习
Rotaion and Scale-invariant Object Detector for High Resolution Optical Remote Sensing Images
Huang H(黄河)1,2; Huo CL(霍春雷)1; Wei FL(魏飞龙)3; Pan CH(潘春洪)1
2019-04-05
Conference NameInternational Geoscience and Remote Sensing Symposium (IGARSS)
Conference Date2019年7月29日-2019年8月2日
Conference Place日本横滨
Abstract

Object detection of high-resolution optical remote sensing images is challenging due to two fundamental problems. One is the huge scale variation of objects in images, e.g., small vehicle and cross-sea bridge. The other one is the objects could take on arbitrary orientations because of the high angle shot. In this paper, we propose a Rotation and Scale-invariant Detector (RS-Det) for remote sensing images to solve the above problem in an unified network. Specifically, RS-Det consists of a deformable convolution module to learn spatial transformation (such as rotation, transition, etc) and a feature pyramid architecture for multi-scale feature representation. These two modules enable a better feature learning of convolutional neural network and boost the performance by 3.6% compared with the baseline method. In DOTA, a large-scale
dataset for aerial image object detection, our RS-Det achieves the state-of-the-art accuracy, which verifies our method’s superiority.

KeywordRotation-invariant Scale-invariant Convolutional Neural Network Optical Remote Sensing Object Detection
MOST Discipline Catalogue工学::计算机科学与技术(可授工学、理学学位)
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23934
Collection模式识别国家重点实验室_先进数据分析与学习
Corresponding AuthorHuang H(黄河)
Affiliation1.中科院自动化所模式识别国家重点实验室
2.中国科学院大学
3.北京联合大学机器人学院
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Huang H,Huo CL,Wei FL,et al. Rotaion and Scale-invariant Object Detector for High Resolution Optical Remote Sensing Images[C],2019.
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