Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images

Zhang Shuo; He Guanghui; Chen Hai-Bao; Jing Naifeng; Wang Qin

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Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images

机译：遥感图像中对象检测的缩放自适应提案网络

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摘要

Object detection in aerial images is widely applied in many applications. In recent years, faster region convolutional neural network shows a great improvement on object detecting in natural images. Considering the size and distribution characteristic of object in remote sensing images, the region proposal network (RPN) should be changed before being adopted. In this letter, a scale adaptive proposal network (SAPNet) is proposed to improve the accuracy of multiobject detection in remote sensing images. The SAPNet consists of multilayer RPNs which are designed to generate multiscale object proposals, and a final detection subnetwork in which fusion feature layer has been applied for better multiobject detection. Comparative experimental results show that the proposed SAPNet significantly improves the accuracy of multiobject detection.

机译：在许多应用中广泛应用于航拍图像的对象检测。近年来，较快的地区卷积神经网络显示出对自然图像的对象的巨大改进。考虑到遥感图像中对象的大小和分布特性，在采用之前应该改变区域提议网络（RPN）。在这封信中，提出了一个规模的自适应提议网络（SAPNET），以提高遥感图像中的多机器检测的准确性。 SAPNET由多层RPN组成，该RPN被设计为生成多尺度对象提案，以及最终检测子网，其中融合特征层已应用于更好的多对多检测。比较实验结果表明，所提出的SAPNET显着提高了多致喷射检测的准确性。

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来源
《IEEE Geoscience and Remote Sensing Letters》 |2019年第6期|864-868|共5页
作者
Zhang Shuo; He Guanghui; Chen Hai-Bao; Jing Naifeng; Wang Qin;
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作者单位

Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai 200240 Peoples R China;

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正文语种 eng
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关键词
Convolution neural network (CNN); multiobject detection; remote sensing images; region proposal network (RPN);

机译：卷积神经网络（CNN）;多对多检测;遥感图像;区域提案网络（RPN）;

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专利

1. Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images [J] . Zhang Shuo, He Guanghui, Chen Hai-Bao, IEEE Geoscience and Remote Sensing Letters . 2019,第6期

机译：用于遥感图像目标检测的比例自适应提案网络
2. Object Detection in High Resolution Remote Sensing Imagery Based on Convolutional Neural Networks With Suitable Object Scale Features [J] . Dong Zhipeng, Wang Mi, Wang Yanli, IEEE Transactions on Geoscience and Remote Sensing . 2020,第3期

机译：基于卷积神经网络的高分辨率遥感图像中具有合适对象尺度特征的对象检测
3. Object-Scale Adaptive Convolutional Neural Networks for High-Spatial Resolution Remote Sensing Image Classification [J] . Jie Wang, Yalan Zheng, Min Wang, Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of . 2021,第1期

机译：对象级自适应卷积神经网络，用于高空间分辨率遥感图像分类
4. Cropping Region Proposal Network Based Framework for Efficient Object Detection on Large Scale Remote Sensing Images [C] . Qifeng Lin, Jianhui Zhao, Qianqian Tong, IEEE International Conference on Multimedia and Expo . 2019

机译：基于裁剪区域提议网的大规模遥感图像有效目标检测框架
5. Object Detection in High Resolution Aerial Images and Hyperspectral Remote Sensing Images [D] . Liang, Yilong . 2019

机译：高分辨率空中图像和高光谱遥感图像中的对象检测
6. Remote Sensing Imagery Super Resolution Based on Adaptive Multi-Scale Feature Fusion Network [O] . Xinying Wang, Yingdan Wu, Yang Ming, 2020

机译：基于自适应多尺度特征融合网络的遥感影像超分辨率
7. A2RMNet: Adaptively Aspect Ratio Multi-Scale Network for Object Detection in Remote Sensing Images [O] . Heqian Qiu, Hongliang Li, Qingbo Wu, 2019

机译：A2RMNET：遥感图像中的对象检测的自适应宽高比网络

Scale Adaptive Proposal Network for Object Detection in Remote Sensing Images

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