A Scale Adaptive Convolutional Neural Network for Object Detection in Remote Sensing Images

WU Jia-xiang; LIU Hui; HE Guang-hui

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A Scale Adaptive Convolutional Neural Network for Object Detection in Remote Sensing Images

更新时间：2026-09-04

- A Scale Adaptive Convolutional Neural Network for Object Detection in Remote Sensing Images
- Vol. 35, Issue 8, Pages: 78-81(2018)
- 作者机构：
  
  1. 上海交通大学电子信息与电气工程学院,上海,200240
  2. 上海卫星工程研究所,上海,201109
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published：2018
- 稿件说明：
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吴佳祥, 刘辉, 贺光辉. 一种应用于高分辨率遥感图像目标检测的尺度自适应卷积神经网络[J]. 微电子学与计算机, 2018, 35(8): 78-81, 86.

WU Jia-xiang, LIU Hui, HE Guang-hui. A Scale Adaptive Convolutional Neural Network for Object Detection in Remote Sensing Images[J]. 2018, 35(8): 78-81.
吴佳祥, 刘辉, 贺光辉. 一种应用于高分辨率遥感图像目标检测的尺度自适应卷积神经网络[J]. 微电子学与计算机, 2018, 35(8): 78-81, 86. DOI：

WU Jia-xiang, LIU Hui, HE Guang-hui. A Scale Adaptive Convolutional Neural Network for Object Detection in Remote Sensing Images[J]. 2018, 35(8): 78-81. DOI：

摘要

为了解决高分辨率遥感图像中的目标检测问题并提高检测准确率

本文提出了一种基于卷积神经网络的检测方法.该方法采用Faster R-CNN作为遥感图像目标检测的基础框架

在此基础之上

针对遥感图像中不同尺度物体的准确率差异较大这一问题

提出了一种尺度自适应卷积设计网络.经过在遥感图像数据集上的测试

该方法与已有的方法相比

大大提高了高分辨率遥感图像中目标检测的准确率.

Abstract

A new approach based on convolutional neural network (CNN) is proposed to improve the accuracy of multi-object in remote sensing images. Faster region convolutional neural network has been employed as the basic framework. Moreover

a scale adaptive convolutional neural network (SA-CNN) is proposed to deal with the multi-scale target detection in remote sensing images. The comparative experimental results show that the proposed SA-CNN significantly improves the accuracy of multi-object detection.

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references

K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition[C]. In: International Conference on Learning Representations (ICLR), 2015.

S. Ren, K. He, R. Girshick, and J. Sun. Faster R-CNN: towards realtime object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137-1149. DOI: 1137

L. Zhang, L. Zhang, and B. Du. Deep learning for remote sensing data: A technical tutorial on the state of the art[J]. IEEE Geoscience and Remote Sensing Magazine, 2016, 4(2): 22-40. DOI: 22

G. Cheng, P. Zhou, and J. Han. Learning rotation-invariant convolutional neural networks for object detection in vhr optical remote sensing images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(12):7405-7415. DOI: 7405

Z. Zou and Z. Shi. Ship detection in spaceborne optical image with svd networks[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10):5832-5845. DOI: 5832

R. Girshick, J. Donahue, T. Darrell, and J. Malik. Rich feature hierarchies for accurate object detection and semantic segmentation[C].In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014: 580-587.

R. Girshick. Fast R-CNN[C]//Proceedings of the IEEE International Conference on Computer Vision(ICCV). 2015: 1440-1448.

G. Cheng, J. Han, P. Zhou, and L. Guo. Multi-class geospatial object detection and geographic image classification based on collection ofpart detectors[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 98(12):119-132. DOI: 119

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