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弹箭与制导学报 >

2023 , Vol. 43 >Issue 5: 1 - 8

DOI: https://doi.org/10.15892/j.cnki.djzdxb.2023.05.001

基于空洞空间金字塔池化U-Net的遥感图像多目标检测方法

  • 张善文 ,
  • 许新华 ,
  • 齐国红
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  • 郑州西亚斯学院电子信息工程学院,河南 郑州 451150

张善文(1965—),男,教授,博士,研究方向:机器学习及其应用。

收稿日期: 2022-01-01

  网络出版日期: 2024-12-30

基金资助

河南省科技厅科技攻关项目(222102110134)

河南省高等学校重点科研项目(22B520049)

收起

Multi-target Detection in Remote Sensing Images Based on Dilated Spatial Pyramid Pooling U-Net

  • ZHANG Shanwen ,
  • XU Xinhua ,
  • QI Guohong
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  • School of Electronic and Information Engineering, Zhengzhou Sias University, Zhengzhou 451150, Henan, China

Received date: 2022-01-01

  Online published: 2024-12-30

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摘要

针对遥感图像(RSI)中的目标相对较小、形变多样,且包含分布不均匀的非目标和背景等问题,提出一种基于空洞空间金字塔池化U-Net的遥感图像多目标检测方法。该方法利用空洞多尺度卷积提取多尺度目标的分类特征,运用空洞空间池化金字塔模块扩大卷积特征图的感受野,提取更充分的目标特征,并采用注意力机制、残差连接和长跳跃连接充分保留卷积层提取的 RSI的敏感特征。在公开遥感图像数据库EORSSD上的实验结果表明,所提出的方法能够从复杂多样的RSI中检测多尺度目标,检测精度为96.56%。

关键词： 遥感图像多目标检测; 空洞多尺度卷积; 空洞空间金字塔池化; 空洞空间金字塔池化U-Net

本文引用格式

张善文 , 许新华 , 齐国红 . 基于空洞空间金字塔池化U-Net的遥感图像多目标检测方法[J]. 弹箭与制导学报, 2023 , 43(5) : 1 -8 . DOI: 10.15892/j.cnki.djzdxb.2023.05.001

Abstract

Target detection in remote sensing image (RSI) is an important and challenging research. Aiming at the problems of relatively small targets, uneven non-target, complex background and diverse deformation in RSI, a dilated spatial pyramid pooling U-Net (DSPPU) model is constructed for multi-target detection in RSI. In DPPU, dilated multi-scale convolution is used to extract the classification features of multi-scale targets, and dilated spatial pooling pyramid (DSPP) module is used to enlarge the convolutional receptive field to extract more adequate target features. Moreover, attention mechanism, residual connection and skip connection are used to fully retain the sensitive features of the RSI extracted by the convolutional layer. Experimental results on EORSSD, a public remote sensing image database show that the proposed method can detect multi-scale objects from complex and diverse RSI with a detection accuracy of 96.56%.

Key words： multi-target detection in remote sensing image; dilated multi-scale convolution; dilated spatial pyramid pooling; DSPP U-Net

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