基于小波变换和深度网络的着陆地貌图像分类

刘芳; 韩笑

doi:10.12263/DZXB.20200870

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基于小波变换和深度网络的着陆地貌图像分类

学术论文 | 更新时间：2025-12-08

- 基于小波变换和深度网络的着陆地貌图像分类
- Image Classification of Landing Landform Based on Wavelet Transform and Deep Network
- 电子学报 2021年49卷第11期页码：2171-2176
- 作者机构：
  
  北京工业大学信息学部，北京 100124
- 作者简介：
  
  [ "刘　芳女，1972年生，陕西西安人.现为北京工业大学信息学部副教授、硕士生导师，主要研究领域为图像处理、深度学习、视频跟踪.E-mail:liufang@bjut.edu.cn" ]
  [ "韩笑（通信作者）男，1996年生，河北沧州人.现为北京工业大学信息学部硕士研究生，主要研究方向为数字图像处理、深度学习.E-mail:hxdeemail@163.com" ]
- 基金信息：
  
  国家自然科学基金(61171119)
- DOI：10.12263/DZXB.20200870
  中图分类号： TN911.73;TP391
- 收稿：2020-08-10，
  
  修回：2021-04-20，
  
  纸质出版：2021-11-25
- 稿件说明：
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刘芳,韩笑.基于小波变换和深度网络的着陆地貌图像分类[J].电子学报,2021,49(11):2171-2176.

LIU Fang,HAN Xiao.Image Classification of Landing Landform Based on Wavelet Transform and Deep Network[J].ACTA ELECTRONICA SINICA,2021,49(11):2171-2176.
刘芳,韩笑.基于小波变换和深度网络的着陆地貌图像分类[J].电子学报,2021,49(11):2171-2176. DOI： 10.12263/DZXB.20200870.

LIU Fang,HAN Xiao.Image Classification of Landing Landform Based on Wavelet Transform and Deep Network[J].ACTA ELECTRONICA SINICA,2021,49(11):2171-2176. DOI： 10.12263/DZXB.20200870.

摘要

针对无人机着陆地貌图像场景复杂、纹理特征丰富等问题，提出一种基于小波变换和深度网络的无人机着陆地貌图像分类算法.利用非下采样小波变换（Non-Subsampled Wavelet Transform，NSWT）的快速压缩能力，将小波变换后的前两层子图系数引入到卷积神经网络（CNN）中，压缩数据量.根据无人机着陆地貌图像的特点，采用轻量化卷积模块设计了15层卷积神经网络.通过支持向量机（SVM）实现复杂地貌场景的正确分类.实验结果表明：所提算法具有良好的特征表达能力，提升了着陆地貌图像的分类准确率.

Abstract

Aiming at the problems of complex scenes and rich texture features of UAV landing landform image

a classification algorithm of UAV landing landform image based on wavelet transform and deep network is proposed. Using the fast compression capability of non-subsampled wavelet transform (NSWT)

the first two layers of sub-image coefficients after wavelet transform are introduced into the convolutional neural network (CNN) to compress the amount of data. According to the characteristics of UAV landing landform image

a 15-layer convolutional neural network is designed using a lightweight convolution module. The correct classification of complex geomorphological scenes is realized by support vector machine (SVM). The experimental results show that the proposed algorithm has good feature expression ability and improves the classification accuracy of landing landform images.

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references

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