一种轻量级的高光谱遥感图像分类方法

马少鹏; 梁路; 滕少华

doi:10.12052/gdutxb.200153

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一种轻量级的高光谱遥感图像分类方法

更新时间：2026-05-27

- 一种轻量级的高光谱遥感图像分类方法
- A Lightweight Hyperspectral Remote Sensing Image Classification Method
- 广东工业大学学报 2021年38卷第3期页码：29-35
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
  
  马少鹏(1996–)，男，硕士研究生，主要研究方向为机器学习和图像处理
  梁路(1980–)，女，副教授，博士，主要研究方向为协同计算、数据挖掘、机器学习，E-mail：lianglu@gdut.edu.cn
- 基金信息：
  
  国家自然科学基金资助项目(61972102，61603100)
- DOI：10.12052/gdutxb.200153
  中图分类号： TP751
- 网络首发：2021-05-10，
  
  纸质出版：2021
- 稿件说明：
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马少鹏, 梁路, 滕少华. 一种轻量级的高光谱遥感图像分类方法[J]. 广东工业大学学报, 2021, 38(3): 29-35.

Ma Shao-peng, Liang Lu, Teng Shao-hua. A Lightweight Hyperspectral Remote Sensing Image Classification Method[J]. 2021, 38(3): 29-35.
马少鹏, 梁路, 滕少华. 一种轻量级的高光谱遥感图像分类方法[J]. 广东工业大学学报, 2021, 38(3): 29-35. DOI： 10.12052/gdutxb.200153.

Ma Shao-peng, Liang Lu, Teng Shao-hua. A Lightweight Hyperspectral Remote Sensing Image Classification Method[J]. 2021, 38(3): 29-35. DOI： 10.12052/gdutxb.200153.

摘要

智慧农业已成为当今世界现代农业发展的大趋势

其中低空无人机遥感图像分析是现代精准农业的重点研究方向

它通过对无人机拍摄的高光谱遥感图像进行学习

来指导无人机进行精准作业。然而

中小型农场在发展智慧农场的过程中存在设备资源不足的弊端

因此本文提出了一种基于卷积神经网络的轻量级高光谱遥感图像分类方法

旨在保证较高分类精度的同时降低模型训练成本

从而帮助中小型农场避免更换昂贵的高性能设备

降低运营成本。本文方法使用主成分分析、数据扩增等数据预处理方法对高光谱遥感图像进行降维以及样本扩充

引入空谱联合特征提高分类精度

并对卷积神经网络结构进行优化加速了网络的训练过程。最后

通过在3个中小规模的基准数据集上进行实验

并与一些经典的传统分类方法以及深度学习方法进行对比

结果表明本方法能够保证较好的分类效果

同时减少网络训练的成本。

Abstract

Intelligent agriculture has become a major trend in the development of modern agriculture. Low-altitude UAV remote sensing image analysis is the key point in precision agriculture

which guides UAV to carry out precise job by studying on hyperspectral remote sensing images taken by UAV. However

small and medium-sized farms have the disadvantage of insufficient computational resource. To deal with the problems

a classification method based on lightweight Convolutional Neural Networks (CNN) is proposed. This method aims to reduce the training cost and maintain an acceptable classification accuracy

so as to help farms avoid replacing expensive high-performance equipment. PCA is used to reduce the image spectral dimension and some data augmentation methods are used to enlarge the dataset. The CNN structure is optimized to accelerate the training process and the model used to extract Spectral-Spatial feature to improve classification accuracy. Experiments are conducted on three benchmark datasets

which shows that our lightweight CNN model can guarantee a satisfying accuracy while having a lower training cost compared with both traditional methods and deep learning method.

关键词

Keywords

references

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