A Lightweight Hyperspectral Remote Sensing Image Classification Method

Ma Shao-peng; Liang Lu; Teng Shao-hua

doi:10.12052/gdutxb.200153

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A Lightweight Hyperspectral Remote Sensing Image Classification Method

更新时间：2026-05-27

- A Lightweight Hyperspectral Remote Sensing Image Classification Method
- Vol. 38, Issue 3, Pages: 29-35(2021)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.200153
  CLC： TP751
- Online First：10 May 2021，
  
  Published：2021
- 稿件说明：
移动端阅览
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

HE Y Z, ZHANG Z Q, GUAN C Y. Application and prospect of hyperspectral remote sensing technology in precision agriculture monitoring [J]. Crop Research, 2015, 29(1): 96-100.

MIAO R H, HUANG F H, YANG H, et al . Farmland classification of hyperspectral image ba sed on spatial-spectral integration method [J ] . Jiangsu Journal of Agricultural Sciences, 2018, 34(4): 818-824.

DENG X L, ZENG G L, ZHU Z H, et al . Classification and feature band extraction of diseased citrus plants based on UAV hyperspectral remote sensing [J ] . Journal of South China Agricultural University, 2020, 41(6): 100-108.

HUANG Y, TANG L B, LI Z, et al . Research on peanut planting area classification technology using remote sensing image based deep learning [J ] . Journal of Signal Processing, 2019, 35(4): 617-622.

ZHANG F, XIONG Z, KOU N. Airborne hyperspectral remote sensing image data is used for rice precise classification [J]. Journal of Wuhan University of technology, 2002, 24(10): 36-39.

HAM J, CHEN Y, CRAWFORD M M, et al . Investigation of the random forest framework for classification of hyperspectral data [J ] . IEEE Transactions on Geoscience Remote Sensing, 2005, 43(3): 492-501.

MELGANI F, BRUZZONE L. Classification of hyperspectral remote sensing images with support vector machines [J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(8): 1778-1790.

QIAN Y, YE M, ZHOU J. Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture Features [J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(4): 2276-2291.

ZHANG X H, HAO R F, LI T Y. Hyperspectral abnormal target detection based on low rank and sparse matrix decomposition sparse representation [J]. Laser Optoelectronics Progress, 2019, 56(4): 234-240.

SHEN L, JIA S. Three-dimensional gabor wavelets for pixel-based hyperspectral imagery classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(12): 5039-5046.

CHEN Y, LIN Z, ZHAO X, et al . Deep learning-based classification of hyperspectral data [J ] . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(6): 2094-2107.

GAO X, OU Y N, YUAN H. A hyperspectral image classification method based on fast denoising and deep belief network [J]. Journal of Guilin University of Electronic Science and Technology, 2016, 36(6): 469-476.

WEI H, HUANG Y, WEI L, et al . Deep convolutional neural networks for hyperspectral image classification [J ] . Journal of Sensors, 2015, 2015: 1-12.

CHEN Y, JIANG H, LI C, et al . Deep feature extraction and classification of hyperspectral images based on convolutional neural networks [J ] . IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(10): 6232-6251.

YING L, ZHANG H, QIANG S. Spectral-Spatial classification of hyperspectral imagery with 3D convolutional neural network [J]. Remote Sensing, 2017, 9(1): 67-88.

LI Z Q, ZHU R F, GAO F, et al . Hyperspectral remote sensing image classification based on three dimensional convolution neural network combined with conditional random field optimization [J ] . Acta Optica Sinica, 2018, 38(8): 404-413.

CAO X, ZHOU F, XU L, et al . Hyperspectral image classification with markov random fields and a convolutional neural network [J ] . IEEE Transactions on Image Processing, 2018, 27(5): 2354-2367.

LI T, LENG J, KONG L, et al . DCNR: deep cube CNN with random forest for hyper-spectral image classification [J ] . Multimedia Tools and Applications, 2019, 78(3): 3411-3433.

HERVÉ A, WILLIAMS L J. Principal component analysis [J]. Wiley Interdisciplinary Reviews Computational Statistics, 2010, 2(4): 433-459.

YU S, JIA S, XU C. Convolutional neural networks for hyperspectral image classification [J]. Neurocomputing, 2017, 219(Jan. 5): 88-98.

SÁIZ-ABAJO M J, MEVIK B H, SEGTNAN V H, et al . Ensemble methods and data augmentation by noise addition applied to the analysis of spectroscopic data [J ] . Analytica Chimica Acta, 2005, 533(2): 147-159.

ZHANG H, WANG K F, WANG F Y. Advances and perspectives on applications of deep learning in visual object detection [J]. Acta Automatica Sinica, 2017, 43(8): 1289-1305.

JIANG F, GU Q, HAO H Z, et al . Survey on content-based image segmentation methods [J ] . Journal of Software, 2017, 28(1): 160-183.

SHEN X M, LI B J, SUN X, et al . Large scale face clustering based on convolutional neural network [J ] . Journal of Guangdong University of Technology, 2016, 33(6): 77-84.

IOFFE S, SZEGEDY C. Batch normalization: accelerating deep network training by reducing internal covariate shift[C]//Proceedings of the 32nd International Conference on Machine Learning. Lille, France: ICML, 2015, 448-456.

SRIVASTAVA N, HINTON G, KRIZHEVSKY A, et al . Dropout: a simple way to prevent neural networks from overfitting [J ] . Journal of Machine Learning Research, 2014, 15(1): 1929-1958.

CAO X, XU L, MENG D, et al . Integration of 3-dimensional discrete wavelet transform and markov random field for hyperspectral image classification [J ] . Neurocomputing, 2017, 226(Feb. 22): 90-100.

HAUT J M, PAOLETTI M E, PLAZA J, et al . Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach [J ] . IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(11): 6440-6461.

ZHANG H, LI Y, ZHANG Y, et al . Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network [J ] . Remote Sensing Letters, 2017, 8(5): 438-447.

ZHAO W, LI S, LI A, et al . Hyperspectral images classification with convolutional neural network and textural feature using limited training samples [J ] . Remote Sensing Letters, 2019, 10(5): 449-458.

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