The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Digital Archiving Policy
  • Promote your Publication
  • Metadata Harvesting (OAI2)

IJACSA

  • About the Journal
  • Call for Papers
  • Editorial Board
  • Author Guidelines
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Fees/ APC
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors
  • SUSAI-EE 2025
  • ICONS-BA 2025
  • IoT-BLOCK 2025

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Editorial Board
  • Guidelines
  • Submit
  • Current Issue
  • Archives
  • Indexing
  • Fees
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2023.0140617
PDF

A Multi-branch Feature Fusion Model Based on Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification

Author 1: Jinli Zhang
Author 2: Ziqiang Chen
Author 3: Yuanfa Ji
Author 4: Xiyan Sun
Author 5: Yang Bai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 6, 2023.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

Abstract: Hyperspectral image classification constitutes a pivotal research domain in the realm of remote sensing image processing. In the past few years, convolutional neural networks (CNNs) with advanced feature extraction capabilities have demonstrated remarkable performance in hyperspectral image classification. However, the challenges faced by classification methods are compounded by the difficulties of "dimensional disaster" and limited sample distinctiveness in hyperspectral images. Despite existing efforts to extract spectral spatial information, low classification accuracy remains a persistent issue. Therefore, this paper proposes a multi-branch feature fusion model classification method based on convolutional neural networks to fully extract more effective and adequate high-level semantic features. The proposed classification model first undergoes PCA dimensionality reduction, followed by a multi-branch network composed of three-dimensional and two-dimensional convolutions. Convolutional kernels of varying scales are utilized for multi-feature extraction. Among them, the 3D convolution not only adapts to the cube of hyperspectral data but also fully exploits the spectral-spatial information, while the 2D convolution learns deeper spatial information. The experimental results of the proposed model on three datasets demonstrate its superior performance over traditional classification models, enabling it to accomplish the task of hyperspectral image classification more effectively.

Keywords: Hyperspectral image classification; convolutional neural network (CNN); multi-branch network; feature fusion

Jinli Zhang, Ziqiang Chen, Yuanfa Ji, Xiyan Sun and Yang Bai, “A Multi-branch Feature Fusion Model Based on Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 14(6), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140617

@article{Zhang2023,
title = {A Multi-branch Feature Fusion Model Based on Convolutional Neural Network for Hyperspectral Remote Sensing Image Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140617},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140617},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {6},
author = {Jinli Zhang and Ziqiang Chen and Yuanfa Ji and Xiyan Sun and Yang Bai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Computer Vision Conference (CVC) 2026

16-17 April 2026

  • Berlin, Germany

Healthcare Conference 2026

21-22 May 2026

  • Amsterdam, The Netherlands

Computing Conference 2025

19-20 June 2025

  • London, United Kingdom

IntelliSys 2025

28-29 August 2025

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2025

6-7 November 2025

  • Munich, Germany
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org