research-article

A Deep Learning approach to Hyperspectral Image Classification using an improved Hybrid 3D-2D Convolutional Neural Network

Authors:

Dimitra Koumoutsou,

Eleni CharouAuthors Info & Claims

SETN 2020: 11th Hellenic Conference on Artificial Intelligence

Pages 85 - 92

https://doi.org/10.1145/3411408.3411462

Published: 02 September 2020 Publication History

Abstract

In recent years, the task of Hyperspectral Image (HSI) classification has appeared in various fields, including Remote Sensing. Meanwhile, the evolution of Deep Learning, and the prevalence of the Convolutional Neural Network (CNN) has revolutionized the way unstructured, especially visual, data are processed. 2D CNN have proved highly efficient in exploiting the spatial information of images, but in HSI classification, data contain both spectral and spatial features. To make use of these characteristics, many variations of a 3D CNN have been proposed, but a 3D Convolution comes at a high computational cost. A fusion of 3D and 2D convolutions decreases processing time by distributing spectral-spatial feature extraction across a lighter, less complex model. An enhanced Hybrid network architecture is proposed alongside a data preprocessing plan, with the aim of achieving a significant improvement in classification results. Four benchmark datasets (Indian Pines, Pavia University, Salinas and Data Fusion 2013 Contest) are used to compare the model to other hand-crafted or deep learning architectures. It is demonstrated that the proposed network outperforms state-of-the-art approaches in terms of classification accuracy, while avoiding some commonly used, computationally expensive design choices.

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Cited By

Tekyeh-Nejad MEbrahimzadeh AAhmadi M(2023)Improving Classification Accuracy of Hyperspectral Image Using Convolutional Neural NetworksJournal of Geospatial Information Technology10.61186/jgit.11.1.5911:1(59-82)Online publication date: 1-Jun-2023
https://doi.org/10.61186/jgit.11.1.59
Pandi GAggarwal K(2023)Deep Learning-Based 3-D Model for the Cultural Heritage Sites in the State of Gujarat, IndiaArtificial Intelligence and Sustainable Computing10.1007/978-981-99-1431-9_59(737-750)Online publication date: 24-Sep-2023
https://doi.org/10.1007/978-981-99-1431-9_59
Sawant SRaisinghani DVazirani SZawar KGiri N(2023)Nirbhaya Naari: An Artificial Intelligence Tool for Detection of Crime Against WomenIntelligent Systems and Human Machine Collaboration10.1007/978-981-19-8477-8_3(29-45)Online publication date: 30-Mar-2023
https://doi.org/10.1007/978-981-19-8477-8_3
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A Deep Learning approach to Hyperspectral Image Classification using an improved Hybrid 3D-2D Convolutional Neural Network
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches

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SETN 2020: 11th Hellenic Conference on Artificial Intelligence

September 2020

249 pages

ISBN:9781450388788

DOI:10.1145/3411408

Copyright © 2020 ACM.

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Published: 02 September 2020

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SETN 2020: 11th Hellenic Conference on Artificial Intelligence

September 2 - 4, 2020

Athens, Greece

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Cited By

Tekyeh-Nejad MEbrahimzadeh AAhmadi M(2023)Improving Classification Accuracy of Hyperspectral Image Using Convolutional Neural NetworksJournal of Geospatial Information Technology10.61186/jgit.11.1.5911:1(59-82)Online publication date: 1-Jun-2023
https://doi.org/10.61186/jgit.11.1.59
Pandi GAggarwal K(2023)Deep Learning-Based 3-D Model for the Cultural Heritage Sites in the State of Gujarat, IndiaArtificial Intelligence and Sustainable Computing10.1007/978-981-99-1431-9_59(737-750)Online publication date: 24-Sep-2023
https://doi.org/10.1007/978-981-99-1431-9_59
Sawant SRaisinghani DVazirani SZawar KGiri N(2023)Nirbhaya Naari: An Artificial Intelligence Tool for Detection of Crime Against WomenIntelligent Systems and Human Machine Collaboration10.1007/978-981-19-8477-8_3(29-45)Online publication date: 30-Mar-2023
https://doi.org/10.1007/978-981-19-8477-8_3
Yalamarthi SJoga LMadem SVaddi R(2022)Deep Net based Framework for Hyperspectral Image Classification2022 7th International Conference on Communication and Electronics Systems (ICCES)10.1109/ICCES54183.2022.9835917(1475-1479)Online publication date: 22-Jun-2022
https://doi.org/10.1109/ICCES54183.2022.9835917

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