A lightweight 3D-2D convolutional neural network for spectral-spatial classification of hyperspectral images

Author:

Haque Md. Rakibul1,Mishu Sadia Zaman1,Palash Uddin Md.2,Al Mamun Md.1

Affiliation:

1. Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Bangladesh

2. Department of Computer Science and Engineering, Hajee Mohammad Danesh Science and Technology University, Bangladesh

Abstract

Hyperspectral Image (HSI) is usually composed of hundreds of capturing wavelength bands, which not only increase the size of the HSI rapidly but also impose various obstacles in classifying the objects accurately. Moreover, the traditional machine learning schemes utilize only the spectral features for HSI classification, which, therefore, neglect the spatial features that have a significant impact on the classification improvement. To address the aforementioned issues, in this paper, we propose to employ the principal component analysis (PCA), the baseline feature extraction method, and a thoughtfully designed stacked autoencoder, a deep learning-based feature extraction approach, for reducing the high dimensionality of the HSI and then propose a novel lightweight 3D-2D convolutional neural network (CNN) framework to concurrently exploit both spatial and spectral features from the dimensionality-reduced HSI for classification. In particular, PCA and stacked autoencoder are applied to reduce the high dimensionality of the original HSI and then the proposed 3D-2D CNN provides a combination of 3D and 2D convolution operations to extract the subtle spatial and spectral features for efficient classification. We well-adjust the proposed 3D-2D CNN architecture, and perform extensive experiments on three benchmark HSI datasets and compare our approach with the state-of-the-art classical and deep learning methods. Experimental results illustrate that we have achieved an overall accuracy of 99.73%, 99.90%, and 99.32% on Indian Pines, Pavia University, and Kennedy Space Center datasets, respectively, which outperform the classical machine learning and independent 2D and 3D CNN-based state-of-the-art methods.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Enhancing Hyperspectral Image Compression Through Stacked Autoencoder Approach;2024 6th International Conference on Electrical Engineering and Information & Communication Technology (ICEEICT);2024-05-02

3. A Comprehensive Review of Deep Learning Methods for Hyperspectral Image Compression;2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE);2024-04-25

4. An Evaluation of Convolutional Neural Networks for Lithological Mapping Based on Hyperspectral Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

5. Hybrid 2D-3D convolution neural network for hyperspectral image classification;2023 26th International Conference on Computer and Information Technology (ICCIT);2023-12-13

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