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

journal contribution
posted on 2023-05-19, 06:04 authored by Md Rakibul Haque, Sadia Zaman Mishu, MD PALASH UDDINMD PALASH UDDIN, Md Al Mamun
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.

History

Journal

Journal of Intelligent and Fuzzy Systems

Volume

43

Pagination

1241-1258

Location

Amsterdam, The Netherlands

ISSN

1064-1246

eISSN

1875-8967

Language

English

Publication classification

C1.1 Refereed article in a scholarly journal

Issue

1

Publisher

IOS Press