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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.<\/jats:p>","DOI":"10.3233\/jifs-212829","type":"journal-article","created":{"date-parts":[[2022,3,4]],"date-time":"2022-03-04T18:53:34Z","timestamp":1646420014000},"page":"1241-1258","source":"Crossref","is-referenced-by-count":7,"title":["A lightweight 3D-2D convolutional neural network for spectral-spatial classification of hyperspectral images"],"prefix":"10.1177","volume":"43","author":[{"given":"Md. 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