Multi-scale 3D-convolutional neural network for hyperspectral image classification
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Published:2022-01-01
Issue:1
Volume:25
Page:307
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ISSN:2502-4760
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Container-title:Indonesian Journal of Electrical Engineering and Computer Science
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language:
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Short-container-title:IJEECS
Author:
Kanthi Murali,Sarma Thogarcheti Hitendra,Bindu Chigarapalle Shoba
Abstract
Deep Learning methods are state-of-the-art approaches for pixel-based hyperspectral images (HSI) classification. High classification accuracy has been achieved by extracting deep features from both spatial-spectral channels. However, the efficiency of such spatial-spectral approaches depends on the spatial dimension of each patch and there is no theoretically valid approach to find the optimum spatial dimension to be considered. It is more valid to extract spatial features by considering varying neighborhood scales in spatial dimensions. In this regard, this article proposes a deep convolutional neural network (CNN) model wherein three different multi-scale spatial-spectral patches are used to extract the features in both the spatial and spectral channels. In order to extract these potential features, the proposed deep learning architecture takes three patches various scales in spatial dimension. 3D convolution is performed on each selected patch and the process runs through entire image. The proposed is named as multi-scale three-dimensional convolutional neural network (MS-3DCNN). The efficiency of the proposed model is being verified through the experimental studies on three publicly available benchmark datasets including Pavia University, Indian Pines, and Salinas. It is empirically proved that the classification accuracy of the proposed model is improved when compared with the remaining state-of-the-art methods.
Publisher
Institute of Advanced Engineering and Science
Subject
Electrical and Electronic Engineering,Control and Optimization,Computer Networks and Communications,Hardware and Architecture,Information Systems,Signal Processing
Cited by
3 articles.
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1. 3D-MACNet: A Multiscale Asymetric Convolution Network for Feature Extraction and Classification of Hyperspectral Images;2023 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS);2023-11-03
2. 3D-CAN: A 3D Convolution Attention Network for Feature Extraction and Classification of Hyperspectral Images;2023 International Conference on Network, Multimedia and Information Technology (NMITCON);2023-09-01
3. A Multiple Branch Fusion Network for Feature Learning and Hyperspectral Image Classification;2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT);2023-07-06