An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification Article Swipe

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Donghang Yu , Qing Xu , Haitao Guo , Chuan Zhao , Yuzhun Lin , Daoji Li ·

YOU? · · 2020 · Open Access · · DOI: https://doi.org/10.3390/s20071999

Classifying remote sensing images is vital for interpreting image content. Presently, remote sensing image scene classification methods using convolutional neural networks have drawbacks, including excessive parameters and heavy calculation costs. More efficient and lightweight CNNs have fewer parameters and calculations, but their classification performance is generally weaker. We propose a more efficient and lightweight convolutional neural network method to improve classification accuracy with a small training dataset. Inspired by fine-grained visual recognition, this study introduces a bilinear convolutional neural network model for scene classification. First, the lightweight convolutional neural network, MobileNetv2, is used to extract deep and abstract image features. Each feature is then transformed into two features with two different convolutional layers. The transformed features are subjected to Hadamard product operation to obtain an enhanced bilinear feature. Finally, the bilinear feature after pooling and normalization is used for classification. Experiments are performed on three widely used datasets: UC Merced, AID, and NWPU-RESISC45. Compared with other state-of-art methods, the proposed method has fewer parameters and calculations, while achieving higher accuracy. By including feature fusion with bilinear pooling, performance and accuracy for remote scene classification can greatly improve. This could be applied to any remote sensing image classification task.

Related Topics

Convolutional Neural Network

Computer Science

Bilinear Interpolation

Artificial Intelligence

Concepts

Convolutional neural network Pooling Computer science Bilinear interpolation Artificial intelligence Contextual image classification Feature (linguistics) Pattern recognition (psychology) Normalization (sociology) Feature extraction Remote sensing Image (mathematics) Computer vision Geology Anthropology Philosophy Linguistics Sociology

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Type: article
Language: en
Landing Page: https://doi.org/10.3390/s20071999
PDF: https://www.mdpi.com/1424-8220/20/7/1999/pdf?version=1586841750
OA Status: gold
Cited By: 101
References: 79
Related Works: 10
OpenAlex ID: https://openalex.org/W3014323018

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DOI: https://doi.org/10.3390/s20071999

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Title: An Efficient and Lightweight Convolutional Neural Network for Remote Sensing Image Scene Classification

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Type: article

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Language: en

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Publication year: 2020

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Publication date: 2020-04-02

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Authors: Donghang Yu, Qing Xu, Haitao Guo, Chuan Zhao, Yuzhun Lin, Daoji Li

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Landing page: https://doi.org/10.3390/s20071999

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PDF URL: https://www.mdpi.com/1424-8220/20/7/1999/pdf?version=1586841750

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Concepts: Convolutional neural network, Pooling, Computer science, Bilinear interpolation, Artificial intelligence, Contextual image classification, Feature (linguistics), Pattern recognition (psychology), Normalization (sociology), Feature extraction, Remote sensing, Image (mathematics), Computer vision, Geology, Anthropology, Philosophy, Linguistics, Sociology

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Cited by: 101

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Citations by year (recent): 2025: 11, 2024: 21, 2023: 24, 2022: 21, 2021: 18

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