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

被引:76
作者
Yu, Donghang [1 ]
Xu, Qing [1 ]
Guo, Haitao [1 ]
Zhao, Chuan [1 ]
Lin, Yuzhun [1 ]
Li, Daoji [1 ]
机构
[1] PLA Strateg Support Force Informat Engn Univ, Inst Geospatial Informat, Zhengzhou 450001, Peoples R China
基金
中国国家自然科学基金;
关键词
scene classification; remote sensing image; bilinear model; MobileNet; convolutional neural network; FEATURES; TEXTURE; DESCRIPTORS; RETRIEVAL; ATTENTION;
D O I
10.3390/s20071999
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
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.
引用
收藏
页数:25
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