A Lightweight Convolutional Neural Network Based on Channel Multi-Group Fusion for Remote Sensing Scene Classification

With the development of remote sensing scene image classification, convolutional neural networks have become the most commonly used method in this field with their powerful feature extraction ability. In order to improve the classification performance of convolutional neural networks, many studies e...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2022-01, Vol.14 (1), p.9
Main Authors: Shi, Cuiping, Zhang, Xinlei, Wang, Liguo
Format: Article
Language:English
Subjects:
Accuracy
Artificial neural networks
channel fusion
channel multi-group fusion (CMGF)
Classification
convolutional neural network (CNN)
Datasets
downsampling
Feature extraction
Image classification
Lightweight
Methods
Neural networks
Remote sensing
remote sensing scene image classification (RSSIC)
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Summary:With the development of remote sensing scene image classification, convolutional neural networks have become the most commonly used method in this field with their powerful feature extraction ability. In order to improve the classification performance of convolutional neural networks, many studies extract deeper features by increasing the depth and width of convolutional neural networks, which improves classification performance but also increases the complexity of the model. To solve this problem, a lightweight convolutional neural network based on channel multi-group fusion (LCNN-CMGF) is presented. For the proposed LCNN-CMGF method, a three-branch downsampling structure was designed to extract shallow features from remote sensing images. In the deep layer of the network, the channel multi-group fusion structure is used to extract the abstract semantic features of remote sensing scene images. The structure solves the problem of lack of information exchange between groups caused by group convolution through channel fusion of adjacent features. The four most commonly used remote sensing scene datasets, UCM21, RSSCN7, AID and NWPU45, were used to carry out a variety of experiments in this paper. The experimental results under the conditions of four datasets and multiple training ratios show that the proposed LCNN-CMGF method has more significant performance advantages than the compared advanced method.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14010009