江西科学2024，Vol.42Issue(2)：396-404,9.DOI:10.13990/j.issn1001-3679.2024.02.027
基于ResNet50与通道注意力的遥感图像场景分类
Remote Sensing Image Scene Classification Based on ResNet50 and Channel Attention
摘要
Abstract
Convolutional neural networks(CNNs)are widely used for image classification.Howev-er,directly applying traditional CNNs to remote sensing image scene classification has certain limita-tions.Remote sensing images exhibit large intra-class differences and high intra-class similarities,making it challenging for networks to accurately extract image features,thus posing significant diffi-culties in remote sensing image classification tasks.The channel attention mechanism,with its abili-ty to focus on important features while ignoring less relevant ones,can enhance the recognition capa-bility of CNNs for image features.Therefore,a network model combining ResNet50 and channel at-tention,called ResNet50+Attention,is proposed.The UCMD data set is used with the initialization of network parameters for remote sensing scene image classification tasks.Classic network models in-cluding AlexNet,DenseNet,VGG16,and GoogLeNet are compared.ResNet50+Attention outper-forms other models significantly in terms of overall accuracy,precision,recall,and specificity clas-sification metrics.Disintegration experiments with the base ResNet50 model are conducted,inclu-ding accuracy curves,confusion matrices,and individual class classification metrics.The results show that ResNet50+Attention achieves overall accuracy,precision,recall,and specificity of 91.7%,92.1%,91.8%,and 99.6%,respectively,showing improvements of 4%,3.8%,4%,and 0.2%compared to ResNet50,thus confirming the effectiveness of this network model.关键词
卷积神经网络/图像分类/注意力机制/遥感图像/ResNet50Key words
convolutional neural network/image classification/attention mechanism/remote sensing images/ResNet50分类
信息技术与安全科学引用本文复制引用
逯登科,罗亦泳,张紫怡,张震,田晓鹏..基于ResNet50与通道注意力的遥感图像场景分类[J].江西科学,2024,42(2):396-404,9.基金项目
江西省自然科学基金项目(20202BABL204070). （20202BABL204070）
