基于有效通道注意力的遥感图像场景分类

针对基于人工设计特征的方法不能提取高层次遥感图像信息以及以往利用VGGNet、ResNet等卷积神经网络（CNN）无法关注到遥感图像中显著分类特征的问题，提出了一种基于有效通道注意力（ECA）机制的遥感图像场景分类新模型——ECA-ResNeXt-8-SVM。为了建立高效模型，一方面，设计了嵌入ECA模块的深度特征提取网络ECA-ResNeXt-8，通过端到端的学习使网络更关注分类特征明显的通道；另一方面，利用支持向量机（SVM）代替全连接层作为已提取到的深度特征的分类器，从而进一步提高模型的分类准确率与泛化能力。该模型在实验数据集UC Merced Land-Use上的分类准确率达到95.81%，相较于使用SE-ResNeXt50与ResNeXt50网络，分别提高了6%与18%，且在分类准确率为75%时所提模型的训练时间比上述两个网络分别减少了82%与81%。实验结果表明，所提模型能够有效地减少模型的收敛时间并提升遥感图像场景分类的准确率。

Remote sensing image scene classification based on effective channel attention

The methods based on artificially designed features cannot extract high-level information from remote sensing images and previously used Convolutional Neural Network （CNN） such as VGGNet and ResNet cannot focus on distinguishable classification features in remote sensing images. In order to solve the problems， a novel method called ECA-ResNeXt-8-SVM was proposed based on Effective Channel Attention （ECA） mechanism for remote sensing image scene classification. In order to build an effective model， a deep feature extraction network called ECA-ResNeXt-8 embedded with the ECA module was designed， and the end-to-end learning was used to make network lay emphasis on channels with distinguishable classification features. At the same time， Support Vector Machine （SVM） was utilized to replace the fully connected layer as the classifier of the extracted deep features， which helped to improve the classification accuracy and generalization ability of model. On the experimental dataset UC Merced Land-Use， the classification accuracy of the proposed model reaches 95.81%， which is increased by 6% and 18% compared to SE-ResNeXt50 and ResNeXt50 networks respectively. When the classification accuracy is 75%， the proposed model has the training time reduced by 82% and 81% compared to the two above networks respectively. Experimental results show that the proposed model can reduce the convergence time of model effectively and improve the classification accuracy for remote sensing image scene.