School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China.
Comput Intell Neurosci. 2022 Oct 11;2022:2661231. doi: 10.1155/2022/2661231. eCollection 2022.
Remote-sensing image scene data contain a large number of scene images with different scales. Traditional scene classification algorithms based on convolutional neural networks are difficult to extract complex spatial distribution and texture information in images, resulting in poor classification results. In response to the above problems, we introduce the vision transformer network structure with strong global modeling ability into the remote-sensing image scene classification task. In this paper, the parallel network structure of the local-window self-attention mechanism and the equivalent large convolution kernel is used to realize the spatial-channel modeling of the network so that the network has better local and global feature extraction performance. Experiments on the RSSCN7 dataset and the WHU-RS19 dataset show that the proposed network can improve the accuracy of scene classification. At the same time, the effectiveness of the network structure in remote-sensing image classification tasks is verified through ablation experiments, confusion matrix, and heat map results comparison.
遥感图像场景数据包含大量不同尺度的场景图像。传统的基于卷积神经网络的场景分类算法难以提取图像中复杂的空间分布和纹理信息，导致分类效果较差。针对上述问题，我们将具有较强全局建模能力的视觉Transformer 网络结构引入到遥感图像场景分类任务中。本文采用局部窗口自注意力机制和等效大卷积核的并行网络结构，实现网络的空间-通道建模，使网络具有更好的局部和全局特征提取性能。在 RSSCN7 数据集和 WHU-RS19 数据集上的实验表明，所提出的网络可以提高场景分类的准确性。同时，通过消融实验、混淆矩阵和热图结果比较验证了网络结构在遥感图像分类任务中的有效性。
