Multi-Label Remote Sensing Image Classification (MLRSIC) has received increasing research interest. Taking the cooccurrence relationship of multiple labels as additional information helps to improve the performance of this task. Current methods focus on using it to constrain the final feature output of a Convolutional Neural Network (CNN). On the one hand, these methods do not make full use of label correlation to form feature representation. On the other hand, they increase the label noise sensitivity of the system, resulting in poor robustness. In this paper, a novel method called Semantic Interleaving Global Channel Attention (SIGNA) is proposed for MLRSIC. First, the label co-occurrence graph is obtained according to the statistical information of the data set. The label co-occurrence graph is used as the input of the Graph Neural Network (GNN) to generate optimal feature representations. Then, the semantic features and visual features are interleaved, to guide the feature expression of the image from the original feature space to the semantic feature space with embedded label relations. SIGNA triggers global attention of feature maps channels in a new semantic feature space to extract more important visual features. Multihead SIGNA based feature adaptive weighting networks are proposed to act on any layer of CNN in a plug-and-play manner. For remote sensing images, better classification performance can be achieved by inserting CNN into the shallow layer. We conduct extensive experimental comparisons on three data sets: UCM data set, AID data set, and DFC15 data set. Experimental results demonstrate that the proposed SIGNA achieves superior classification performance compared to state-of-the-art (SOTA) methods. It is worth mentioning that the codes of this paper will be open to the community for reproducibility research. Our codes are available at https://github.com/kyle-one/SIGNA.

翻译：多频遥感图像分类( MLISIC) 已经受到越来越多的研究兴趣。 作为有助于改进此任务绩效的额外信息, 多个标签的叠加关系已经得到了越来越多的研究兴趣 。 由于多个标签的叠加关系有助于改进此任务的绩效 。 目前的方法侧重于使用它来限制神经神经神经网络(CNN)的最终特性输出 。 一方面, 这些方法没有充分利用标签关联来形成特征表达。 另一方面, 它们增加了系统的标签噪声敏感性, 导致系统不够稳健。 在本文中, 提议了一种名为 SMRISIC Interleading Global Guide 注意的新方法。 首先, 根据数据集的统计信息, 获取了标签的叠加关系 。 标签共叠加图形图形图形图形图形图形图表是用来限制进化神经神经神经网络的最终特性输出功能。 然后, 语系特征特征和视觉图像显示的图像表达方式可以从原始地段空间到内嵌标签系统。 Signal- SINA 将让全球地段高级地图频道的更好关注到新的Slevia- develyal developmental exal exal a ex develop ex exmade made made madeal made max the the the max max max max max max mais