  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.
