Label enhancement for multi-label classification of remote sensing images with missing labels

Multi-label classification of remote sensing (RS) images under the deep learning framework has attracted increasing attentions. However, manual annotation of RS images is time-consuming and easily results in missing labels due to the subjectivity of human perception. The deep learning models could suffer from performance drop because of the incomplete labels of the training set. To address the issue, a deep learning model with label enhancement is proposed for incomplete multi-label classification of RS images. The model consists of a semantic feature extraction (SFE) module, a label correlation learning (LCL) module and a label enhancement (LE) module. Firstly, the semantic features of RS images are extracted by an improved ResNet-101 in the SFE module. Secondly, the global label correlations are learned by a graph convolutional network (GCN) in the LCL module. Subsequently, the labels predicted according to the semantic features are enhanced by the label correlations and local semantics in the LE module. Finally, a weighted combination of the label probability generated by the semantic features and the enhanced one through the LE module is applied in the loss function for classification. The multi-label classification experiments on three public RS image datasets prove the effectiveness of our model. © 2024 Informa UK Limited, trading as Taylor & Francis Group.