Label-Driven Graph Convolutional Network for Multilabel Remote Sensing Image Classification

Abstract

Multilabel classification in remote sensing is very significant and plays an important role in extracting valuable information from satellite imagery. Ignoring the distinct information provided by labels in each image or transforming images into content-aware category representations without considering the inherent correlation of labels within the dataset can result in the establishment of improper relationships between images and labels, ultimately leading to a significant degradation in accuracy. To address this problem, this article proposes a label-driven graph convolutional network (LD-GCN) to excavate substantial information using the inherent correlation of labels from datasets and build a strong relationship between labels and images. The framework consists of two modules, i.e., the label recognition GCN (LRGCN) and the semantic enrichment module (SEM). The LRGCN module yields rich and valuable information from the inherent correlation of labels and builds a strong relationship between images and labels. The SEM further enriches the semantics obtained from LRGCN. Experiments conducted on UCM, AID, and DFC15 multilabel remote sensing datasets illustrate that LD-GCN outperforms the state-of-the-art methods on key evaluation metrics.

Keywords

• Graph convolutional network (GCN)
• label-driven GCN
• multilabel image classification
• remote sensing