Label-Driven Graph Convolutional Network for Multilabel Remote Sensing Image Classification
Boyi Ma, Falin Wu, Tianyang Hu, Loghman Fathollahi, Xiaohong Sui, Yushuang Liu, Byambakhuu Gantumur
Abstract
Multi-label classification in remote sensing is very significant which 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 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 paper 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. label recognition GCN (LRGCN) and 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 multi-label remote sensing datasets illustrate that LD-GCN outperforms the state-of-the-art methods on key evaluation metrics.