Label Relation Inference for Multi-Label Aerial Image Classification

Multi-label aerial image classification is a challenging visual task and obtaining increasing attention recently. Most of the existing methods resort to training independent classifier for each label, while underlying label correlations are not fully exploited while making predictions. To this end, we propose an innovative inference network, which takes advantage of pairwise label relations to infer multiple object labels of a high-resolution aerial image. Specifically, we first employ a feature extraction module to extract high-level feature representations of an aerial image, and then, feed them into a relational inference module to predict the presence of each object label. We evaluate our network on the UCM multilabel dataset and experiment with various popular convolutional neural networks (CNNs) as the backbone of the feature extraction module. Experimental results demonstrate that the proposed network behaves superiorly in comparison with other existing methods.