  Multi-label classification plays a momentous role in perceiving intricate
contents of an aerial image and triggers several related studies over the last
years. However, most of them deploy few efforts in exploiting label relations,
while such dependencies are crucial for making accurate predictions. Although
an LSTM layer can be introduced to modeling such label dependencies in a chain
propagation manner, the efficiency might be questioned when certain labels are
improperly inferred. To address this, we propose a novel aerial image
multi-label classification network, attention-aware label relational reasoning
network. Particularly, our network consists of three elemental modules: 1) a
label-wise feature parcel learning module, 2) an attentional region extraction
module, and 3) a label relational inference module. To be more specific, the
label-wise feature parcel learning module is designed for extracting high-level
label-specific features. The attentional region extraction module aims at
localizing discriminative regions in these features and yielding attentional
label-specific features. The label relational inference module finally predicts
label existences using label relations reasoned from outputs of the previous
module. The proposed network is characterized by its capacities of extracting
discriminative label-wise features in a proposal-free way and reasoning about
label relations naturally and interpretably. In our experiments, we evaluate
the proposed model on the UCM multi-label dataset and a newly produced dataset,
AID multi-label dataset. Quantitative and qualitative results on these two
datasets demonstrate the effectiveness of our model. To facilitate progress in
the multi-label aerial image classification, the AID multi-label dataset will
be made publicly available.
