  Scene classification is a fundamental problem to understand the
high-resolution remote sensing imagery. Recently, convolutional neural network
(ConvNet) has achieved remarkable performance in different tasks, and
significant efforts have been made to develop various representations for
satellite image scene classification. In this paper, we present a novel
representation based on a ConvNet with context aggregation. The proposed
two-pathway ResNet (ResNet-TP) architecture adopts the ResNet as backbone, and
the two pathways allow the network to model both local details and regional
context. The ResNet-TP based representation is generated by global average
pooling on the last convolutional layers from both pathways. Experiments on two
scene classification datasets, UCM Land Use and NWPU-RESISC45, show that the
proposed mechanism achieves promising improvements over state-of-the-art
methods.
