Abstract
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 [1] 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.
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Notes
- 1.
The download link can be found from https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py.
- 2.
The stochastic gradient descent (SGD) is used with batch size of 64 and momentum of 0.9. The learning rate is initially set to be 0.01 and is divided by 10 every 30 epochs.
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Acknowledgments
This work was supported in part by grants from National Natural Science Foundation of China (No. 61602459) and Science and Technology Commission of Shanghai Municipality (No. 17511101902 and No. 18511103103).
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Zhou, Z., Zheng, Y., Ye, H., Pu, J., Sun, G. (2018). Satellite Image Scene Classification via ConvNet With Context Aggregation. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_31
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