Abstract
Tile2vec has proven to be a good representation learning model in the remote sensing field. The success of the model depends on l2-norm regularization. However, l2-norm regularization has the main drawback that affects the regularization. We propose to replace the l2-norm with regularization with predicting noise framework. We then develop an algorithm to integrate the framework. We evaluate the model by using it as a feature extractor on the land cover classification task. The result shows that our proposed model outperforms all the baseline models.
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Appendices
A Model’s Architecture
The model adopts Resnet18 architecture with slight differences. Each row describes a residual block with particular kernels except the first row. All blocks set padding as 1.
Encoder | Note |
|---|---|
Conv(kernels=64, size=3, stride=1), B-Norm, ReLU | 1 Block |
Conv(kernels=64, size=3, stride=1), B-Norm, ReLU | 2 Blocks |
Conv(kernels=64, size=3, stride=1), B-Norm, ReLU | |
Conv(kernels=128, size=3, stride=2), B-Norm, ReLU | 2 Blocks |
Conv(kernels=128, size=3, stride=1), B-Norm, ReLU | |
Conv(kernels=256, size=3, stride=2), B-Norm, ReLU | 2 Blocks |
Conv(kernels=256, size=3, stride=1), B-Norm, ReLU | |
Conv(kernels=512, size=3, stride=2), B-Norm, ReLU | 2 Blocks |
Conv(kernels=512, size=3, stride=1), Batch Norm, ReLU | |
Conv(kernels=z, size=3, stride=2), Batch Norm, ReLU | 2 Blocks |
Conv(kernels=z, size=3, stride=1), Batch Norm, ReLU |
B Hyperparameters
Parameter | Value |
|---|---|
Learning rate | 0.02 |
\(\alpha \) | 1.0 |
Representation dimension | 512 |
m | 0.1 |
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Sinaga, M.A., Ali, F.M., Arymurthy, A.M. (2021). Tile2Vec with Predicting Noise for Land Cover Classification. In: Mantoro, T., Lee, M., Ayu, M.A., Wong, K.W., Hidayanto, A.N. (eds) Neural Information Processing. ICONIP 2021. Lecture Notes in Computer Science(), vol 13111. Springer, Cham. https://doi.org/10.1007/978-3-030-92273-3_8
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DOI: https://doi.org/10.1007/978-3-030-92273-3_8
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