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Hyperspectral Image Classification Using Random Occlusion Data Augmentation
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DOI：10.1109/LGRS.2019.2909495.png)
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Convolutional neural networks (CNNs) have become a powerful tool for remotely sensed hyperspectral image (HSI) classification due to their great generalization ability and high accuracy. However, owing to the huge amount of parameters that need to be learned and to the complex nature of HSI data itself, these approaches must deal with the important problem of overfitting, which can lead to inadequate generalization and loss of accuracy. In order to mitigate this problem, in this letter, we adopt random occlusion, a recently developed data augmentation (DA) method for training CNNs, in which the pixels of different rectangular spatial regions in the HSI are randomly occluded, generating training images with various levels of occlusion and reducing the risk of overfitting. Our results with two well-known HSIs reveal that the proposed method helps to achieve better classification accuracy with low computational cost.
Keyword:
Training
Hyperspectral imaging
Computer architecture
Random access memory
Convolutional neural networks
Tools
Convolutional neural networks (CNNs)
hyperspectral images (HSIs)
random occlusion data augmentation (DA)
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