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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >Hyperspectral Image Classification Using Random Occlusion Data Augmentation
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Hyperspectral Image Classification Using Random Occlusion Data Augmentation

机译：使用随机遮挡数据增强的高光谱图像分类

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摘要

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.
机译：卷积神经网络（CNN）具有强大的归纳能力和较高的准确性，已成为遥感高光谱图像（HSI）分类的强大工具。但是，由于需要学习大量的参数以及HSI数据本身的复杂性，这些方法必须解决重要的过拟合问题，这可能导致泛化不足和准确性下降。为了缓解这个问题，在本文中，我们采用随机遮挡，这是一种最近开发的用于训练CNN的数据增强（DA）方法，其中HSI中不同矩形空间区域的像素被随机遮挡，从而生成具有各种图像的训练图像闭塞程度并降低过度拟合的风险。我们的两个著名HSI的结果表明，该方法有助于以较低的计算成本实现更好的分类精度。

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