Hyperspectral imaging is a rich source of data, allowing for multitude of effective applications. On the other hand such imaging remains challenging because of large data dimension and, typically, small pool of available training examples. While deep learning approaches have been shown to be successful in providing effective classification solutions, especially for high dimensional problems, unfortunately they work best with a lot of labelled examples available. To alleviate the second requirement for a particular dataset the transfer learning approach can be used: first the network is pre-trained on some dataset with large amount of training labels available, then the actual dataset is used to fine-tune the network. This strategy is not straightforward to apply with hyperspectral images, as it is often the case that only one particular image of some type or characteristic is available. In this paper, we propose and investigate a simple and effective strategy of transfer learning that uses unsupervised pre-training step without label information. This approach can be applied to many of the hyperspectral classification problems. Performed experiments show that it is very effective in improving the classification accuracy without being restricted to a particular image type or neural network architecture. An additional advantage of the proposed approach is the unsupervised nature of the pre-training step, which can be done immediately after image acquisition, without the need of the potentially costly expert's time.
翻译:超光谱成像是一种丰富的数据来源,它允许多种有效的应用。另一方面,这种成像由于庞大的数据维度和一般而言,现有培训实例很少,仍然具有挑战性。虽然深层次学习方法在提供有效的分类解决方案方面证明是成功的,特别是对于高维问题而言,但不幸的是,它们使用许多贴有标签的例子最有效。为了减轻对特定数据集的第二个要求,可以使用传输学习方法:首先,网络在具有大量培训标签的某些数据集上预先接受了培训,然后实际的数据集被用来微调网络。这一战略并非直接适用于超光谱图像,因为通常只有某种特定类型的或特征的图像。在本文件中,我们建议并调查一个简单有效的转移学习战略,在没有标签信息的情况下使用未经监督的培训前步骤。这一方法可以适用于许多超光谱分类问题。进行实验表明,在改进分类准确性方面非常有效,而不局限于特定的图像类型或神经网络结构。这个战略并非简单易应用,因为通常只提供某种特定类型的或特征的图像。在高光谱网络结构下,拟议的方法的另一个优点是获得成本很高的图像,在不需进行专家培训之后,因此立即获得可能进行这种程度的升级。