Effective training of deep convolutional neural networks for hyperspectral image classification through artificial labeling

TL;DR

This paper introduces an unsupervised transfer learning strategy for hyperspectral image classification that significantly improves accuracy without requiring labeled data, making deep learning more practical for limited datasets.

Contribution

It proposes a simple, effective unsupervised pre-training method for deep neural networks in hyperspectral classification, overcoming the need for large labeled datasets.

Findings

Over 21% accuracy improvement on Indian Pines dataset

Over 13% accuracy improvement on Pavia University dataset

Effective across various neural network architectures and training set sizes

Abstract

Hyperspectral imaging is a rich source of data, allowing for multitude of effective applications. However, 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 at improving the classification accuracy without being restricted to a particular image type or neural network architecture. The experiments were carried out on several deep neural network architectures and various sizes of labeled training sets. The greatest improvement in overall accuracy on the Indian Pines and Pavia University datasets is over 21 and 13 percentage points, respectively. 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.