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Article

Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling

Institute of Theoretical and Applied Informatics, Polish Academy of Sciences; 44-100 Gliwice, Poland
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These authors contributed equally to this work.
Remote Sens. 2020, 12(16), 2653; https://doi.org/10.3390/rs12162653
Received: 22 July 2020 / Revised: 11 August 2020 / Accepted: 15 August 2020 / Published: 17 August 2020
(This article belongs to the Special Issue Computer Vision and Deep Learning for Remote Sensing Applications)
Hyperspectral imaging is a rich source of data, allowing for a multitude of effective applications. However, such imaging remains challenging because of large data dimension and, typically, a 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. The transfer learning approach can be used to alleviate the second requirement for a particular dataset: 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. The 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. View Full-Text
Keywords: hyperspectral image classification; deep learning; convolutional neural networks; transfer learning; unsupervised training sample selection hyperspectral image classification; deep learning; convolutional neural networks; transfer learning; unsupervised training sample selection
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MDPI and ACS Style

Masarczyk, W.; Głomb, P.; Grabowski, B.; Ostaszewski, M. Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling. Remote Sens. 2020, 12, 2653. https://doi.org/10.3390/rs12162653

AMA Style

Masarczyk W, Głomb P, Grabowski B, Ostaszewski M. Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling. Remote Sensing. 2020; 12(16):2653. https://doi.org/10.3390/rs12162653

Chicago/Turabian Style

Masarczyk, Wojciech, Przemysław Głomb, Bartosz Grabowski, and Mateusz Ostaszewski. 2020. "Effective Training of Deep Convolutional Neural Networks for Hyperspectral Image Classification through Artificial Labeling" Remote Sensing 12, no. 16: 2653. https://doi.org/10.3390/rs12162653

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