ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

Deep TEC: Deep transfer learning with ensemble classifier for road extraction from UAV imagery

Senthilnath, J and Varia, N and Dokania, A and Anand, G and Benediktsson, JA (2020) Deep TEC: Deep transfer learning with ensemble classifier for road extraction from UAV imagery. In: Remote Sensing, 12 (2).

rem_sen_12-02_2020.pdf - Published Version

Download (2MB) | Preview
Official URL: https://dx.doi.org/10.3390/rs12020245


Unmanned aerial vehicle (UAV) remote sensing has a wide area of applications and in this paper, we attempt to address one such problem-road extraction from UAV-captured RGB images. The key challenge here is to solve the road extraction problem using the UAV multiple remote sensing scene datasets that are acquired with different sensors over different locations. We aim to extract the knowledge from a dataset that is available in the literature and apply this extracted knowledge on our dataset. The paper focuses on a novel method which consists of deep TEC (deep transfer learning with ensemble classifier) for road extraction using UAV imagery. The proposed deep TEC performs road extraction on UAV imagery in two stages, namely, deep transfer learning and ensemble classifier. In the first stage, with the help of deep learning methods, namely, the conditional generative adversarial network, the cycle generative adversarial network and the fully convolutional network, the model is pre-trained on the benchmark UAV road extraction dataset that is available in the literature. With this extracted knowledge (based on the pre-trained model) the road regions are then extracted on our UAV acquired images. Finally, for the road classified images, ensemble classification is carried out. In particular, the deep TEC method has an average quality of 71, which is 10 higher than the next best standard deep learning methods. Deep TEC also shows a higher level of performance measures such as completeness, correctness and F1 score measures. Therefore, the obtained results show that the deep TEC is efficient in extracting road networks in an urban region. © 2020 by the authors.

Item Type: Journal Article
Publication: Remote Sensing
Publisher: MDPI AG
Additional Information: The copyright of this article belongs to MDPI AG
Keywords: Antennas; Data mining; Extraction; Feature extraction; Image classification; Learning systems; Remote sensing; Road vehicles; Roads and streets; Transfer learning; Unmanned aerial vehicles (UAV), Adversarial networks; Convolutional networks; Ensemble classification; Ensemble classifiers; Learning methods; Performance measure; Road extraction; Urban regions, Deep learning
Department/Centre: Division of Mechanical Sciences > Aerospace Engineering(Formerly Aeronautical Engineering)
Date Deposited: 30 Jun 2020 06:50
Last Modified: 30 Jun 2020 06:50
URI: http://eprints.iisc.ac.in/id/eprint/64903

Actions (login required)

View Item View Item