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Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery

Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (2), p.245 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 2072-4292 ;EISSN: 2072-4292 ;DOI: 10.3390/rs12020245

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  • Title:
    Deep TEC: Deep Transfer Learning with Ensemble Classifier for Road Extraction from UAV Imagery
  • Author: Senthilnath, J. ; Varia, Neelanshi ; Dokania, Akanksha ; Anand, Gaotham ; Benediktsson, Jón Atli
  • Subjects: Aerial photography ; Algorithms ; Classifiers ; Color imagery ; Datasets ; Deep learning ; ensemble classifier ; Generative adversarial networks ; Global positioning systems ; GPS ; Image acquisition ; Image classification ; Machine learning ; Neural networks ; Remote sensing ; Remote sensors ; road extraction ; Roads ; Roads & highways ; Semantics ; Transfer learning ; uav ; Unmanned aerial vehicles ; Urban areas
  • Is Part Of: Remote sensing (Basel, Switzerland), 2020-01, Vol.12 (2), p.245
  • Description: 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.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 2072-4292
    EISSN: 2072-4292
    DOI: 10.3390/rs12020245
  • Source: Directory of Open Access Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
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