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Analysis of Hybrid Classification Approach to Differentiate Dense and Non-dense Grass Regions

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Simulated Evolution and Learning (SEAL 2014)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 8886))

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Abstract

Vegetation classification from satellite and aerial images is a common research area for fire risk assessment and environmental surveys for decades. Recently classification from video data obtained by vehicle mounted video in outdoor environments is receiving considerable attention due to the large number of real-world applications. However this is a very challenging task and requires novel research techniques. This paper presents an analysis of hybrid classification approach to distinguish vegetation in particularly the type of roadside grasses from videos recorded by the Queensland transport and main roads. The proposed framework can distinguish dense and non-dense grass regions from roadside video data. While most of the recent works focuses on infrared images, proposed approach uses image texture feature for vegetation region classification. Analysis of hybrid approach using texture feature and multiple classifiers is the main contribution of this research work. The classifiers include: Support Vector Machine (SVM), Neural Network (NN), k- Nearest Neighbor (k-NN), AdaBoost and Naïve Bayes. The different images were created from video data containing roadside vegetation in various conditions for training and testing purposes. The hybrid classification approach has been analysed on roadside data obtained and results are discussed.

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Author information

Authors and Affiliations

  1. Central Queensland University, Australia

    Sujan Chowdhury & Brijesh Verma

  2. Queensland Transport and Main Roads, Australia

    David Stockwell

Authors
  1. Sujan Chowdhury
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  2. Brijesh Verma
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  3. David Stockwell
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Editor information

Editors and Affiliations

  1. Otago University, Dunedin, New Zealand

    Grant Dick

  2. Victoria University of Welling, New Zealand

    Will N. Browne

  3. University of Otago, Dunedin, New Zealand

    Peter Whigham

  4. Unitec Institute of Technology, Victoria University of Wellington, New Zealand

    Mengjie Zhang

  5. Le Quy Don Technical University, Hanoi, Vietnam

    Lam Thu Bui

  6. Department of Computer Science and Intelligent Systems, Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Naka-ku, 599-8531, Sakai, Osaka, Japan

    Hisao Ishibuchi

  7. Department of Computing, University of Surrey, GU2 7XH, Guildford, Surrey, UK

    Yaochu Jin

  8. RMIT University, Melbourne, Australia

    Xiaodong Li

  9. Department of Electrical & Electronic Engineering, Xi’an Jiaotong-Liverpool University, Suzhou, China

    Yuhui Shi

  10. Indian Institute of Information Technology and Management, Gwalior, India

    Pramod Singh

  11. Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, 117576, Singapore, Singapore

    Kay Chen Tan

  12. USTC-Birmingham Joint Research Institute in Intelligent Computation and Its Applications (UBRI), School of Computer Science and Technology, University of Science and Technology of China, 230027, Hefei, China

    Ke Tang

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© 2014 Springer International Publishing Switzerland

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Chowdhury, S., Verma, B., Stockwell, D. (2014). Analysis of Hybrid Classification Approach to Differentiate Dense and Non-dense Grass Regions. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_70

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  • DOI: https://doi.org/10.1007/978-3-319-13563-2_70

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13562-5

  • Online ISBN: 978-3-319-13563-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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