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Classification of Hybrid Multiscaled Remote Sensing Scene Using Pretrained Convolutional Neural Networks

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Computational Vision and Bio-Inspired Computing ( ICCVBIC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1108))

Abstract

A novel hybrid multiscaled Remote Sensed (RS) image classification method based on spatial-spectral feature extraction using pretrained neural network approach is proposed in this paper. The spectral and spatial features like colour, texture and edge of RS images are extracted by using nonlinear spectral unmixing which is further scaled by using bilinear interpolation. The same RS image in parallel path is first scaled using bilinear interpolation and further spectrally unmixed. These two paths are further fused together using spatial-spectral fusion to give multiscaled RS image which is further given to a pretrained network for feature extraction. For authentication and discrimination purposes, the proposed approach is evaluated via experiments with five challenging high-resolution remote sensing data sets and two famously used pretrained network (Alexnet/Caffenet). The experimental results provides classification accuracy of about 98% when classified at multiscale level compared to 83% when classified at single scale level using pretrained convolutional networks.

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Acknowledgements

This work is supported in part by NVIDIA GPU grant program. We thank NVIDIA for giving us Titan XP GPU as a grant to carry out our work in deep learning. We also thank the anonymous reviewers for their insightful comments.

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Authors and Affiliations

  1. Thakur College of Engineering and Technology, Thakur Village, Kandivali - (East), Mumbai, 400101, Maharashtra, India

    Sujata Alegavi & Raghvendra Sedamkar

Authors
  1. Sujata Alegavi
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  2. Raghvendra Sedamkar
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Corresponding author

Correspondence to Sujata Alegavi .

Editor information

Editors and Affiliations

  1. Department of CSE, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Faculty of Engineering, Faculdade de Engenharia da Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Faculty of Engineering, Aurel Vlaicu University of Arad, Arad, Romania

    Valentina Emilia Balas

  4. School of Computing, Tokyo Institute of Technology, Tokyo, Japan

    Abdullah M. Iliyasu

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✓ All authors declare that there is no conflict of interest

✓ No humans/animals involved in this research work.

✓ We have used our own data.

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Alegavi, S., Sedamkar, R. (2020). Classification of Hybrid Multiscaled Remote Sensing Scene Using Pretrained Convolutional Neural Networks. In: Smys, S., Tavares, J., Balas, V., Iliyasu, A. (eds) Computational Vision and Bio-Inspired Computing. ICCVBIC 2019. Advances in Intelligent Systems and Computing, vol 1108. Springer, Cham. https://doi.org/10.1007/978-3-030-37218-7_17

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