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Shortwave Infrared-Based Phenology Index Method for Satellite Image Land Cover Classification

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1057))

Abstract

Recent technology relay upon the satellites. Satellites are having their own technical property to its behavioral methods. Communication methodology and sensor techniques are very important to take earth imagery. Satellites take the images in various combinations of bands to produce the imagery with maximum details. The efficacy of the multispectral imagery is limitless. Multispectral imagery is having the details about a particular region using 11 band combinations. Grouping the pixels into the meaningful class is called as image classification. Satellite image classification is used to define the land surface segmentation and feature extraction. Weather forecasting, agriculture, air force department, and water department are the major departments which rely upon the satellite imagery. Remote sensing input for those real-time applications is endless. Satellite image classification falls with three categories as automatic, semi-automated, and hybrid methods. Medium resolution multi spectral imagery classification algorithms designed with Automatic and Manual classification method to reach the maximum accuracy. High and very high spectral imagery classification algorithms follow the hybrid- or object-based image analysis. Phonology index is designed to classify the multispectral satellite imagery based on the reflectance value of the passive sensor imagery. This paper provides a phenology-based method called shortwave infrared-based phenology index (SIPI) to classify the multispectral imagery with maximum accuracy. Confusion matrix and Kappa coefficient are the quality measures used to justify the classification efficiency.

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Acknowledgment

This article has been written with the financial support of RUSA – phase 2.0 grant sanctioned via Letter No F.24-51 / 2014-U, Policy (TNMulti-Gen), Dept. of Edn. Govt. of India, Dt. 09.10.2018.

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Authors and Affiliations

  1. Department of Computer Science, Alagappa University, Karaikudi, India

    KR. Sivabalan & E. Ramaraj

Authors
  1. KR. Sivabalan
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  2. E. Ramaraj
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Corresponding author

Correspondence to KR. Sivabalan .

Editor information

Editors and Affiliations

  1. Department of Mathematics, National Institute of Technology Silchar, Silchar, Assam, India

    Kedar Nath Das

  2. Department of Mathematics, South Asian University, New Delhi, Delhi, India

    Jagdish Chand Bansal

  3. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, Uttarakhand, India

    Kusum Deep

  4. Department of Mathematics, Faculty of Science, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

  5. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Ponnambalam Pathipooranam

  6. School of Electrical Engineering, VIT University, Vellore, Tamil Nadu, India

    Rani Chinnappa Naidu

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Sivabalan, K., Ramaraj, E. (2020). Shortwave Infrared-Based Phenology Index Method for Satellite Image Land Cover Classification. In: Das, K., Bansal, J., Deep, K., Nagar, A., Pathipooranam, P., Naidu, R. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1057. Springer, Singapore. https://doi.org/10.1007/978-981-15-0184-5_75

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