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Cluster Computing

, Volume 22, Supplement 1, pp 443–450 | Cite as

A novel SMLR-PSO model to estimate the chlorophyll content in the crops using hyperspectral satellite images

  • Archana NandibewoorEmail author
  • Ravindra Hegadi
Article

Abstract

The estimating the chlorophyll contents in the crops helps to identify the condition of crops and different classification of crops with soil characteristics in order to assist the farmer or others with agriculture growth. In this paper, a hybrid approach is introduced to estimate the Chlorophyll contents in the crops using hyperspectral image segmentation with active learning, which consists of two main steps. First, we use a sparse multinomial logistic regression (SMLR) model to learn the class posterior probability distributions with Quadratic Programming or joint probability distribution. Second, we use the information acquired in the previous step to segment the hyper spectral image using a Markov Random field segments to estimate the dependencies using spatial information and edge Information by minimum spanning forest rooted on markers. In order to reduce the cost of acquiring large training sets, PSO optimization is performed based on the SMLR posterior probabilities on the Normalized difference vegetation index (NDVI). The state-of-the-art performance of the proposed approach is illustrated using real hyper spectral data sets collected from the North Karnataka in a number of experimental comparisons with recently developed or statistical hyperspectral image analysis methods in terms of precision, recall and f—measure.

Keywords

Hyperspectral image analysis NDVI index Particle swarm optimization Spatio-spectral analysis Feature selection 

Notes

Acknowledgements

The authors would like to thank the Dr. S. B. Vanakudre, Principal, SDMCET, Dharwad, and Dr. V. S. Hegde, Geology Laboratories, Department of Civil Engineering, SDMCET, Dharwad, India for providing the required facilities. Authors would like to acknowledge Dr. Prashant K Srivatsava, Department of Environmental Science, Banaras Hindu University, Varanasi, for his timely support. Authors extend their gratitude to the staff members of Department of Computer Science and Engineering, SDMCET, Dharwad, India and Agricultural University, Dharwad for their support and information.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research and Development CentreBharathiar UniversityCoimbatoreIndia
  2. 2.Department of CSESDM College of Engineering and TechnologyDharwadIndia
  3. 3.School of Computational ScienceSolapur UniversitySolapurIndia

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