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Identification of Interfaces of Mixtures with Nonlinear Models

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Proceedings of the Global AI Congress 2019

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1112))

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Abstract

The study attempts to identify the most appropriate unmixing model to detect endmember mixtures in a hyperspectral image scene. Least squares error-based linear spectral unmixing and gradient descent maximum entropy unmixing models have been used to identify linear mixtures in the datasets. Fuzzy C-Means-based unmixing algorithm extracts the interface of two major endmembers which is similar to the feature of the nonlinear spectral unmixing algorithms. Two popular bilinear models, Nascimento’s model and Fan’s model, have been studied. This study has been done on two hyperspectral datasets, i.e., homogeneous and heterogeneous. It is observed that the endmember interfaces detected by Fuzzy C-Means are more prominent in the dataset that has well-defined boundaries between endmembers, whereas Nascimento’s and Fan’s models show more accuracy in the dataset containing dense mixtures. The image derived fractional abundance values estimated by each model has been validated from ground abundance values after field visits.

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Acknowledgements

We acknowledge the Department of Science and Technology, Government of India, for providing necessary funds to the major research project “Development of Algorithms for Spectral Unmixing and Sub-Pixel Classification of Hyperspectral Image Data.”

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Authors and Affiliations

  1. Department of Information Technology, Maulana Abul Kalam Azad University of Technology, West Bengal, Kolkata, West Bengal, India

    Srirupa Das & Somdatta Chakravortty

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  1. Srirupa Das
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  2. Somdatta Chakravortty
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Correspondence to Srirupa Das .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  2. Department of Computer Science and Engineering, Assam University, Silchar, Assam, India

    Somnath Mukhopadhyay

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Das, S., Chakravortty, S. (2020). Identification of Interfaces of Mixtures with Nonlinear Models. In: Mandal, J., Mukhopadhyay, S. (eds) Proceedings of the Global AI Congress 2019. Advances in Intelligent Systems and Computing, vol 1112. Springer, Singapore. https://doi.org/10.1007/978-981-15-2188-1_19

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