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Hybrid 3D-CNN Based Airborne Hyperspectral Image Classification with Extended Morphological Profiles Features

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Modeling, Simulation and Optimization

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 292))

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Abstract

The classification of high-definition hyperspectral images from metropolitan locations needs to resolve specific critical issues. The conventional morphological openings and closures weaken object barriers and deform item forms, making the process more challenging. The morphological openings and closings by reconstruction can circumvent this issue to an extent with a few unintended effects. The images that are anticipated to vanish at a particular scale stay available later when morphological openings and closings are done. The extended morphological profiles (EMPs) with unique structuring factors and a growing number of morphological operators generate extremely high-dimensional data. These multidimensional facts may also contain duplicated data, creating a brand-new classification task for standard classification algorithms, particularly for classifiers that are not resistant to the Hughes phenomenon. In this article, we examine extended morphological profiles with partial reconstruction and directed MPs to categorize high-definition hyperspectral snap images from urban locations. Second, we expand it using a Hybrid 3D CNN to boost classification performance. In this article, the total accuracy of 99.42% is obtained with a small number of testing samples. Similarly, the average accuracy is reasonable when compared to other 2D and 3D convolutional neural networks.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India

    R. Anand & S. Veni

  2. Department of CEN, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India

    P. Geetha

Authors
  1. R. Anand
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  2. S. Veni
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  3. P. Geetha
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Corresponding author

Correspondence to R. Anand .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, National Institute of Technology Silchar, Silchar, India

    Biplab Das

  2. Department of Computer Science and Engineering, National Institute of Technology Silchar, Silchar, India

    Ripon Patgiri

  3. National Institute of Technology Silchar, Silchar, India

    Sivaji Bandyopadhyay

  4. Department of Automatics and Applied Software, “Aurel Vlaicu” University of Arad, Arad, Romania

    Valentina Emilia Balas

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Anand, R., Veni, S., Geetha, P. (2022). Hybrid 3D-CNN Based Airborne Hyperspectral Image Classification with Extended Morphological Profiles Features. In: Das, B., Patgiri, R., Bandyopadhyay, S., Balas, V.E. (eds) Modeling, Simulation and Optimization. Smart Innovation, Systems and Technologies, vol 292. Springer, Singapore. https://doi.org/10.1007/978-981-19-0836-1_39

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