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Hyperspectral Remote Sensing Image Classification Using Active Learning

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Machine Learning Algorithms for Industrial Applications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 907))

Abstract

Hyperspectral remote sensing images capture a large number of narrow spectral bands ranging between visible and infrared spectrum. The abundant spectral data provides huge land cover information that helps in accurate classification of land use land cover of earth’s surface. However, obtaining labelled training data in hyperspectral images (HSIs) is labour expensive and time consuming. Therefore, designing a classifier that uses fewer labelled samples as possible for classification is highly desirable. Active learning (AL) is a branch of machine learning that finds most uncertain samples in an iterative way from unlabelled dataset resulting relatively smaller training set to achieve adequate classification accuracy. Support vector machine (SVM) has been extensively used as a classifier in AL approach. However, it has high computational complexity. Recently, a non-iterative learning algorithm based on least square solution known as extreme learning machine (ELM), has been integrated in AL framework for HSI classification. It provides a comparable classification accuracy while reducing computation time drastically.

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Authors and Affiliations

  1. School of Electronics Engineering, Kalinga Institute of Industrial Technology (KIIT), Bhubaneswar, India

    Vimal K. Shrivastava

  2. Department of Agricultural Statistics and Social Sciences (L), Indira Gandhi Agricultural University, Raipur, India

    Monoj K. Pradhan

Authors
  1. Vimal K. Shrivastava
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  2. Monoj K. Pradhan
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Correspondence to Vimal K. Shrivastava .

Editor information

Editors and Affiliations

  1. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

    Santosh Kumar Das

  2. School of Computer Science and Engineering, National Institute of Science and Technology (Autonomous), Berhampur, Odisha, India

    Shom Prasad Das

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Founder and Head of the Egyptian Scientific Research Group (SRGE), Information Technology Department, Cairo University, Faculty of Computer and Artificial Intelligence, Giza, Egypt

    Aboul-Ella Hassanien

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Shrivastava, V.K., Pradhan, M.K. (2021). Hyperspectral Remote Sensing Image Classification Using Active Learning. In: Das, S., Das, S., Dey, N., Hassanien, AE. (eds) Machine Learning Algorithms for Industrial Applications. Studies in Computational Intelligence, vol 907. Springer, Cham. https://doi.org/10.1007/978-3-030-50641-4_8

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