A Survey on Hyperspectral Image Segmentation Approaches with the Integration of Numerical Techniques

  • Satish Kumar SoniEmail author
  • Ramjeevan Singh Thakur
  • Anil Kumar Gupta
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 34)


Technological advancements in various sectors of science and IT-enabled services have broadened the spectrum and quantum of data in very large scale. The picture is even more furious than assumptions, and a huge volume of static and dynamic data is being produced every second by various means such as air traffic control systems, remote sensing and GPS satellites, social media, imaging techniques used in medical radiology and so many other datasets in the form of texts, images, audios, videos, etc. Same way, a very rich dataset is generated by ultra-sensitive electronic sensors which are used in modern imaging systems, known as hyperspectral images. This data may give variety of information, useful for solving the problems of real world, but gathering or extracting meaningful information from that much data are as difficult as ‘getting the needle from a haystack.’ Machine learning approaches have been proven to be useful for analyzing large datasets of various types and formats. This paper is an honest effort of presenting a comprehensive survey of various machine learning approaches like clustering along with numerical methods used by researchers worldwide for analyzing hyperspectral image data.


Machine learning Datasets Hyperspectral images Numerical techniques 


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Satish Kumar Soni
    • 1
    Email author
  • Ramjeevan Singh Thakur
    • 2
  • Anil Kumar Gupta
    • 1
  1. 1.Department of Computer Science and ApplicationsBarkatullaha UniversityBhopalIndia
  2. 2.Department of Mathematics and Computer ApplicationsMaulana Azad National Intitute of TechnologyBhopalIndia

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