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Dimensionality Reduction of Hyperspectral Images Using Pooling

  • Representation, Processing, Analysis, and Understanding of Images
  • Published:
  • volume 29, pages 72–78 (2019)
Pattern Recognition and Image Analysis Aims and scope

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Abstract

Hyperspectral image having huge numbers of narrow and contiguous bands involves high computation complexity in processing and analysing the image. Hence dimensionality reduction is applied as an essential pre-processing step for hyperspectral data. Pooling is a technique of reducing spatial dimension and successfully applied in convolutional neural network. There are various types of pooling strategies present viz. max pool, mean pool and having their respective merits. In the present article, the concept of pooling is applied in the spectral dimension of the hyperspectral data to reduce the dimensionality and compared the result with standard reduction process like principal component analysis. Different pooling methods are applied and compared across and the mean pooling is found to be performing better. The results are compared in terms of overall accuracy and execution time.

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Authors and Affiliations

  1. Regional Remote Sensing Centre-East, ISRO, Kolkata, India

    Arati Paul

  2. Computer Science and Engineering, University of Calcutta, Kolkata, India

    Nabendu Chaki

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  1. Arati Paul
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  2. Nabendu Chaki
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Correspondence to Arati Paul.

Additional information

The article is published in the original.

Arati Paul is a Scientist in Regional Remote Sensing Centre – East, National Remote Sensing Centre, Indian Space Research Organisation. She has completed B.Tech., followed by M.Tech. in computer science and Engineering. Her area of work includes remote sensing, GIS, image processing and data analytics. She has published nearly 40 research papers and technical reports in her area of expertise.

Nabendu Chaki is a Professor in the Department Computer Science and Engineering, University of Calcutta, Kolkata, India. He is sharing 7 international patents including 4 US patents. Besides editing more than 30 conference proceedings with Springer, Dr. Chaki has authored 7 text and research books and nearly 200 Scopus Indexed research papers in Journals and International conferences. He has served as a Visiting Professor in different places including Naval Postgraduate School, USA; Ca Foscari University, Italy and AGH University in Poland. He is the founder Chair of ACM Professional Chapter in Kolkata and served in that capacity for 3 years since January 2014. He was active during 2009–2015 towards developing several international standards in Software Engineering and Service Science as a Global (GD) member for ISO-IEC.

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Paul, A., Chaki, N. Dimensionality Reduction of Hyperspectral Images Using Pooling. Pattern Recognit. Image Anal. 29, 72–78 (2019). https://doi.org/10.1134/S1054661819010085

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  • DOI: https://doi.org/10.1134/S1054661819010085

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