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Optimal Band Selection Using Generalized Covering-Based Rough Sets on Hyperspectral Remote Sensing Big Data

  • Harika KelamEmail author
  • M. Venkatesan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 750)

Abstract

Hyperspectral remote sensing has been gaining attention from the past few decades. Due to the diverse and high dimensionality nature of the remote sensing data, it is called as remote sensing Big Data. Hyperspectral images have high dimensionality due to number of spectral bands and pixels having continuous spectrum. These images provide us with more details than other images but still, it suffers from ‘curse of dimensionality’. Band selection is the conventional method to reduce the dimensionality and remove the redundant bands. Many methods have been developed in the past years to find the optimal set of bands. Generalized covering-based rough set is an extended method of rough sets in which indiscernibility relations of rough sets are replaced by coverings. Recently, this method is used for attribute reduction in pattern recognition and data mining. In this paper, we will discuss the implementation of covering-based rough sets for optimal band selection of hyperspectral images and compare these results with the existing methods like PCA, SVD and rough sets.

Keywords

Big Data Remote sensing Hyperspectral images Covering-based rough sets Rough sets Rough set and fuzzy C-mean (RS-FCM) Singular value decomposition (SVD) Principal component analysis (PCA) 

References

  1. 1.
    L’heureux, A., et al.: Machine learning with big data: challenges and approaches. IEEE Access 5, 7776–7797 (2017)Google Scholar
  2. 2.
    Patra, S., Bruzzone, L.: A rough set based band selection technique for the analysis of hyperspectral images. In: 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) (2015)Google Scholar
  3. 3.
    Maji, P., Paul, S.: Rough set based maximum relevance-maximum significance criterion and gene selection from microarray data. Int. J. Approx. Reason. (2010) (Elsevier)Google Scholar
  4. 4.
    Jensen, K., Shen, Q.: Semantics-preserving dimentionality reduction: rough and fuzzy rough based approches. IEEE Trans. Knowl. Data Eng. 16, 1457–1471 (2004)CrossRefGoogle Scholar
  5. 5.
    Rodarmel, C., Shan, J.: Principal component analysis for hyperspectral image classification. Surveying Land Inf. Syst. 62(2), 115–000 (2002)Google Scholar
  6. 6.
    Guo, B., et al.: Band selection for hyperspectral image classification using mutual information. IEEE Geosci. Remote Sens. Lett. 3(4), 522–526 (2006)CrossRefGoogle Scholar
  7. 7.
    Lavanya, A., Sanjeevi, S.: An improved band selection technique for hyperspectral data using factor analysis. J. Indian Soc. Remote Sens. 41(2), 199–211 (2013)CrossRefGoogle Scholar
  8. 8.
    Nahr, S., Talebi, P., et al.: Different optimal band selection of hyperspectral images using a continuous genetic algorithm. In: ISPRS—International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W3, pp. 249–253 (2014)Google Scholar
  9. 9.
    Shi, H., Shen, Y., Liu, Z.: Hyperspectral bands reduction based on rough sets and fuzzy C-means clustering. IEEE Instrumentation and Measurement and Technology Conference 2, 1053–1056 (2003)Google Scholar
  10. 10.
    Zakowski, W.: Approximations in the space (u, \(\pi \)). Demonstratio Math. 16, 761–769 (1983)Google Scholar
  11. 11.
    Kotsiantis, S., Kanellopoulos, D.: Discretization techniques: a recent survey. GESTS Int. Trans. Comput. Sci. Eng. 32(1), 47–58 (2006)Google Scholar
  12. 12.
    Wang, C., Shao, M., et al.: An improved attribute reduction scheme with covering based rough sets. Appl. Soft Comput. (2014) (Elsevier)Google Scholar
  13. 13.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology KarnatakaSurathkalIndia
  2. 2.Faculty of Computer Science and Engineering DepartmentNational Institute of Technology KarnatakaSurathkalIndia

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