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Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications

  • Radhesyam Vaddi
  • Prabukumar ManoharanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 941)

Abstract

Hyper spectral image (HSI) Classification has become important research areas of remote sensing which can be used in many practical applications, including precision agriculture, Land cover mapping, environmental monitoring etc. HSI Classification includes various steps like Noise removal, dimensionality reduction, and classification. In this work, we adopted structure-preserving recursive filter (SPRF) to noise removal and Probabilistic based principal component analysis (PPCA) is applied to reduce dimensionality. Finally classification is performed using multi class large marginal distribution machine (LDM). The proposed (HSI) Classification method is carried out and results are validated across the three widely used standard datasets like Indian Pines, University of Pavia and Salinas. The obtained results show that the proposed method provides results on par with similar type of methods from literature.

Keywords

Hyper spectral image Principal component analysis Agriculture 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Information Technology Engineering (SITE)VIT UniversityVelloreIndia
  2. 2.Department of Information TechnologyV. R. Siddhartha Engineering CollegeVijayawadaIndia

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