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Probabilistic PCA Based Hyper Spectral Image Classification for Remote Sensing Applications
Vaddi R,
Published in Springer International Publishing
Pages: 863 - 869

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.

About the journal
JournalData powered by TypesetAdvances in Intelligent Systems and Computing Intelligent Systems Design and Applications
PublisherData powered by TypesetSpringer International Publishing
Open Access0