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Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques

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Abstract

In the era of climate change, monitoring and effective retrieval of soil, water bodies, vegetation parameters etc. are of utmost importance which is successfully being executed using remote sensing from last few decades. The advancement of technologies has enabled us to reach effective decision making through these sensors. The advantage of acquiring multitemporal spatially continuous data sometimes turns into a disadvantage due to class imbalance where minority class instances are often misclassified by most of the classifiers. The current work explored the solution to handle this problem by resampling the datasets before the application of classification algorithms by proposing a new computationally efficient class wise resampling technique which is based on SMOTE and centroid-based clustering. The experiment was conducted on two benchmarked publicly available hyperspectral datasets. The output of the current work shows the superiority of the current work over past studies based on the performance evaluation metrics, accuracy, precision, recall and kappa values.

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Acknowledgement

First author would like to thank and acknowledge the University Grants Commission (UGC), New Delhi for providing fellowship to pursue his research through UGC-Junior Research Fellowship Scheme. Further, the authors would like to thank the Department of Science and Technology (DST), New Delhi for the technical support provided through DST-PURSE scheme.

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Authors and Affiliations

  1. Department of Computer Science, Institute of Science, Banaras Hindu University, Varanasi, India

    Pangambam Sendash Singh, Vijendra Pratap Singh, Manish Kumar Pandey & Subbiah Karthikeyan

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  1. Pangambam Sendash Singh
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  2. Vijendra Pratap Singh
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  4. Subbiah Karthikeyan
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Correspondence to Vijendra Pratap Singh.

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Singh, P.S., Singh, V.P., Pandey, M.K. et al. Enhanced classification of hyperspectral images using improvised oversampling and undersampling techniques. Int. j. inf. tecnol. 14, 389–396 (2022). https://doi.org/10.1007/s41870-021-00676-0

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