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Classification of complex environments using pixel level fusion of satellite data

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Abstract

The present study reports classification and analysis of composite land features using fusion images obtained by fusing two original hyperspectral and multispectral datasets. The high spatial-spectral resolution, multi-instrument and multi-period satellite images were used for fusion. Three pixel level fusion based techniques, Color Normalized Spectral Sharpening (CNSS), Principal Component Spectral Sharpening Transform (PCSST) and Gram-Schmidt Transform (GST), were implemented on the datasets. Performance evaluations of three fusion algorithms were done using classification results. The Support Vector Machine (SVM) and Gaussian Maximum Likelihood Classification (MLC) were used for classification using five types of images, viz. hyperspectral, multispectral and three fused images. Number of classes considered was eight. Sufficient number of ground field data for each class has also been acquired which was needed for supervise based classification. The accuracy was improved from 74.44 to 97.65% when the fused images were considered with SVM classifier. Similarly, the results were improved from 69.25 to 94.61% with original and fused data using MLC classifier. The fusion image technique was found to be superior to the single original image and the SVM is better than the MLC method.

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Acknowledgements

The authors would like to thanks to the United States Geological Survey (USGS) for providing EO-1 Hyperion Data for this study. The authors would also like to thanks to UGC for providing BSR fellowship and lab facilities under UGC SAP (II) DRS Phase-I F.No.-3-42/2009, Phase-II 4-15/2015/DRS-II for this study. The authors would like to thank the Editor-in-Chief, the Associate Editor, managing and handling editors of MTAP and anonymous reviewers for their valuable suggestions and comments, which improved the quality of this manuscript.

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Authors and Affiliations

  1. School of Computational Sciences, Punyashlok Ahilyadevi Holkar Solapur University, Solapur, MS, 413255, India

    Amol D. Vibhute & Rajesh K. Dhumal

  2. Department of Computer Science and IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad, MS, 431004, India

    Karbhari V. Kale, Sandeep V. Gaikwad, Amarsinh B. Varpe, Dhananjay B. Nalawade & Suresh C. Mehrotra

  3. Dr. G.Y. Pathrikar College of Computer Science and Information Technology, M G M, Aurangabad, MS, India

    Ajay D. Nagne

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  1. Amol D. Vibhute
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Correspondence to Amol D. Vibhute.

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Vibhute, A.D., Kale, K.V., Gaikwad, S.V. et al. Classification of complex environments using pixel level fusion of satellite data. Multimed Tools Appl 79, 34737–34769 (2020). https://doi.org/10.1007/s11042-020-08978-4

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  • DOI: https://doi.org/10.1007/s11042-020-08978-4

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