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The Enriched Feature Enhancement Technique for Satellite Image Based on Transforms Using PCNN

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Abstract

The features of the satellite images can be improved by fusing or combining two images with complementary property. By fusing these two images the spatial property of the resultant image is improved. Satellite images are one of the agents that give the features of the earth’s surface. Processing these satellite images will provide more geographical information hidden in the images. This research paper have an detailed insight study of two types of the satellite images one is Panchromatic (PAN) and other Multispectral (MS). The PAN image with high spatial resolution and MS image with spectral resolution are fused to get better resultant output. For fusion process Nonsubsampled Contour let Transform is used to decompose the images into low and high frequency values. Pulse Coupled Neural Network is used to motivate the low frequency pixel and Morphological filter is applied to the edge detected image for finding the features in the images. This is an real time transformations which will give better results in SAR image processing, video processing, stereo based reconstruction of depth and width of the features present in the image.

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Acknowledgements

Author would like to thank Mr. Manickavasagam, Director, DRDL, Hydrabad and Mr Peter Pethuru Raj, CEO, SRE, Reliance Jio Cloud, Banglore, for their continuous support throughout the work.

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Authors and Affiliations

  1. School of Computing, SASTRA Deemed University, Thanjavur, India

    K. Hariharan

  2. School of Electrical and Electronics Communication Engineering, SASTRA Deemed University, Thanjavur, India

    N. R. Rajaan & Malini Deepika

  3. (SRE) Division, Reliance Jio Cloud, Banglore, India

    Peter Pethuru Raj Chelliah

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  1. K. Hariharan
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  2. N. R. Rajaan
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  3. Peter Pethuru Raj Chelliah
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  4. Malini Deepika
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Correspondence to K. Hariharan.

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Hariharan, K., Rajaan, N.R., Chelliah, P.P.R. et al. The Enriched Feature Enhancement Technique for Satellite Image Based on Transforms Using PCNN. Wireless Pers Commun 117, 2729–2744 (2021). https://doi.org/10.1007/s11277-020-07044-4

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  • DOI: https://doi.org/10.1007/s11277-020-07044-4

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