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An Efficient Approach for Enhancing Contrast Level and Segmenting Satellite Images: HNN and FCM Approach

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Abstract

Satellite image segmentation has gotten bunches of consideration of late because of the accessibility of commented on high-goals image informational indexes caught by the last age of satellites. The issue of fragmenting a satellite image can be characterized as ordering (or marking) every pixel of the image as indicated by various classes, for example, structures, streets, water, etc. In this paper centered to build up a satellite image segmenting process by utilizing distinctive optimization methods. The work is prepared dependent on three stages that are RGB change, preprocessing, and division. At first the database images are assembled from the database at that point select the blue band images by performing RGB change. To improve the differentiation and furthermore decreasing the commotion of these chose blue band images, Hopfield neural network (HNN) is utilized. After image upgrade, the images are fragmented dependent on fuzzy C means (FCM) clustering method. The images are clustered and segmented in the way of optimizing the centroid in FCM utilizing oppositional crow search algorithm. The exhibition of the proposed framework is investigated dependent on the presentation measurements, for example, affectability, particularity and accuracy. From the outcomes, the proposed strategy diminished the computational time by expanding the accuracy of 98.3% with HNN system.

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References

  1. Kalist, V., Ganesan, P., Sathish, B. S., Jenitha, J. M. M., & Bashashaik, K. (2015). Possiblistic-fuzzy C-means clustering approach for the segmentation of satellite images in HSL color space. Procedia Computer Science, 57, 49–56.

    Article  Google Scholar 

  2. Banerjee, B., & Buddhiraju, K. M. (2012). Satellite image segmentation: A novel adaptive mean-shift clustering based approach. In 2012 IEEE International geoscience and remote sensing symposium.

  3. Ganesan, P., & Rajini, V. (2014). YIQ color space based satellite image segmentation using modified FCM clustering and histogram equalization. In 2014 International conference on advances in electrical engineering (ICAEE).

  4. Deepika, N. P., & Vishnu, K. (2015). Different techniques for satellite image segmentation. In 2015 Online international conference on green engineering and technologies (IC-GET).

  5. Ganesan, P., & Sajiv, G. (2017). User oriented color space for satellite image segmentation using fuzzy based techniques. In 2017 International conference on innovations in information, embedded and communication systems (ICIIECS).

  6. Ben Arab, T., Zribi, M., & Masmoudi, A. (2013). Unsupervised satellite image segmentation using a Bivariate Beta type-II mixture model. In 2013 IEEE 9th international conference on computational cybernetics (ICCC).

  7. Jia, H., Sun, K., Song, W., Peng, X., Lang, C., & Li, Y. (2019). Multi-strategy emperor penguin optimizer for RGB histogram-based color satellite image segmentation using masi entropy. IEEE Access,7, 134448–134474.

    Article  Google Scholar 

  8. Zhou, Y., Li, J., Feng, L., Zhang, X., & Hu, X. (2017). Adaptive scale selection for multiscale segmentation of satellite images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,10(8), 3641–3651.

    Article  Google Scholar 

  9. Ghassemi, S., Fiandrotti, A., Francini, G., & Magli, E. (2019). Learning and adapting robust features for satellite image segmentation on heterogeneous data sets. IEEE Transactions on Geoscience and Remote Sensing, 57(9), 6517–6529.

    Article  Google Scholar 

  10. Henry, C., Azimi, S. M., & Merkle, N. (2018). Road segmentation in sar satellite images with deep fully convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 15(12), 1867–1871.

    Article  Google Scholar 

  11. Nalepa, J., Myller, M., & Kawulok, M. (2019). Validating hyperspectral image segmentation. IEEE Geoscience and Remote Sensing Letters, 16(8), 1264–1268.

    Article  Google Scholar 

  12. Sedov, A. G., Khryashchev, V. V., Larionov, R. V., & Ostrovskaya, A. A. (2019). Loss function selection in a problem of satellite image segmentation using convolutional neural network. In 2019 Systems of signal synchronization, generating and processing in telecommunications (SYNCHROINFO).

  13. Nivaggioli, A., & Randrianarivo, H. (2019). Weakly supervised semantic segmentation of satellite images. In 2019 Joint urban remote sensing event (JURSE).

  14. Deepika, N. P., Subha, M. S. L., & Gopal, V. (2015). Pattern extraction in segmented satellite images by reducing semantic gap using relevance feedback mechanism. Procedia Computer Science,46, 1809–1816.

    Article  Google Scholar 

  15. Cao, L., Wang, C., & Li, J. (2016). Vehicle detection from highway satellite images via transfer learning. Information Sciences,366, 177–187.

    Article  MathSciNet  Google Scholar 

  16. Wang, S., Yang, B., Zhou, Y., Wang, F., Zhang, R., & Zhao, Q. (2018). Three-dimensional information extraction from GaoFen-1 satellite images for landslide monitoring. Geomorphology,309, 77–85.

    Article  Google Scholar 

  17. Ada, N., Harsono, T., & Basuki, A. (2018). Cloud satellite image segmentation using MengHeeHeng K-means and DBSCAN clustering. In 2018 International electronics symposium on knowledge creation and intelligent computing (IES-KCIC).

  18. Bhandari, A. K., Singh, V. K., Kumar, A., & Singh, G. K. (2014). Cuckoo search algorithm and wind driven optimization based study of satellite image segmentation for multilevel thresholding using Kapur’s entropy. Expert Systems with Applications,41(7), 3538–3560.

    Article  Google Scholar 

  19. Bhandari, A. K., Kumar, A., & Singh, G. K. (2015). Modified artificial bee colony based computationally efficient multilevel thresholdingfor satellite image segmentation using Kapur’s, Otsu and Tsallis functions. Expert Systems with Applications,42(3), 1573–1601.

    Article  Google Scholar 

  20. Bhandari, A. K., Kumar, A., & Singh, G. K. (2015). Tsallis entropy based multilevel thresholding for colored satellite image segmentation using evolutionary algorithms. Expert Systems with Applications,42(22), 8707–8730.

    Article  Google Scholar 

  21. Sammouda, R., Adgaba, N., Touir, A., & Al-Ghamdi, A. (2014). Agriculture satellite image segmentation using a modified artificial Hopfield neural network. Computers in Human Behavior,30, 436–441.

    Article  Google Scholar 

  22. Pare, S., Bhandari, A. K., Kumar, A., Singh, G. K., & Khare, S. (2015). Satellite image segmentation based on different objective functions using genetic algorithm: A comparative study. In 2015 IEEE International conference on digital signal processing (DSP).

  23. Ganesan, P., & Rajini, V. (2014). Assessment of satellite image segmentation in RGB and HSV color space using image quality measures. In 2014 International conference on advances in electrical engineering (ICAEE).

  24. Muangkote, N., Sunat, K., & Chiewchanwattana, S. (2016). Multilevel thresholding for satellite image segmentation with moth-flame based optimization. In 2016 13th International joint conference on computer science and software engineering (JCSSE).

  25. Bhandari, A. K., Soni, V., Kumar, A., & Singh, G. K. (2014). Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT–SVD. ISA Transactions,53(4), 1286–1296.

    Article  Google Scholar 

  26. Parastesh, F., Jafari, S., Azarnoush, H., Hatef, B., Namazi, H., & Dudkowski, D. (2019). Chimera in a network of memristor-based Hopfield neural network. The European Physical Journal Special Topics,228(10), 2023–2033.

    Article  Google Scholar 

  27. Deng, Z. H., Qiao, H. H., Song, Q., & Gao, L. (2019). A complex network community detection algorithm based on label propagation and fuzzy C-means. Physica A: Statistical Mechanics and its Applications,519, 217–226.

    Article  Google Scholar 

  28. Yamini, M. P. C. (2019). A violent crime analysis using fuzzy c-means clustering approach. ICTACT Journal on Soft Computing,9(3), 1939–1944.

    Google Scholar 

  29. Zhang, H., Ma, J., Jing, J., & Li, P. (2019). Fabric defect detection using L0 gradient minimization and fuzzy C-means. Applied Sciences,9(17), 3506.

    Article  Google Scholar 

  30. Sayed, G. I., Hassanien, A. E., & Azar, A. T. (2019). Feature selection via a novel chaotic crow search algorithm. Neural Computing and Applications,31(1), 171–188.

    Article  Google Scholar 

  31. Zamani, H., Nadimi-Shahraki, M. H., & Gandomi, A. H. (2019). CCSA: Conscious neighborhood-based crow search algorithm for solving global optimization problems. Applied Soft Computing,85, 105583.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Science, Utkal University, Bhubaneswar, India

    Ramesh Chandra Sahoo & Sateesh Kumar Pradhan

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  1. Ramesh Chandra Sahoo
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  2. Sateesh Kumar Pradhan
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Correspondence to Ramesh Chandra Sahoo.

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Sahoo, R.C., Pradhan, S.K. An Efficient Approach for Enhancing Contrast Level and Segmenting Satellite Images: HNN and FCM Approach. Wireless Pers Commun 113, 651–667 (2020). https://doi.org/10.1007/s11277-020-07247-9

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