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, Volume 104, Issue 1, pp 357–372 | Cite as

Advanced Satellite Image Classification of Various Resolution Image Using a Novel Approach of Deep Neural Network Classifier

  • S. Jayanthi
  • C. Vennila


Image registration is computationally intensive and applied in a variety of applications, for example, multispectral classification, change recognition, climate prediction and multi-view analysis in GIS and medicine. There are three types of registration namely multi-view, multimodal and multi-temporal. In multi-view based registration, the images of the same scene taken at different viewpoints are analyzed and modeled for the requirement. Hence stereoscopic image sequences of the same view are acquired, and accurate comparison for the image classification is essential. This paper presents a robust method which has three steps. The first phase includes obtaining hyper spectral (satellite) images and preprocessing of them, the second period subdivides into image blocks for alignment, and the final step focuses on classification based on hyper graph structure using deep learning approach. For processing of satellite images, a new method linear iterative clustering and deep neural network classification are employed. Previous works in remote sensing applications involve training samples and hence prior knowledge of image sets which incurs more computational time. The implementation of this method shows an automatic, achieving better accuracy and dynamic reconfigurable image registration in reduced complexity. The mathematical model used is hidden markov chain model for clustering which provides region-wise feature construction for evaluating region shape and contextual information. The work yields classification accuracy of 94.12% which is far better than past outcomes in this engaged field of research. The execution of the usage is examined, a comparison is additionally influenced regarding false classification ratio, time complexity and clustering accuracy is demonstrated.


Satellite image Deep neural network Multiview image registration 



  1. 1.
    Camps-Valls, G., Tuia, D., Bruzzone, L., & Benediktsson, J. A. (2014). Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Processing Magazine, 31(1), 45–54.CrossRefGoogle Scholar
  2. 2.
    Cui, J., et al. (2015). Temperature and emissivity separation and mineral mapping based on airborne TASI hyperspectral thermal infrared data. International Journal of Applied Earth Observation and Geoinformation, 40, 19–28.CrossRefGoogle Scholar
  3. 3.
    Cierniewski, J., Kaźmierowski, C., Królewicz, S., Piekarczyk, J., Wróbel, M., & Zagajewski, B. (2014). Effects of different illumination and observation techniques of cultivated soils on their hyperspectral bidirectional measurements under field and laboratory conditions. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2525–2530.CrossRefGoogle Scholar
  4. 4.
    Luft, L., Neumann, C., Freude, M., Blaum, N., & Jeltsch, F. (2014). Hyperspectral modeling of ecological indicators—a new approach for monitoring former military training areas. Ecological Indicators, 46, 264–285.CrossRefGoogle Scholar
  5. 5.
    Borengasser, M., Hungate, W. S., & Watkins, R. (2008). Hyperspectral remote sensing: Principles and applications. Boca Raton, FL: CRC Press.Google Scholar
  6. 6.
    Wang, Q., Lin, J., & Yuan, Y. (2016). Salient band selection for hyperspectral image classification via manifold ranking. IEEE Transactions on Neural Networks and Learning Systems, 27(6), 1279–1289.CrossRefGoogle Scholar
  7. 7.
    Peng, X., Tang, H., Zhang, L., Yi, Z., & Xiao, S. (2016). A unified framework for representation-based subspace clustering of out-of-sample and largescale data. IEEE Transactions on Neural Networks and Learning Systems, 27(12), 2499–2512.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Peng, X., Lu, J., Yi, Z., & Yan, R. (2017). Automatic subspace learning via principal coefficients embedding. IEEE Transactions on Cybernetics, 47(11), 3583–3596.CrossRefGoogle Scholar
  9. 9.
    Peng, X., Yu, Z., Yi, Z., & Tang, H. (2017). Constructing the L2-graph for robust subspace learning and subspace clustering. IEEE Transactions on Cybernetics, 47(4), 1053–1066.CrossRefGoogle Scholar
  10. 10.
    Gao, S., Tsang, I. W.-H., & Chia, L.-T. (2010). Kernel sparse representation for image classification and face recognition. In Proc. ECCV (pp. 1–14).Google Scholar
  11. 11.
    Zhang, L., Yang, M., Feng, X., Ma, Y., & Zhang, D. (2012) Collaborative representation based classification for face recognition. Unpublished paper. [Online]. Available:
  12. 12.
    Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2011). Hyperspectral image classification using dictionary-based sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3973–3985.CrossRefGoogle Scholar
  13. 13.
    Chen, Y., Nasrabadi, N. M., & Tran, T. D. (2013). Hyperspectral image classification via kernel sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 51(1), 217–231.CrossRefGoogle Scholar
  14. 14.
    Liu, J., Wu, Z., Wei, Z., Xiao, L., & Sun, L. (2013). Spatial-spectral kernel sparse representation for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(6), 2462–2471.CrossRefGoogle Scholar
  15. 15.
    Li, W., & Du, Q. (2014). Joint within-class collaborative representation for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2200–2208.CrossRefGoogle Scholar
  16. 16.
    Yuan, Y., Lin, J., & Wang, Q. (2016). Hyperspectral image classification via multitasking joint sparse representation and stepwise MRF optimization. IEEE Transactions on Cybernetics, 46(12), 2966–2977.CrossRefGoogle Scholar
  17. 17.
    Wang, Q., Wan, J., & Yuan, Y. (2018). Locality constraint distance metric learning for traffic congestion detection. Pattern Recognition, 75, 272–281.CrossRefGoogle Scholar
  18. 18.
    Wang, Q, Chen, M., & Li, X. (2017). Quantifying and detecting collective motion by manifold learning. In Proc. 31st AAAI conf. artif. intell. (pp. 4292–4298).Google Scholar
  19. 19.
    Lu, C.-Y., Min, H., Gui, J., Zhu, L., & Lei, Y.-K. (2013). Face recognition via weighted sparse representation. Journal of Visual Communication and Image Representation, 24(2), 111–116.CrossRefGoogle Scholar
  20. 20.
    Li, W., Tramel, E. W., Prasad, S., & Fowler, J. E. (2014). Nearest regularized subspace for hyperspectral classification. IEEE Transactions on Geoscience and Remote Sensing, 52(1), 477–489.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • S. Jayanthi
    • 1
  • C. Vennila
    • 2
  1. 1.Department of Computer Science and EngineeringUniversity College of EngineeringAriyalurIndia
  2. 2.Department of Electronics and Communication EngineeringSaranathan College of EngineeringTrichyIndia

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