Noisy Satellite Image Segmentation Using Statistical Features

  • Salma El Fellah
  • Salwa Lagdali
  • Mohammed Rziza
  • Mohamed El Haziti
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 37)


Satellite image segmentation is a principal task in many applications of remote sensing such as natural disaster monitoring and residential area detection and especially for Smart cities, which make demands on Satellite image analysis systems. This type of image (satellite image) is rich and various in content however it suffers from noise that affects the image in the acquisition. The most of methods retrieve the textural features from various methods but they do not produce an exact descriptor features from the image and they do not consider the effect of noise. Therefore, there is a requirement of an effective and efficient method for features extraction from the noisy image. This paper presents an approach for satellite image segmentation that automatically segments image using a supervised learning algorithm into urban and non-urban area. The entire image is divided into blocks where fixed size sub-image blocks are adopted as sub-units. We have proposed a statistical feature including local feature computed by using the probability distribution of the phase congruency computed on each block. The results are provided and demonstrate the good detection of urban area with high accuracy in absence of noise but a low accuracy when noise is added which yields as to present a novel features based on higher order spectra known by their robustness against noise.


Computer vision Segmentation Classification Satellite image Statistical feature Phase gradient Higher Order Statistics 


  1. 1.
    Blaschke, T.: Object based image analysis for remote sensing. ISPRS J. 65, 2–16 (2010)CrossRefGoogle Scholar
  2. 2.
    Sirmacek, B., Unsalan, C.: A probabilistic approach to detect urban regions from remotely sensed images based on combination of local features. In: 5th RAST 2011 Conference (2011)Google Scholar
  3. 3.
    Szummer, M., Picard, R.W.: Indoor-outdoor image classification. In: Proceedings of the IEEE ICCV Workshop, Bombay, India, pp. 42–51, January 1998Google Scholar
  4. 4.
    Pagare, R., Shinde, A.: A study on image annotation techniques. Int. J. Comput. Appl. 37(6), 42–45 (2012)Google Scholar
  5. 5.
    Mehralian, S., Palhang, M.: Principal components of gradient distribution for aerial images segmentation. In: 11th Intelligent Systems Conference (2013)Google Scholar
  6. 6.
    Ilea, D.E., Whelan, P.F.: Image segmentation based on the integration of color texture descriptors - a review. Patt. Recogn. 44, 2479–2501 (2011)CrossRefzbMATHGoogle Scholar
  7. 7.
    Fauqueur, J., Kingsbury, G., Anderson, R.: Semantic discriminant mapping for classification and browsing of remote sensing textures and objects. In: Proceedings of IEEE ICIP 2005 (2005)Google Scholar
  8. 8.
    Ma, W.Y., Manjunath, B.S.: A texture thesaurus for browsing large aerial photographs. J. Am. Soc. Inf. Sci. 49(7), 633–648 (1998)CrossRefGoogle Scholar
  9. 9.
    Tiwari, S., Shukla, V.P., Biradar, S.R., Singh, A.K.: A blind blur detection scheme using statistical features of phase congruency and gradient magnitude. Adv. Elect. Eng. 2014, 10 (2014). Article ID 521027. Lang. Syst. 15(5), 795–825 (1993).
  10. 10.
    Salma E.F., Mohammed E.H., Mohamed R., Mohamed M.: A hybrid feature extraction for satellite image segmentation using statistical global and local feature. In: Lecture Notes in Electrical Engineering (LNEE), vol. 380, pp. 247–255, April 2016.
  11. 11.
    Nikias, C.L., Mendel, J.M.: Signal processing with higher-order spectra. IEEE Sig. Process. Mag. 10(3), 10–37 (1993)CrossRefGoogle Scholar
  12. 12.
    Petropulu, A.: Higher-order spectral analysis. In: Madisetti, V.K., Williams, D.B. (eds.) Digital Signal Processing Handbook. Chapman & Hall/CRCnetBASE (1999)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Salma El Fellah
    • 1
  • Salwa Lagdali
    • 1
  • Mohammed Rziza
    • 1
  • Mohamed El Haziti
    • 1
  1. 1.LRIT, Rabat IT Center, Faculty of SciencesMohammed V University in RabatRabatMorocco

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