Satellite Image Segmentation Using Wavelet Transforms Based on Color and Texture Features

  • Ricardo Dutra da Silva
  • Rodrigo Minetto
  • William Robson Schwartz
  • Helio Pedrini
Conference paper

DOI: 10.1007/978-3-540-89646-3_12

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5359)
Cite this paper as:
da Silva R.D., Minetto R., Schwartz W.R., Pedrini H. (2008) Satellite Image Segmentation Using Wavelet Transforms Based on Color and Texture Features. In: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2008. Lecture Notes in Computer Science, vol 5359. Springer, Berlin, Heidelberg

Abstract

Image segmentation is a fundamental process in remote sensing applications, whose main purpose is to allow a meaningful discrimination among constituent regions of interest. This work presents a novel image segmentation method based on wavelet transforms for extracting a number of color and texture features from the images. Traditional feature extraction techniques based on individual pixels usually demand high computational cost. To reduce such computational cost, while achieving high-quality results, our approach is composed of two main stages. Initially, the image is decomposed into blocks of pixels and a wavelet transform is applied to each block to identify homogeneous regions of the image, assigning the entire block to a class. A refinement stage is applied to the remaining pixels which belong to blocks marked as heterogenous in the first stage. The developed method, tested on several remote sensing images and compared to a well known image segmentation method, presents high adaptability to image regions.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Ricardo Dutra da Silva
    • 1
  • Rodrigo Minetto
    • 2
  • William Robson Schwartz
    • 3
  • Helio Pedrini
    • 2
  1. 1.Department of Computer ScienceFederal University of ParanáCuritibaBrazil
  2. 2.Institute of ComputingUniversity of CampinasCampinasBrazil
  3. 3.Department of Computer ScienceUniversity of MarylandUSA

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