Novel Segmentation Method for Fractal Geometry Based Satellite Images Classification

  • Dr. Mohammed Sahib Altaei Dept. of Computers, College of Science, Alnahrain University, Baghdad, Iraq
  • Aqeel A. Abdul Hassan Dept. of Civil, College of Engineering, Wasit University, Wasit, Iraq
Keywords: Box Counting, Classification, Fractal Features, Satellite Image.

Abstract

The use of efficient image classification methods gains most interest due to its close relation with the improvements happen in the fields of compression and communications. Fractal geometry is receiving increased attention as a quantitative and qualitative model for natural phenomena description, which can establish an active classification technique when applied on satellite image. In this paper, the used satellite image is taken by Landsat for Al-Kut city in Iraq. Different parts of this image that contains different visible classes are chosen manually to be a training area. The training areas are passing two stages: segmentation and classification. To credit effective segmentation, the training areas are segmented by a hybrid technique consists of two sequenced methods: Diagonal (Dg) method that operated inside the quadtree (Q) method. The hybrid method segments each squared image block into either four quadrants or two triangular blocks according to uniformity criterion. Then, unsupervised classification is applied depending on the fractal feature. The fractional Brownian motion (FBM) is the fractal feature that employed for classification. The classification is implemented for each image segment; squared or triangular. The results of FBM are grouped into five deferent clusters, each represents distinct class of image. The center of each group and its dispersion distance are stored in a database table to be used in the classification of whole image. The classification results gave 95% classification score, which ensures the ability of FBM to recognize different satellite image regions when used as fractal feature.

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Published
2017-02-25
Section
Articles