A Novel Wavelet Filtering Method in SAR Image Classification by Neural Networks

  • Giovanni Simone
  • Francesco Carlo Morabito
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


This paper presents a method for classification of Synthetic Aperture Radar (SAR) images [1], based on the joint exploitation of a novel Wavelet Filtering Algorithm [2] and Neural Networks [3]. An illustrative example is presented that shows how the use of the proposed technique of Wavelet Filtering allows both to mitigate negative effects of multiplicative noise (speckle) and to classify the considered image without resolution decrease. The proposed Wavelet Filtering, applied on the image, provides four images (an approximation image and three detail images); detail images have the same resolution as original image. The use of Wavelet Filtering (WF) on the detail images jointly to a Multilayer Feedforward Network allows us to reduce some of typical drawbacks of the SAR classification problems, and thus to have a more efficient tool with respect to the traditional approach. The proposed method is tested on a real SAR image, and it is compared with traditional approaches based on MLP use, and on FFT-2Dfiltering-MLP technique.


Input Image Synthetic Aperture Radar High Pass Synthetic Aperture Radar Image Detail Image 
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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Giovanni Simone
    • 1
  • Francesco Carlo Morabito
    • 1
  1. 1.Università di Reggio Calabria - Facoltà di Ingegneria — DIMETReggio CalabriaItaly

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