Curvelet-Based Synthetic Aperture Radar Image Classification


Uslu E. , Albayrak S.

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, vol.11, pp.1071-1075, 2014 (Journal Indexed in SCI) identifier identifier

  • Publication Type: Article / Article
  • Volume: 11
  • Publication Date: 2014
  • Doi Number: 10.1109/lgrs.2013.2286089
  • Journal Name: IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.1071-1075
  • Keywords: Curvelet transform (CT), generalized Gaussian distribution (GGD), histogram of curvelets (HoC), land use classification, synthetic aperture radar (SAR)
  • Yıldız Technical University Affiliated: Yes

Abstract

Curvelet transform (CT) is a multiscale directional transform that enables the use of texture and spatial locality information. In synthetic aperture radar (SAR) imaging, CT is mostly used in speckle noise reduction. This letter utilizes CT for feature extraction in land use classification. Two types of curvelet-based feature extraction methods are implemented for SAR. The first one is defined and used in content-based image retrieval and is based on generalized Gaussian distribution parameter estimation for each curvelet subband. The second implementation is a genuine method that utilizes the use of curvelet subband histograms, namely, histogram of curvelets (HoC). Using the proposed curvelet-based feature extraction method (HoC) on SAR data, better classification accuracies up to 99.56% are achieved compared to original data and H/A/alpha decomposition features. Compared to speckle-noise-reduced data classification results, it can be said that curvelet-based feature extraction is also robust against speckle noise.