Conference Paper

Speckle Noise Removal of SAR Images Based on 2-Dimensional S-Transform

DOI: 10.1109/IGARSS.2006.804 Conference: Geoscience and Remote Sensing Symposium, 2006. IGARSS 2006. IEEE International Conference on
Source: IEEE Xplore


With its ability to image the earth's surface in nearly all weather conditions, together with its high spatial resolution, Synthetic Aperture Radar (SAR) has shown its potential for classifying and monitoring geographysical parameters both locally and globally. However, SAR images are usually corrupted by the speckle noise. It badly disturbs the extraction and interpretation of the information of the objects, influences the application of the SAR images. So it has important meaning to reduce the speckle noise in the SAR images. In this paper, 2-dimensional S-transform is proposed to remove the speckle noise of SAR images. Firstly, the 2-dimensional S transform algorithm is analyzed and applied to removal speckle noise of RADARSAT images. Then, the flatness index (FI), the edge remaining index (ERI) and primary statistical parameters are used to compare the results among median filtering, average filtering, LEE filtering, FROST filtering, KUAN filtering, GAMMA filtering and 2-dimensional S-transform. The experiment results show that the speckle noise is most effectively restrained using the two dimensional S-transform.

0 Reads
  • [Show abstract] [Hide abstract]
    ABSTRACT: The presence of speckle in radar images makes the radiometric and textural aspects less efficient for class discrimination. Many adaptive filters have been developed for speckle reduction, the most well known of which are analyzed. It is shown that they are based on a test related to the local coefficient of variation of the observed image, which describes the scene heterogeneity. Some practical criteria are introduced to modify the filters in order to make them more efficient. The filters are tested on a simulated synthetic aperture radar (SAR) image and an SAR-580 image. As was expected, the new filters perform better, i.e. they average the homogeneous areas better and preserve texture information, edges, linear features, and point target responses better at the same time. Moreover, they can be adapted to features other than the coefficient of variation to reduce the speckle while preserving the corresponding information
    IEEE Transactions on Geoscience and Remote Sensing 12/1990; 28(6-28):992 - 1000. DOI:10.1109/36.62623 · 3.51 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The S transform, which is introduced in the present correspondence, is an extension of the ideas of the continuous wavelet transform (CWT) and is based on a moving and scalable localizing Gaussian window. It is shown to have some desirable characteristics that are absent in the continuous wavelet transform. The S transform is unique in that it provides frequency-dependent resolution while maintaining a direct relationship with the Fourier spectrum. These advantages of the S transform are due to the fact that the modulating sinusoids are fixed with respect to the time axis, whereas the localizing scalable Gaussian window dilates and translates
    IEEE Transactions on Signal Processing 05/1996; 44(4-44):998 - 1001. DOI:10.1109/78.492555 · 2.79 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Computational techniques involving contrast enhancement and noise filtering on two-dimensional image arrays are developed based on their local mean and variance. These algorithms are nonrecursive and do not require the use of any kind of transform. They share the same characteristics in that each pixel is processed independently. Consequently, this approach has an obvious advantage when used in real-time digital image processing applications and where a parallel processor can be used. For both the additive and multiplicative cases, the a priori mean and variance of each pixel is derived from its local mean and variance. Then, the minimum mean-square error estimator in its simplest form is applied to obtain the noise filtering algorithms. For multiplicative noise a statistical optimal linear approximation is made. Experimental results show that such an assumption yields a very effective filtering algorithm. Examples on images containing 256 × 256 pixels are given. Results show that in most cases the techniques developed in this paper are readily adaptable to real-time image processing.
    IEEE Transactions on Pattern Analysis and Machine Intelligence 04/1980; PAMI-2(2-PAMI-2):165 - 168. DOI:10.1109/TPAMI.1980.4766994 · 5.78 Impact Factor