Application of the Modified Fractal Signature Method for Terrain Classification from Synthetic Aperture Radar Images

Abstract

In this paper the Modified Fractal Signature (MFS) method is applied to real Synthetic Aperture Radar (SAR) images provided to our research group by SET 163 Working Group on SAR radar techniques. This method uses the 'blanket' technique to provide useful information for SAR image classification. It is based on the calculation of the volume of a 'blanket', corresponding to the image to be classified, and then on the calculation of the corresponding Fractal Area curve and Fractal Dimension curve of the image. The main idea concerning this proposed technique is the fact that different terrain types encountered in SAR images yield different values of Fractal Area curves and Fractal Dimension curves, upon which classification of different types of terrain is possible. As a result, a classification technique for five different terrain types (urban, suburban, rural, mountain and sea) is presented in this paper.

Characterization of Rough Fractal Surfaces from Backscattered Radar Data

“…In this paper we concentrate on the backscattering of EM waves from rough fractal surfaces (e.g. monostatic SAR radar [9], [10]), i.e. θs = -θi at Fig.…”

“…and M is the number of frequencies, fo is the carrier frequency, f = B/M is the frequency step and B is the bandwidth of the radar, i.e. 'stepped -frequency' transmitted radar waveform [9], [10]. Once again, in all our simulations it is set θs = -θi, i.e.…”

“…θs= -θi, from (8) it follows that in this case z  ≈ 0, and from (10) we see that our method is not applicable in this special case (i.e. radar not airborne or spaceborne, any more [9], [10]), since in this special case the scattering coefficient |γ(k)| cannot be computed. Summarizing, our proposed method for characterizing a rough fractal surface provides reliable results for appropriate values of radar bandwidth, surface 'patch size' and angle of incidence.…”

Characterization of 2D and 3D Rough Fractal Surfaces from Backscattered Radar Data

“…In this paper, a novel method is presented for the characterization of rough fractal surfaces from backscattered radar data of sufficient bandwidth [9], [10]. As resulted from the plots of the backscattered signal magnitude as a function of the wavenumber (therefore a 'spectral method') of the incident EM wave, as the roughness of the fractal surface increases, then the observed slope between the main lobe and the side lobes also increases.…”

Amber Gemstones Sorting By Colour

“…Such systems are based on optical properties of object's surfaces, by using different types of sensors for example CCD cameras, spectroscopy, stereo vision, infrared light, among others. Optical sensors are able to acquire colour [5], shape, texture, and other optical features [6] and in many cases it is a multiclass [7] identification problem. Optical properties relay on lighting condition, which makes isolating objects from environment and implementing artificial lighting source some of the most important key points of the system to work properly.…”