From the literature review, there are two constant false alarm rate detectors for detecting edges in multi-look fully polarimetric synthetic aperture radar (POLSAR) imagery, namely the likelihood ratio edge detector [1] and the Roy's largest eigenvalue-based edge detector [2]. In the latter approach, one major restriction is the computation complexity, i.e. in the context of the chosen C language-based implementation. Thus, in this paper, a novel hardware-based architecture is presented to improve the processing time for the Roy's largest eigenvalue-based edge detection. The algorithm was implemented in a field-programmable gate array (FPGA) with an accelerated solution targeting data rates of up to 1 Gb/s. Its performance was examined using ninelook NASA/JPL C-band data and evaluated in terms of processing speed and accuracy as compared to the C language-based implementation on a personal computer (PC) with a Core TM 2 Duo processor clocked at 2.2 GHz.
This paper studies the applications of squared radius and trace statistic under homogeneous and texture models for modeling terrain radar clutter in polarimetric synthetic aperture radar (SAR) data. The squared radius and trace statistic involve the use of scattering vector and polarimetric covariance matrix, respectively, in their computation. The terrain radar clutter modeling was performed on TerraSAR-X single-look high-resolution spotlight data and multi-look NASA/JPL AIRSAR POLSAR data. Both the homogeneous and texture models were investigated and evaluated quantitatively through chi-squared goodness-of-fit test. The obtained results showed that the texture model provided a better fitting compared with the homogeneous model.
instead of the full polarimetric (FP) vector in Eqn. (2): tFP = [SHH V2SHV SVV]T (2)The notation S H H denotes the scattering element for horizontal-horizontal polarization. The corresponding covariance matrices for both CP and FP modes are J and C respectively:Comparing Eqns. (3) and (4), it is clear that the measurable data in CP reduces from nine to four unique data elements per pixel, leading to the potential for significant savings in storage requirements and transmission bandwidth. However, where only CP data is available but FP data is required, an FP matrix needs to be reconstructed from the reduced CP information set. From only four known measurable data elements in CP, nine unknown data elements in the FP matrix need to be obtained in some way.Fortunately, this under-determined estimation process can be constrained in some cases by applying a symmetry assumption, where the following condition holds:This assumption has been shown to be applicable for natural surfaces [4], and allows matrix J in Eqn. (3) to be simplified as follows: J -[ ISHHI 2 + ISHVI 2 SHHSVV + ISHVI 2 ] -sHHsVV + ISHVI 2 ISHVI 2 + IsvvI 2 Furthermore, the matrix C in Eqn. (4) now becomes: C __ [ISHHI 2 0 SHHOSVV ] o 21sHVI 2 svvsHH 0 IsvvI 2
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