Abstract
In conventional synthetic aperture radar (SAR) working modes, targets are assumed isotropic because the viewing angle is small. However, most man-made targets are anisotropic. Therefore, anisotropy should be considered when the viewing angle is large. From another perspective, anisotropy is also a useful feature. Circular SAR (CSAR) can detect the scattering variation under different azimuthal look angles by a 360-degree observation. Different targets usually have varying degrees of anisotropy, which aids in target discrimination. However, there is no effective method to quantify the degree of anisotropy. In this paper, aspect entropy is presented as a descriptor of the scattering anisotropy. The range of aspect entropy is from 0 to 1, which corresponds to anisotropic to isotropic. First, the method proposed extracts aspect entropy at the pixel level. Since the aspect entropy of pixels can discriminate isotropic and anisotropic scattering, the method prescreens the target from the isotropic clutters. Next, the method extracts aspect entropy at the target level. The aspect entropy of targets can discriminate between different types of targets. Then, the effect of noise on aspect entropy extraction is analyzed and a denoising method is proposed. The Gotcha public release dataset, an X-band circular SAR data, is used to validate the method and the discrimination capability of aspect entropy.
Highlights
Synthetic aperture radar (SAR) is a high-resolution imaging radar that works all-weather and all-day [1]
It can be found that the denoising method significantly improved the accuracy of aspect entropy in low signal-to-noise ratio (SNR)
The results show that our proposed aspect entropy extraction method can obtain the Radar cross section (RCS) curve of the target and extract the aspect entropy of the target
Summary
Synthetic aperture radar (SAR) is a high-resolution imaging radar that works all-weather and all-day [1]. Li et al propose an anisotropic scattering detection method to characterize targets [14] These methods all require the use of full-polarization data. Stojanovic et al use the sub-aperture method to extract the curve of the radar cross section (RCS) amplitude of pixels versus aspect angles using single-polarization CSAR data [15]. We propose the extraction method of aspect entropy using real CSAR data. Using the result of pixel-wise aspect entropy extraction, anisotropic pixels that belong to targets can be discriminated from isotropic clutters by thresholding. The result shows that the aspect entropy of pixels and targets can be extracted from CSAR data. The proposed RCS curve denoising method can remove the noise from the RCS curve extracted from the real data It makes the result of aspect entropy extraction more accurate. Since the aspect entropy of different types of targets falls into different ranges, targets can be discriminated from each other by the aspect entropy value
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