Abstract
Multiaspect synthetic aperture radar (SAR) can obtain the response of the target to the radar illumination in the angle dimension. The radar cross section data referring to scattering behavior can be obtained by multiaspect SAR. The anisotropy characteristics of the man-made target in the imaging scene can reflect its scattering changing behavior. Meanwhile, the amplitude characteristics of man-made target in some observing angles is target's another scattering property. Based on man-made target's anisotropy characteristics and amplitude characteristics, a dual-channel model (DCM) based on distribution and membership for man-made target extraction is generated in this article. The model considers man-made target's data changing in different observing aspects and strong points in specific aspects. First, the preprocessing of multiaspect data using low-rank matrix decomposition is discussed. Then, the parameters of SAR amplitude images’ distribution function are calculated by using expectation maximization method. Third, one channel is fuzzy C-means (FCM) method combining spatial neighborhood information for amplitude characteristics extraction. Another channel is statistical distribution model for anisotropy characteristics extraction. Finally, The calculated membership degree (MD) and statistical probability describe the man-made target and natural target. C-band circular SAR data is used to validate our method. The result of our DCM is compared with the result of only using a single channel model. The man-made targets are extracted better using the DCM.
Highlights
M ULTIASPECT synthetic aperture radar (SAR) is better than traditional SAR working mode in obtaining target’s aspect information [1]– [3]
In this article, based on man-made target’s anisotropy characteristics and amplitude characteristics, a dual-channel model (DCM) based on distribution and membership for man-made target extraction using multiaspect SAR data is presented
The statistical model is analyzed for anisotropy scattering characteristics of the man-made target
Summary
M ULTIASPECT SAR is better than traditional SAR working mode in obtaining target’s aspect information [1]– [3]. Teng et al used aspect entropy with single polarization data to obtain multiaspect anisotropy extraction results [12]. To analyze the anisotropy characteristics and amplitude characteristics of the man-made target using multiaspect data, intuitive aspect data can be used to analyze anisotropy characteristics such as using statistical analysis. The subaperture pixel clustering method can be used for amplitude characteristics extraction. The statistical distribution characteristics of SAR images using multiaspect data information is considered. Fuzzy C-means (FCM) method combining spatial neighborhood information (FCM-SNI) is used to analyze. The extraction of man-made target based on the likelihood ratio and membership degree (MD) is proposed. The result shows that our DCM combining with two kinds of characteristics about man-made target is better compared with only considering single characteristics. The man-made target can be discriminated better from natural target by our method
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