Dielectric constant is an important role to describe the properties of matter. This paper proposes This paper proposes the concept of mixed dielectric constant(MDC) in passive microwave radiometric measurement. In addition, a MDC inversion method is come up, Ratio of Angle-Polarization Difference(RAPD) is utilized in this method. The MDC of several materials are investigated using RAPD. Brightness temperatures(TBs) which calculated by MDC and original dielectric constant are compared. Random errors are added to the simulation to test the robustness of the algorithm. Keywords: Passive detection, microwave/millimeter, radiometric measurement, ratio of angle-polarization difference (RAPD), mixed dielectric constant (MDC), brightness temperatures, remote sensing, target recognition.
Synthetic aperture interferometric radiometers (SAIR) has been introduced to threat detection for high spatial resolution and no harm to human body. Usually, the SAIR security instrument is about 3 meters away from human body, which means that the SAIR works at near-field and the relationship between the visibility and the brightness temperature is no longer Fourier Transform. The contours and details of prohibited items are blurry in rebuild image by the traditional inversion method, such as Moore-Penrose method and Tikhonov regularization, so it is difficult to identify prohibited items. In this study, a regularization model based on gradient L1 norm minimization is proposed. In image processing, the contour can be regarded as a marker line where the brightness temperature changes sharply along the vertical direction. So, the gradient filed of the brightness temperature map is appropriate to quantify the contours. And the L1 norm minimization model is able to guarantee the inversion accuracy and enhance the contour. Simulation for SAIR is performed to validate the contour enhancement imaging method. A complex scene consisting of many small regions with different shape and brightness temperature value corresponding to different prohibited items is created. The reconstructed image by proposed method is compared with the results by Moore-Penrose method and Tikhonov regularization. The proposed method shows better reconstructed image quality.
Polarimetric measurements can provide additional information as compared to unpolarized ones. In this paper, linear polarization ratio (LPR) is created to be a feature discriminator. The LPR properties of several materials are investigated using Fresnel theory. The theoretical results show that LPR is sensitive to the material type (metal or dielectric). Then a linear polarization ratio-based (LPR-based) method is presented to distinguish between metal and dielectric materials. In order to apply this method to practical applications, the optimal range of incident angle have been discussed. The typical outdoor experiments including various objects such as aluminum plate, grass, concrete, soil and wood, have been conducted to validate the presented classification method.
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