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A proper model of RF absorber must be developed based on information such as absorber reflectivity, in magnitude and phase, for various angles of incidence, and for parallel and perpendicular polarizations. Unfortunately, these data are not available due to the practical limitations of the test fixtures to measure the RF absorber performance. Manufacturer data sheets normally specify only the magnitude of the absorber reflectivity for normal incidence. A model has been developed in this paper for pyramidal RF absorber with pyramid length shorter than a quarter wavelength and poor reflectivity performance. Since the reflection from the metal backing would be much higher than the reflection and scattering from the pyramid tips, the metal boundary may be modeled as a lossy dielectric with certain effective dielectric constant, ε ef f , and effective conductivity, σ eff , and the thickness extends to infinity. The appropriate values of ε eff and σ eff can be derived based on the reflectivity information given by the manufacturer's data sheet. The reflectivity at oblique incidence is calculated and compared with the results of method of homogenization and moment method. A reasonable match between the different models is obtained. The plane-boundary dielectric model can be used to evaluate the degradation of reflectivity level with respect to angle of incidence. It can be used in a simulation tool for design of anechoic chamber.

Abstract-The Antarctic continent is an extremely suitable environment for the application of remote sensing technology as it is one of the harshest places on earth. Satellite images of the terrain can be properly interpreted with thorough understanding of the microwave scattering process. The proper model development for backscattering can be used to test the assumptions on the dominating scattering mechanisms. In this paper, the formulation and analysis of a multilayer model used for sea ice terrain is presented. The multilayer model is extended from the previous single layer model developed based on the Radiative Transfer theory. The Radiative Transfer theory is chosen because of its simplicity and ability to incorporate multiple scattering effects into the calculations. The propagation of energy in the medium is characterized by the extinction and phase matrices. The model also incorporates the Dense Medium Phase and Amplitude Correction Theory (DM-PACT) where it takes into account the close spacing effect among scatterers. The air-snow interface, snow-sea ice interface and sea ice-ocean interface are modelled using the Integral Equation Method (IEM). The simulated backscattering coefficients for co-and cross-polarization using the developed model for 1 GHz and 10 GHz are presented. In addition, the simulated backscattering coefficients from the multilayer model were compared with the measurement results obtained from Coordinated Eastern Artic Experiment (CEAREX) (Grenfell, 1992) and with the results obtained from the model developed by Saibun Tjuatja (based on the Matrix Doubling method) in 1992.

Abstract-This paper presents a combined Entropy Decomposition and Support Vector Machine (EDSVM) technique for SyntheticAperture Radar (SAR) image classification with the application on rice monitoring. The objective of this paper is to assess the use of multi-temporal data for the supervised classification of rice planting area based on different schedules. Since adequate priori information is needed for this supervised classification, ground truth measurements of rice fields were conducted at Sungai Burung, Selangor, Malaysia for an entire season from the early vegetative stage of the plants to the ripening stage. The theoretical results of Radiative Transfer Theory based on the ground truth parameters are used to define training sets of the different rice planting schedules in the feature space of Entropy Decomposition. The Support Vector Machine is then applied to the feature space to perform the image classification. The effectiveness of this algorithm is demonstrated using multi-temporal RADARSAT-1 data. The results are also used for comparison with the results based on information of training sets from the image using Maximum Likelihood technique, Entropy Decomposition technique and Support Vector Machine technique. The proposed method of EDSVM has shown to be useful in retrieving polarimetric information for each class and it gives a good separation between classes. It not only gives significant results on the classification, but also extends the application of Entropy Decomposition to cover multi-temporal data. Furthermore, the proposed method offers the ability to analyze single-polarized, multi-temporal data with the advantage of the unique features from the combined method of Entropy Decomposition and Support Vector Machine which previously only applicable to multipolarized data. Classification based on theoretical modeling is also one of the key components in this proposed method where the results from the theoretical models can be applied as the input of the proposed method in order to define the training sets.

Abstract-In synthetic aperture radar (SAR) processing, autofocus techniques are commonly used to improve SAR image quality by removing its residual phase errors after conventional motion compensation. This paper highlights a SAR autofocus algorithm based on particle swarm optimization (PSO). PSO is a population-based stochastic optimization technique based on the movement of swarms and inspired by social behavior of bird flocking or fish schooling. PSO has been successfully applied in many different application areas due to its robustness and simplicity [1][2][3]. This paper presents a novel approach to solve the low-frequency high-order polynomial and highfrequency sinusoidal phase errors. The power-to-spreading noise ratio (PSR) and image entropy (IE) are used as the focal quality indicator to search for optimum solution. The algorithm is tested on both simulated two-dimensional point target and real SAR raw data from RADARSAT-1. The results show significant improvement in SAR image focus quality after the distorted SAR signal was compensated by the proposed algorithm.

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Abstract-An Experimental Airborne Synthetic Aperture Radar (SAR) Sensor has been designed and developed at Multimedia University, Malaysia. The airborne system is an inexpensive C-band, single polarization, linear-FM airborne radar sensor. An innovative cancellation network is implemented to overcome the poor isolation of the circulator thus allow a single antenna to be used for transmitting and receiving the radar signal. The system will be used for monitoring and management of earth resources such as paddy fields, oil palm plantation and soil surface. This paper highlights the design and development of the SAR transmitter and receiver, as well as the evaluation result of the sensor. Calibration has been performed in the laboratory to verify the performance of the radar sensor. External calibration is accomplished by using three artificial point targets, i.e., 12" conducting sphere, 4"×8" dihedral corner reflector and 8" trihedral corner reflector. The field measurements are conducted in an empty car park, which is a low reflection outdoor environment. Both range detection and radar cross section (RCS) measurement capability are verified in the field experiments.