Hong Tat Ewe

CLASSIFICATION OF MULTI-TEMPORAL SAR IMAGES FOR RICE CROPS USING COMBINED ENTROPY DECOMPOSITION AND SUPPORT VECTOR MACHINE TECHNIQUE

This paper presents a combined Entropy Decomposition and Support Vector Machine (EDSVM) technique for Synthetic Aperture 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 de. ne 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 multi-polarized 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 de. ne the training sets.

Paddy Fields as Electrically Dense Media: Theoretical Modeling and Measurement Comparisons

Early models for paddy fields consist of a single-layered medium in which coherent effects within clusters of leaves are considered but multiple volume scattering is not. In this paper, the paddy canopy is modeled as a multilayered dense discrete random medium consisting of cylindrical and needle-shaped scatterers. Consideration is given to the coherent and near-field effects of the closely spaced scatterers through the Dense Medium Phase and Amplitude Correction Theory and Fresnel corrections, respectively, in the phase matrix. Then, this dense medium phase matrix is applied in the radiative transfer equations and solved up to the second order to consider double-volume scattering. Ground truth measurements of paddy fields were acquired at Sungai Burung, Selangor, Malaysia, for an entire season from the early vegetative stage of the plants to their reproductive stage. Measured parameters are used in the theoretical model to calculate the backscattering coefficients of paddy fields. Theoretical analysis of the simulation results shows in particular that second-order effects are important for cross-polarized backscatter data and that coherent effects need to be considered at lower frequencies. However, the use of needles to represent paddy leaves tends to underestimate the HH-polarized backscattering coefficients especially at the latter stages of plant growth, i.e., when the leaves are broader. The results are also used for comparisons with the backscattering coefficients obtained from RADARSAT images as well as that of earlier models to test the validity of the dense medium model with promising results.

A mobile phone malicious software detection model with behavior checker

There have been cases reported for the new threats from mobile phone technologies and it has raised the awareness among the technology and antivirus vendors. Malicious programs such as Viruses, Trojan, and Worms have been created and targeted at mobile phone. This paper discusses the possible attacking model on mobile phone adapted from malicious attack on computer. It also presents the types of attack and appropriate solution model for mobile phone. A prototype of the simulation of malicious software and detection software on mobile devices is developed and the results of applying this approach to simulated malicious software and detection software on mobile device are also presented.

A SAR Autofocus Algorithm Based on Particle Swarm Optimization

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 (1-3). This paper presents a novel approach to solve the low-frequency high-order polynomial and high- frequency 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.

Multifractal Dimension and its Geometrical Terrain Properties for Classification of Multi-Band Multi-Polarized SAR Image

Multifractal dimensions Dq for real q are a more general parameter than the fractal dimension in describing geometrical properties. It has been shown that the four multifractal dimensions Di1; D0; D1 and D2 are able to extract difierent surface information of SAR images. In this paper, we investigate the dimension properties of multifractal dimensions. For land use classiflcation where the textural information on the surface is important, it is necessary to look into the properties of multifractal dimensions with the geometrical properties of terrain. In order to extract the surface information from SAR images, the optimum number of multifractal dimensions to be used in the classiflcation process is considered. To address the suitability of these parameters, these parameters are applied on a multi-band SAR image with regions of difierent textural information and the results are studied. The abilities of multifractal dimensions in extracting information for difierent land use

Energy efficient routing for wireless sensor networks with grid topology

Agricultural monitoring using wireless sensor networks has gained much popularity recently. In this paper, we review five existing flat-tree routing algorithms and proposed a new algorithm suitable for applications such as paddy field monitoring using wireless sensor network. One of the popular data collection methods is the data aggregation approach, where sensor readings of several nodes are gathered and combined into a single packet at intermediate relay nodes. This approach decreases the number of packets flowing and minimizes the overall energy consumption of the sensor network. However, most studies in the past do not consider the network delay in this context, which is an essential performance measure in real-time interactive agricultural monitoring through Internet and cellular network. We propose an algorithm called Information Selection Branch Grow Algorithm (ISBG), which aims to optimize the network in achieving higher network lifetime and shortening the end-to-end network delay. The performance of this algorithm is assessed by computer simulation and is compared with the existing algorithms used for data aggregation routing in wireless sensor networks.

Image Processing in Polarimetric SAR Images Using a Hybrid Entropy Decomposition and Maximum Likelihood (EDML)

This paper presents a hybrid entropy decomposition and maximum likelihood (EDML) for synthetic aperture radar (SAR) image analysis and classification with the application of land use management. Entropy decomposition is an effective technique to obtain valuable decomposed parameters for image interpretation with analysis of the underlying scattering mechanisms. However, the main disadvantage of entropy decomposition is that the decision boundaries of the analysis plane are arbitrary. To overcome this problem, maximum likelihood technique is taken into consideration to help to determine the decision boundaries based upon Gaussian probability model. Hence, the hybrid EDML is developed to provide alternative way to improve the classification accuracy. The objective of this paper is to assess the use of polarimetric data for the image analysis and classification of land use management. It is illustrated using a well-known polarimetric AIRSAR data of San Francisco. In this paper, EDML technique is shown to have a superior result compared to other techniques.

An ant colony optimization approach to the degree-constrained minimum spanning tree problem

This paper presents the application of an Ant Colony Optimization (ACO) algorithm approach for communications networks design problem. We explore the use of ACO’s for solving a network optimization problem, the degree-constrained minimum spanning tree problem (d-MST), which is a NP-Hard problem. The effectiveness of the proposed algorithm is demonstrated through two kinds of data set: structured hard (SHRD) complete graphs and misleading (M-graph) complete graphs. Empirical results show that ACO performs competitively with other approaches based on evolutionary algorithm (EA) on certain instance set problem.

MULTILAYER MODEL FORMULATION AND ANALYSIS OF RADAR BACKSCATTERING FROM SEA ICE

The Antarctic continent is an extremely suitable environ- ment 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 backscatter- ing can be used to test the assumptions on the dominating scattering mechanisms. In this paper, the formulation and analysis of a multi- layer 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 efiects 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 ef- fect 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 coe-cients for co- and cross-polarization using the developed model for 1GHz and 10GHz are presented. In addition, the simulated backscattering coe-cients from the multilayer model were compared with the measurement results ob- tained from Coordinated Eastern Artic Experiment (CEAREX) (Gren- fell, 1992) and with the results obtained from the model developed by Saibun Tjuatja (based on the Matrix Doubling method) in 1992.

Autofocus Algorithms Performance Evaluations Using an Integrated SAR Product Simulator and Processor

The design and development of synthetic aperture radar (SAR) system for a particular application often requires redesign of software and hardware to optimize the system performance. In addition, evaluations of the performance of existing autofocus and image formation algorithms are required for the SAR system designers to select a most suitable algorithm for a given image quality requirements. This is a time-consuming taskwithout a reconfigurable and comprehensive software package. Thus, a comprehensive SAR integrated simulator and processor software is needed to aid the system designers in optimizing all the system parameters and performance. This paper presents an integrated SAR simulator and processor (iSARSIMP) software package and the performance of three selected SAR autofocus algorithms has been evaluated as examples to demonstrate the usefulness of the iSARSIMP for SAR system designers. In the performance evaluation, simulated and actual SAR raw data were used for further analysis and comparison of the three selected autofocus algorithms.