Asmala Ahmad

Analysis of Landsat 5 TM data of Malaysian land covers using ISODATA clustering technique

This study presents a detailed analysis of Iterative Self Organizing Data Analysis (ISODATA) clustering for multispectral data classification. ISODATA is an unsupervised classification method which assumes that each class obeys a multivariate normal distribution, hence requires the class means and covariance matrices for each class. In this study, we use ISODATA to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that ISODATA is able to detect eight classes from the study area with 93% agreement with the reference map. The behavior of mean and standard deviation of the classes in the decision space is believed to be one of the main factors that enable ISODATA to classify the land covers with relatively good accuracy.

Analysis of Maximum Likelihood classification technique on Landsat 5 TM satellite data of tropical land covers

The aim of this paper is to carry out analysis of Maximum Likelihood (ML) on Landsat 5 TM (Thematic Mapper) satellite data of tropical land covers. ML is a supervised classification method which is based on the Bayes theorem. It makes use of a discriminant function to assign pixel to the class with the highest likelihood. Class mean vector and covariance matrix are the key inputs to the function and can be estimated from the training pixels of a particular class. In this study, we used ML to classify a diverse tropical land covers recorded from Landsat 5 TM satellite. The classification is carefully examined using visual analysis, classification accuracy, band correlation and decision boundary. The results show that the separation between mean of the classes in the decision space is to be the main factor that leads to the high classification accuracy of ML.

The use of AVHRR data to determine the concentration of visible and invisible tropospheric pollutants originating from a 1997 forest fire in Southeast Asia

A massive forest fire in Indonesia in 1997 affected the whole Asian region by producing a large smoke plume, with Malaysia bearing the brunt due to the wind direction and weather conditions and because of its proximity to the source. The five primary fire produced pollutants were carbon monoxide (CO), sulphur dioxide (SO2), nitrogen dioxide (NO2), ozone (O3) and particulate matter less than 10 µm (PM10). The first four of these are, of course, invisible to conventional satellite-flown multispectral scanners operating in the visible and near infrared regions of the electromagnetic spectrum. The fifth, PM10, is present in the haze and therefore makes an observable contribution to the signal received by the Advanced Very High Resolution Radiometer (AVHRR). The haze in AVHRR channels 1 and 2 data for the fires of September 1997 has been used to study the concentration of PM10 directly. It has also been used to study the concentration indirectly--as a tracer or surrogate--for the four remaining materials, the gases CO, SO2, NO2 and O3. Data from ground observations have been used to calibrate the results and the distributions of the fire pollutants over Peninsular Malaysia have been plotted.

Modeling of GPS tropospheric delay wet Neill mapping function (NMF)

The modeling of the GPS tropospheric delay mapping function should be revised by modifying or simplify its mathematical model. Some current mapping functions models are separated into hydrostatic and the wet part. The current tropospheric delay models use mapping functions in the form of continued fractions. This model is quite complex and need to be simplified. By using regression method, the wet mapping function models has been selected to be simplified. There are eleven operations for wet mapping function component of Neill Mapping Function (NMF), to be carried out before getting the mapping function scale factor. So, there is a need to simplify the mapping function models to allow faster calculation and also better understanding of the models.

Application of support vector machine for classification of multispectral data

In this paper, support vector machine (SVM) is used to classify satellite remotely sensed multispectral data. The data are recorded from a Landsat-5 TM satellite with resolution of 30x30m. SVM finds the optimal separating hyperplane between classes by focusing on the training cases. The study area of Klang Valley has more than 10 land covers and classification using SVM has been done successfully without any pixel being unclassified. The training area is determined carefully by visual interpretation and with the aid of the reference map of the study area. The result obtained is then analysed for the accuracy and visual performance. Accuracy assessment is done by determination and discussion of Kappa coefficient value, overall and producer accuracy for each class (in pixels and percentage). While, visual analysis is done by comparing the classification data with the reference map. Overall the study shows that SVM is able to classify the land covers within the study area with a high accuracy.

The Impact of Simplified UNBab Mapping Function On GPS Tropospheric Delay

The atmospheric delay issue is widely investigated in order to minimize the positioning error due to tropospheric and ionospheric delay. The mathematical modeling on the tropospheric model mapping functions should be revised and also simplified to represent simpler mapping function models. The zenith tropospheric delay can be amplified by a coefficient factor called mapping function to form total tropospheric delay. The simplified UNBab mapping function models for both hydrostatics and non-hydrostatics can provide better understanding due to its simpler models compared to the established models. The simplified mapping functions for UNBab models for hydrostatic and non hydrostatic components are given in a form of hyperbolic rather than continued fraction form for the established models. By using linear, hyperbolic, logarithm and also regression method, the mapping function models can be simplified and at the same time can produce similar result with the original models. The calculation of tropospheric delay by using simplified UNBab models for both components does not give significant difference from the original models.

A novel technique of controlling signal propagation within array elements using switchable miniaturized electromagnetic band gap

In this paper, a novel miniaturized capacitive loaded electromagnetic band gap (EBG) has been developed with the patch size of approximately)./36. The dimension is controllable using the lumped capacitive elements values. The surface impedance characteristic of the EBG structure is switchable as pin vias have been inserted and removed within the EBG center and the ground plane. Switching the EBG surface impedance characteristic within the array elements has demonstrated the novel application of miniaturized EBG to control the signal propagation within the array elements. Radiation pattern for the middle antenna is enhanced toward the antenna which connected with low surface impedance EBG structure resulting approximately 12dB improvement in gain. The propagation characteristics and gain of the array have been analyzed using CST Microwave Studio (CST MWS). The new application of the EBG can be very useful to realize the controlling and channeling the signal within the array elements.

Effect of Simpler Neill’s Mapping Function Models Using Non Linear Regression Method on Tropospheric Delay

The Simpler Neill’s Mapping Function for both hydrostatics and wet components are established. The comparison between the simpler and the original Neill’s Mapping Function has shown small error difference between them and not significance. The number of operations for simpler models has been reduced compared to the original model for both components.

Simpler hydrostatics component of UNBabc mapping function using nonlinear curve fitting method

Mapping function models should be simplified to improve the method of calculation of tropospheric delay. For UNBabc mapping function, there are 11 mathematical operations to be carried out for getting the mapping function value. To ensure faster calculation, there is a need to simplify the mapping function models. The simplification of UNBabc mapping function model is using nonlinear curve fitting method. The UNBabc mapping function model for hydrostatics component is given in a form of continued fraction. This model has been selected to be simplified, due to its ability to achieve mapping function value, down to 2 degree of elevation angle. The comparison between the original models with the simplified models can be shown by calculating the sum of error. The number of mathematical operation can be reduced after the process of simplification of the model. The reducing percentage of original model to simplified model is up to 81 percent.

Simpler component of non-hydrostatic UNBabc mapping function using nonlinear curve fitting method

UNBabc mapping function for wet component needs 11 operations to be carried out in order to obtain the mapping function value. This wet component is provided in a form of continued fraction. To obtain faster result faster calculation is required. Therefore, there is a need to simplify the mapping function models. In this paper the method used to simplify non hydrostatic UNBabc mapping function model is nonlinear curve fitting method. This model has been selected to be simplified, due to its ability to achieve mapping function value, down to 2 degree of elevation angle. To show that the simplified model result approach the original model the sum of error are used. The comparison between the original models with the simplified models can be shown by calculating the sum of error. The number of mathematical operation can be reduced from 11 operations to 2 operations after the process of simplification of the model.