A Comparison between Normal and Non-Normal Data in Bootstrap

In the area of statistics, bootstrapping is a general modern approach to resampling methods. Bootstrapping is a way of estimating an estimator such as a variance when sampling from a certain distribution. The approximating distribution is based on the observed data. A set of observations is a population of independent and observed data identically distributed by resampling; the set is random with replacement equal in size to that of the observed data. The study starts with an introduction to bootstrap and its procedure and resampling. In this study, we look at the basic usage of bootstrap in statistics by employing R. The study discusses the bootstrap mean and median. Then there will follow a discussion of the comparison between normal and non-normal data in bootstrap. The study ends with a discussion and presents the advantages and disadvantages of bootstraps

Classification Simulation of RazakSAT Satellite

This study presents simulation of land cover classification for RazakSAT satellite. The simulation makes use of the spectral capability of Landsat 5 TM satellite that has overlapping bands with RazakSAT. The classification is performed using Maximum Likelihood (ML), a supervised classification method that is based on the Bayes theorem. ML 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 are estimated from the training pixels of a particular class. The accuracy of the classification for the simulated RazakSAT data is accessed by means of a confusion matrix. The results show that RazakSAT tends to have lower overall and individual class accuracies than Landsat mainly due to the unavailability of mid-infrared bands that hinders separation between different plant types

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

Classification Simulation of RazakSAT Satellite

This study presents simulation of land cover classification for RazakSAT satellite. The simulation makes use of the spectral capability of Landsat 5 TM satellite that has overlapping bands with RazakSAT. The classification is performed using Maximum Likelihood (ML), a supervised classification method that is based on the Bayes theorem. ML 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 are estimated from the training pixels of a particular class. The accuracy of the classification for the simulated RazakSAT data is accessed by means of a confusion matrix. The results show that RazakSAT tends to have lower overall and individual class accuracies than Landsat mainly due to the unavailability of mid-infrared bands that hinders separation between different plant types

Analysis of Maximum Likelihood Classification on Multispectral Data

The aim of this paper is to carry out analysis of Maximum Likelihood (ML)classification on multispectral data by means of qualitative and quantitative approaches. 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 Effects of Haze on the Spectral and Statistical Properties of Land Cover Classification

Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat satellite. The effects of haze on the data can be observed from the spectral and statistical properties of land cover classification. The work presented in this thesis is meant to analyse the statistical properties of land cover classification of hazy dataset. Maximum Likelihood (ML) was found to be a preferable classification scheme in which the effects of haze can be investigated. The study made use of hazy dataset that were simulated based on real haze spectral and statistical properties. By investigating these dataset, the spectral and statistical properties of the land classes can be systematically analysed, in which showing that haze modifies the class spectral signatures and statistical properties, consequently causing the data quality to decline

The Effects of Haze on the Accuracy of Maximum Likelihood Classification

This study aims to investigate the effects of haze on the accuracy of Maximum Likelihood classification. Data containing eleven land covers recorded from Landsat 5 TM satellite were used. Two ways of selecting training pixels were considered which are choosing from the haze-affected and haze-free data. The accuracy of Maximum Likelihood classification was computed based on confusion matrices where the accuracy of the individual classes and the overall accuracy were determined. The result of the study shows that classification accuracies declines with faster rate as visibility gets poorer when using training pixels from clear compared to hazy data

Comparative Analysis of Supervised and Unsupervised Classification on Multispectral Data

The aim of this study is to compare two methods of image classification, i.e. ML (Maximum Likelihood), a supervised method, and ISODATA (Iterative Self- Organizing Data Analysis Technique), an unsupervised method. The former is knowledge-driven, while the latter is data-driven. The former needs a priori knowledge about the study area but the latter does not. In practice, the former can classify land covers with a higher accuracy and therefore is more widely used but there have been very few attempts to investigate this. Here we use both methods in our study area, Selangor, Malaysia and compare the outcomes by means of qualitative and quantitative analyses to have a better understanding of the underlying reasons that drive the performance of both methods

The effects of haze on the accuracy of satellite land cover classification

Remote sensing data have long been the primary source for land cover map derivation. Nevertheless, for countries within haze-affected regions such as Malaysia, the existence of haze in the atmosphere tends to degrade the data quality. Such scenario is due to attenuation of recorded reflectances in which consequently affects the land cover classification task prior to the map derivation. This study aims to determine the effects of haze on the accuracy of land cover classification. Landsat-5 TM (Thematic Mapper) satellite data over the district of Klang, located in the state of Selangor, Malaysia were used. To account for haze effects, the study made use the Landsat datasets that have been integrated with haze layers. Maximum Likelihood (ML) classification was performed on the hazy datasets using training pixels extracted from the respective datasets. The accuracy of the classification was computed using confusion matrices where individual class and overall accuracy were determined. The results show that individual class accuracy is influenced not only by haze concentration but also class spectral properties. Overall classification accuracy declines with faster rate as visibility gets poorer

Haze Modelling and Simulation in Remote Sensing Satellite Data

In atmospheric haze studies, it is almost impossible to obtain remote sensing data which have the required haze concentration levels. This problem can be overcome if we can generate haze layer based on the properties of real haze to be integrated with remote sensing data. This work aims to generate remote sensing datasets that have been degraded with haze by taking into account the spectral and spatial properties of real haze. Initially, we modelled solar radiances observed from satellite by taking into consideration direct and indirect radiances reflected from the Earth surface during hazy condition. These radiances are then simulated using the 6SV1 radiative transfer model so that the radiances due to haze, or the so called ‘haze layer’, can be computed. The spatial distribution of the haze layer is simulated based on multivariate Gaussian distribution. The haze layer is finally added to a real dataset to produce a hazy dataset. The generated hazy datasets are to be used in investigating the effects of haze on land cover classification in the future