Anthony Gidudu | Makerere University (original) (raw)

Papers by Anthony Gidudu

Research paper thumbnail of An svm multiclassifier approach to land cover mapping

From the advent of the application of satellite imagery to land cover mapping, one of the growing... more From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.

Research paper thumbnail of Increased-specificity famine prediction using satellite observation data

This paper examines the use of remote sensing satellite data to predict food shortages among diff... more This paper examines the use of remote sensing satellite data to predict food shortages among different categories of households in famine-prone areas. Normalized Difference Vegetation Index (NDVI) and rainfall estimate data, which can be derived from multi-spectral satellite radiometer images, has long been used to predict crop yields and hence famine. This gives an overall prediction of food insecurity in an area, though in a heterogeneous population it does not directly predict which sectors of society or households are most at risk.

Research paper thumbnail of Investigating the effects of ensemble classification on remotely sensed data for land cover mapping

Ensemble classification involves consulting experts in taking final decision in classification pr... more Ensemble classification involves consulting experts in taking final decision in classification process. The idea is to improve classification accuracy when compared to their single classifier counterpart. The system is used in remote sensing imagery to obtain information about Land cover. Major challenges associated with classification accuracy include design procedure of classifier, choice of training sets from dataset and information conveyed to the algorithm. Superiority of different classification approaches employed depends on selected dataset and the strategy used during designing phase of each classifier. However, in ensemble approach, there is no definite number of classifiers that should take part in decision making. This study exploits feature selection technique to create diversity in ensemble classification. Results obtained show that for ensemble approach, there is no significant benefit in having many base classifiers. The outcome should reveal how to design best ensemble using feature selection approach for land cover mapping.

Research paper thumbnail of Land Cover Mapping Using Ensemble Feature Selection Methods

Computing Research Repository, Jan 1, 2008

Ensemble classification is an emerging approach to land cover mapping whereby the final classific... more Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base classifiers for each ensemble were created through an exhaustive search algorithm using different separability indices. For each ensemble, the classification accuracy was derived as well as a diversity measure purported to give a measure of the inensemble diversity. The correlation between ensemble classification accuracy and diversity measure was determined to establish the interplay between the two variables. From the findings of this paper, diversity measures as currently formulated do not provide an adequate means upon which to constitute ensembles for land cover mapping.

Research paper thumbnail of Classification of Images Using Support Vector Machines

Computing Research Repository, Jan 1, 2007

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the la... more Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusions that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.

Research paper thumbnail of An svm multiclassifier approach to land cover mapping

Computing Research Repository, Jan 1, 2010

From the advent of the application of satellite imagery to land cover mapping, one of the growing... more From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.

Research paper thumbnail of Random Ensemble Feature Selection for Land Cover Mapping

Random ensemble feature selection is a means through which diversity in ensemble systems is impos... more Random ensemble feature selection is a means through which diversity in ensemble systems is imposed by randomly selecting the features (bands) that constitute the base classifiers. This paper provides insight and discusses the interplay between the size of the resulting ...

Research paper thumbnail of Image Classification Using SVMs: One-against-One Vs One-against-All

Computing Research Repository, Jan 1, 2007

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the la... more Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusion therefore that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.

Research paper thumbnail of An svm multiclassifier approach to land cover mapping

From the advent of the application of satellite imagery to land cover mapping, one of the growing... more From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.

Research paper thumbnail of Increased-specificity famine prediction using satellite observation data

This paper examines the use of remote sensing satellite data to predict food shortages among diff... more This paper examines the use of remote sensing satellite data to predict food shortages among different categories of households in famine-prone areas. Normalized Difference Vegetation Index (NDVI) and rainfall estimate data, which can be derived from multi-spectral satellite radiometer images, has long been used to predict crop yields and hence famine. This gives an overall prediction of food insecurity in an area, though in a heterogeneous population it does not directly predict which sectors of society or households are most at risk.

Research paper thumbnail of Investigating the effects of ensemble classification on remotely sensed data for land cover mapping

Ensemble classification involves consulting experts in taking final decision in classification pr... more Ensemble classification involves consulting experts in taking final decision in classification process. The idea is to improve classification accuracy when compared to their single classifier counterpart. The system is used in remote sensing imagery to obtain information about Land cover. Major challenges associated with classification accuracy include design procedure of classifier, choice of training sets from dataset and information conveyed to the algorithm. Superiority of different classification approaches employed depends on selected dataset and the strategy used during designing phase of each classifier. However, in ensemble approach, there is no definite number of classifiers that should take part in decision making. This study exploits feature selection technique to create diversity in ensemble classification. Results obtained show that for ensemble approach, there is no significant benefit in having many base classifiers. The outcome should reveal how to design best ensemble using feature selection approach for land cover mapping.

Research paper thumbnail of Land Cover Mapping Using Ensemble Feature Selection Methods

Computing Research Repository, Jan 1, 2008

Ensemble classification is an emerging approach to land cover mapping whereby the final classific... more Ensemble classification is an emerging approach to land cover mapping whereby the final classification output is a result of a consensus of classifiers. Intuitively, an ensemble system should consist of base classifiers which are diverse i.e. classifiers whose decision boundaries err differently. In this paper ensemble feature selection is used to impose diversity in ensembles. The features of the constituent base classifiers for each ensemble were created through an exhaustive search algorithm using different separability indices. For each ensemble, the classification accuracy was derived as well as a diversity measure purported to give a measure of the inensemble diversity. The correlation between ensemble classification accuracy and diversity measure was determined to establish the interplay between the two variables. From the findings of this paper, diversity measures as currently formulated do not provide an adequate means upon which to constitute ensembles for land cover mapping.

Research paper thumbnail of Classification of Images Using Support Vector Machines

Computing Research Repository, Jan 1, 2007

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the la... more Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusions that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.

Research paper thumbnail of An svm multiclassifier approach to land cover mapping

Computing Research Repository, Jan 1, 2010

From the advent of the application of satellite imagery to land cover mapping, one of the growing... more From the advent of the application of satellite imagery to land cover mapping, one of the growing areas of research interest has been in the area of image classification. Image classifiers are algorithms used to extract land cover information from satellite imagery. Most of the initial research has focussed on the development and application of algorithms to better existing and emerging classifiers. In this paper, a paradigm shift is proposed whereby a committee of classifiers is used to determine the final classification output. Two of the key components of an ensemble system are that there should be diversity among the classifiers and that there should be a mechanism through which the results are combined. In this paper, the members of the ensemble system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output was determined through a simple majority vote of the individual classifiers. From the results obtained it was observed that the final derived map generated by an ensemble system can potentially improve on the results derived from the individual classifiers making up the ensemble system. The ensemble system classification accuracy was, in this case, better than the linear and quadratic SVM result. It was however less than that of the RBF SVM. Areas for further research could focus on improving the diversity of the ensemble system used in this research.

Research paper thumbnail of Random Ensemble Feature Selection for Land Cover Mapping

Random ensemble feature selection is a means through which diversity in ensemble systems is impos... more Random ensemble feature selection is a means through which diversity in ensemble systems is imposed by randomly selecting the features (bands) that constitute the base classifiers. This paper provides insight and discusses the interplay between the size of the resulting ...

Research paper thumbnail of Image Classification Using SVMs: One-against-One Vs One-against-All

Computing Research Repository, Jan 1, 2007

Support Vector Machines (SVMs) are a relatively new supervised classification technique to the la... more Support Vector Machines (SVMs) are a relatively new supervised classification technique to the land cover mapping community. They have their roots in Statistical Learning Theory and have gained prominence because they are robust, accurate and are effective even when using a small training sample. By their nature SVMs are essentially binary classifiers, however, they can be adopted to handle the multiple classification tasks common in remote sensing studies. The two approaches commonly used are the One-Against-One (1A1) and One-Against-All (1AA) techniques. In this paper, these approaches are evaluated in as far as their impact and implication for land cover mapping. The main finding from this research is that whereas the 1AA technique is more predisposed to yielding unclassified and mixed pixels, the resulting classification accuracy is not significantly different from 1A1 approach. It is the authors conclusion therefore that ultimately the choice of technique adopted boils down to personal preference and the uniqueness of the dataset at hand.