Integration of Machine Learning Algorithms in Land Cover Classification using Remote Sensing Data

Thesis Abstract

Abstract
The rapid advancement in remote sensing technology has revolutionized the field of land cover classification, offering new opportunities for accurate and efficient analysis of large-scale geographical data. This thesis explores the integration of machine learning algorithms in land cover classification using remote sensing data, aiming to enhance the accuracy and automation of land cover mapping processes. The study focuses on the application of various machine learning techniques, such as support vector machines, random forests, and convolutional neural networks, to classify land cover types based on multispectral and hyperspectral remote sensing imagery. The research begins with a comprehensive review of the literature on remote sensing, machine learning, and land cover classification methods. The literature review highlights the significance of integrating machine learning algorithms in land cover classification to improve classification accuracy and reduce human intervention in the process. Various studies and approaches in the field are analyzed to identify the strengths and limitations of different machine learning algorithms for land cover classification. In the methodology chapter, the research design, data collection methods, preprocessing techniques, feature selection, model training, and validation procedures are detailed. The study utilizes a diverse dataset of multispectral and hyperspectral remote sensing imagery, collected from satellite sensors such as Landsat and Sentinel, to train and evaluate the machine learning models for land cover classification. The methodology also includes the implementation of cross-validation techniques to assess the performance and generalization ability of the models. The findings chapter presents the results of the experiments conducted to evaluate the performance of different machine learning algorithms in land cover classification. The classification accuracy, confusion matrices, and receiver operating characteristic curves are analyzed to compare the effectiveness of support vector machines, random forests, and convolutional neural networks in classifying various land cover types. The findings highlight the strengths and weaknesses of each algorithm and provide insights into the optimal choice of algorithm for specific land cover classification tasks. In the discussion chapter, the implications of the research findings are discussed in relation to the existing literature and practical applications in the field of land cover classification. The discussion also addresses the challenges and future research directions for further improving the accuracy and efficiency of machine learning-based land cover classification methods using remote sensing data. In conclusion, this thesis contributes to the advancement of land cover classification techniques by demonstrating the effectiveness of integrating machine learning algorithms with remote sensing data. The study provides valuable insights into the application of machine learning in automating land cover classification processes, enhancing the scalability and accuracy of geographical information systems. The findings of this research have implications for environmental monitoring, land use planning, and natural resource management, offering new opportunities for sustainable development and conservation initiatives.