Master thesis in Computer science: Research on land cover classification methodologies for optical satellite images

In this thesis, I have proposed a LCC method for these areas. Firstly, a dense time-series of composite images was constructed from all available multi-year Landsat 8 images over the study area. A m odified compositing method was proposed for the compositing process using Landsa t 8 SR images.
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Master thesis in Computer science: Research on land cover classification methodologies for optical satellite images VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY MAN DUC CHUC RESEARCH ON LAND-COVER CLASSIFICATIONMETHODOLOGIES FOR OPTICAL SATELLITE IMAGES MASTER THESIS IN COMPUTER SCIENCE Hanoi – 2017 VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY MAN DUC CHUC RESEARCH ON LAND-COVER CLASSIFICATIONMETHODOLOGIES FOR OPTICAL SATELLITE IMAGESDEPARTMENT: COMPUTER SCIENCEMAJOR: COMPUTER SCIENCECODE: 60480101 MASTER THESIS IN COMPUTER SCIENCE SUPERVISOR: Dr. NGUYEN THI NHAT THANH Hanoi – 2017PLEDGE I hereby undertake that the content of the thesis: “Research on Land-Cover classification methodologies for optical satellite images” is the researchI have conducted under the supervision of Dr. Nguyen Thi Nhat Thanh. In thewhole content of the dissertation, what is presented is what I learned anddeveloped from the previous studies. All of the references are legible and legallyquoted. I am responsible for my assurance. Hanoi, day month year 2017 Thesis’s author Man Duc ChucACKNOWLEDGEMENTS I would like to express my deep gratitude to my supervisor, Dr. Nguyen ThiNhat Thanh. She has given me the opportunity to pursue research in my favoritefield. During the dissertation, she has given me valuable suggestions on thesubject, and useful advices so that I could finish my dissertation. I also sincerely thank the lecturers in the Faculty of InformationTechnology, University of Engineering and Technology - Vietnam NationalUniversity Hanoi, and FIMO Center for teaching me valuable knowledge andexperience during my research. Finally, I would like to thank my family, my friends, and those who havesupported and encouraged me. This work was supported by the Space Technology Program of Vietnamunder Grant VT-UD/06/16-20. Hanoi, day month year 2017 Man Duc ChucContentCHAPTER 1. INTRODUCTION....................................................................................5 1.1. Motivation ..........................................................................................................5 1.2. Objectives, contributions and thesis structure ...................................................9CHAPTER 2. THEORETICAL BACKGROUND .......................................................10 2.1. Remote sensing concepts .................................................................................10 2.1.1. General introduction ..............................................................................10 2.1.2. Classification of remote sensing systems ..............................................12 2.1.3. Typical spectrum used in remote sensing systems ................................14 2.2. Satellite images ................................................................................................15 2.2.1. Introduction ............................................................................................15 2.2.2. Landsat 8 images ...................................................................................17 2.3. Compositing methods ......................................................................................20 2.4. Machine learning methods in land cover study ...............................................21 2.4.1. Logistic Regression................................................................................21 2.4.2. Support Vector Machine ........................................................................22 2.4.3. Artificial Neural Network ......................................................................23 2.4.4. eXtreme Gradient Boosting ...................................................................25 2.4.5. Ensemble methods .................................................................................25 2.4.6. Other promising methods ......................................................................26CHAPTER 3. PROPOSED LAND COVER CLASSIFICATION METHOD .............27 3.1. Study area .........................................................................................................27 3.2. Data collection .................................................................................................28 3.2.1. Reference data........................................................................................28 1 3.2.2. Landsat 8 SR data ..................................................................................30 3.2.3. Ancillary data .........................................................................................31 3.3. Proposed method ..............................................................................................31 3.3.1. Generation of composite images ...........................................................32 3.3.2. Land cover classification .......................................................................34 3.4. Metrics for classification assessment ...............................................................35CHAPTER 4. EXPERIMENTS AND RESULTS ........................................................36 4.1. Compositin ...