Thesis Research on land-cover classification methodologies for optical satellite images

Nội dung tài liệu: Thesis Research on land-cover classification methodologies for optical satellite images

1. VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINE ERING AND TECHNOLOGY MAN DUC CHUC RESEARCH ON LAND-COVER CLASSIFICATION METHODOLOGIES FOR OPTICAL SATELLITE IMAGES MASTER THESIS IN COMPUTER SCIENCE Hanoi – 2017
2. VIETNAM NATIONAL UNIVERSITY, HANOI UNIVERSITY OF ENGINEERING AND TECHNOLOGY MAN DUC CHUC RESEARCH ON LAND-COVER CLASSIFICATION METHODOLOGIES FOR OPTICAL SATELLITE IMAGES DEPARTMENT: COMPUTER SCIENCE MAJOR: COMPUTER SCIENCE CODE: 60480101 MASTER THESIS IN COMPUTER SCIENCE SUPERVISOR: Dr. NGUYEN THI NHAT THANH Hanoi – 2017
3. PLEDGE I hereby undertake that the content of the thesis: “Research on Land- Cover classification methodologies for optical satellite images” is the research I have conducted under the supervision of Dr. Nguyen Thi Nhat Thanh. In the whole content of the dissertation, what is presented is what I learned and developed from the previous studies. All of the references are legible and legally quoted. I am responsible for my assurance. Hanoi, day month year 2017 Thesis’s author Man Duc Chuc
4. ACKNOWLEDGEMENTS I would like to express my deep gratitude to my supervisor, Dr. Nguyen Thi Nhat Thanh. She has given me the opportunity to pursue research in my favorite field. During the dissertation, she has given me valuable suggestions on the subject, and useful advices so that I could finish my dissertation. I also sincerely thank the lecturers in the Faculty of Information Technology, University of Engineering and Technology - Vietnam National University Hanoi, and FIMO Center for teaching me valuable knowledge and experience during my research. Finally, I would like to thank my family, my friends, and those who have supported and encouraged me. This work was supported by the Space Technology Program of Vietnam under Grant VT-UD/06/16-20. Hanoi, day month year 2017 Man Duc Chuc
5. Content CHAPTER 1. INTRODUCTION .................................................................................... 5 1.1. Motivation .......................................................................................................... 5 1.2. Objectives, contributions and thesis structure ................................................... 9 CHAPTER 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 ...................................................................... 26 CHAPTER 3. PROPOSED LAND COVER CLASSIFICATION METHOD ............. 27 3.1. Study area ......................................................................................................... 27 3.2. Data collection ................................................................................................. 28 3.2.1. Reference data ........................................................................................ 28 1
6. 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 ............................................................... 35 CHAPTER 4. EXPERIMENTS AND RESULTS ........................................................ 36 4.1. Compositing results ......................................................................................... 37 4.2. Assessment of land-cover classification based on point validation ................. 38 4.2.1. Yearly single composite classification versus yearly time-series composite classification ......................................................................................... 38 4.2.2. Improvement of ensemble model against single-classifier model ......... 40 4.3. Assessment of land-cover classification results based on map validation ...... 42 CHAPTER 5. CONCLUSION ...................................................................................... 44 2
7. LIST OF TABLES Table 1. Description of seven global land-cover datasets. .............................................. 7 Table 2. Some featured satellite images ........................................................................ 16 Table 3. Landsat 8 bands. .............................................................................................. 18 Table 4. Review of compositing methods for satellite images. ..................................... 20 Table 5. Training and testing data. ................................................................................ 28 Table 6. Summary of Year score, DOY score, Opacity score and Distance to cloud/cloud shadow for L8SR composition ...................................................................................... 33 Table 7. F1 score, F1 score average, OA and kappa coefficient for 7 land cover classes of six classification cases obtained using XGBoost. Best classification cases are written in bold. ........................................................................................................................... 39 Table 8. OA, kappa coefficient, F1 score average for each single-classifier and ensemble model. Best classification cases are written in bold. ..................................................... 40 Table 9. Confusion matrix of ensemble model. ............................................................ 41 Table 10. Error (ha and %) of rice mapped area for different classification scenarios. 43 3
8. LIST OF FIGURES Figure 1. Rice covers map of Mekong river delta, Vietnam in 2012. ............................. 6 Figure 2. The acquisition of data in remote sensing. ..................................................... 11 Figure 3. Introduction of a typical remote sensing system. ........................................... 12 Figure 4. Passive (left) and active (right) remote sensing systems. .............................. 13 Figure 5. Geostationary satellite (left) and Polar orbital satellite (right). ..................... 14 Figure 6. Typical wavelengths used in remote sensing. ................................................ 15 Figure 7. Landsat 8 images ............................................................................................ 17 Figure 8. Landsat 7 and Landsat 8 bands ...................................................................... 18 Figure 9. Comparison of Landsat 8 OLI (left) and SR (right) images. ......................... 19 Figure 10. An example of MLP. .................................................................................... 24 Figure 11. Hanoi city, study area of this study. ............................................................. 28 Figure 12. Examples of experimental data shown in Google Earth, sampled points are represented by while-colored squares over the Google Earth base images. ................. 30 Figure 13. Landsat 8 footprints over Hanoi. ................................................................. 30 Figure 14. Statistics of Landsat 8 SR images over Hanoi, (a) number of images by year and month, (b) cloud coverage percentage per image ................................................... 31 Figure 15. Overall flowchart of the method .................................................................. 32 Figure 16. Clear observation count maps for each image used in the compositing process (DOY 137, 169, 265, 281) ............................................................................................. 34 Figure 17. NDVI (above) and BSI (below) temporal profile of land-cover class ......... 38 Figure 18. (a) Original surface reflectance images, (b) composite images, (c) classification maps for each image, and (d) classified map obtained from time-series composite images. ......................................................................................................... 39 Figure 19. F1 score for land-cover class obtained using multiple classifiers. .............. 41 Figure 20. 2016 Land-cover map for Hanoi based on the most accurate classification using time-series composite imagery and the ensemble of five classifiers. .................. 42 4
9. CHAPTER 1. INTRODUCTION In this chapter, I briefly present an introduction to remote sensing images and its applications in different research areas. Furthermore, the problem of land cover classification is also presented. Current progress and challenges in land cover classification are discussed. Finally, motivations and problem statement of the research are shown in the end of the chapter. 1.1. Motivation Remotely-sensed images have been used for a long time in both military and civilization applications. The images could be collected from satellites, airborne platforms or Unmanned Aerial Vehicles (UAVs). Among the three, satellite images have gained popularity due to large coverage, available data and so on. In general, remotely- sensed images store information about Earth object’s reflectance of lights, i.e. Sun’s light in passive remote sensing [1]. Therefore, the images contain itself lots of valuable information of the Earth’s surface or even under the surface. Applications of remotely-sensed images are diverse. For example, satellite images could be used in agriculture, forestry, geology, hydrology, sea ice, land cover mapping, ocean and coastal [1]. In agriculture, two important tasks are crop type mapping and crop monitoring. Crop type mapping is the process of identification crops and its distribution over an area. This is the first step to crop monitoring which includes crop yield estimation, crop condition assessment, and so on. To these aims, satellite images are efficient and reliable means to derive the required information [1]. In forestry, potential applications could be deforestation mapping, species identification and forest fire mapping. In the forest where human access is restricted, satellite imagery is an unique source of information for management and monitoring purposes. In geology, satellite images could be used for structural mapping and terrain analysis. In hydrology, some possible applications cloud be flood delineation and mapping, river change detection, irrigation canal leakage detection, wetlands mapping and monitoring, soil moisture monitoring, and a lot of other researches. Iceberg detection and tracking is also done via satellite data. Furthermore, air pollution and meteorological monitoring 5
10. could be possible from satellite perspective. In general, many of the applications more or less relate to land cover mapping, i.e. agriculture, flood mapping, forest mapping, sea ice mapping, and so on. Land cover (LC) is a term that refers to the material that lies above the surface of the Earth. Some examples of land covers are: plants, buildings, water and clouds. Land cover is the thing that reflects or radiates the Sun’s lights which then be captured by the satellite’s sensors. Land use and land cover classification (LULCC) has been considering as one of the most traditional and important applications in remote sensing since LULCC products are essential for a variety of environmental applications [2]. Figure 1 shows a land cover map for Mekong river delta, Vietnam in 2012 derived from MODIS images [3]. This map shows distribution of rice lands in the region. Figure 1. Rice covers map of Mekong river delta, Vietnam in 2012. Regarding land cover classification (LCC), there are currently many researches around the world. These researches could be categorized by several criteria such as geographical scale of classification, multiple land covers classification or single land 6