应地理科学学院邀请，Dr. LEI WANG（王磊教授）将于2014年12月8日-12月19日为地理科学学院研究生授课，欢迎全校师生积极参加。
1、教学内容
Course Title: Advanced GIS and Remote Sensing methods
Course description:
Challenges for graduate students in Geography are their problem solving abilities by using geospatial data at various spatial scales and from various sources. the This course will introduce students to some of the cutting-edge remote sensing and GIS methods for spatial data processing and analysis that are normally not included in current textbooks. The overarching goal of the course is to enhance students’ spatial thinking and critical thinking ability for their research equipped with modern technologies. The course take 10 consecutive work days (2 weeks) in order to give students intensive training on the selected topics. It combines lecture (1 hour) and lab hours (2 hours) in each teaching day. This course will give students opportunities of hands-on experiences through series step-by-step in class instruction and lab exercises. This will allow students to comprehend their operation skills on GIS and remote sensing image processing software such as ArcGIS, ENVI and ERDAS to process, analyze and interpret spatial data.
Learning outcomes:
By the end of the course, students will be able to:
1) Describe in great details the methods taught in the class
2) Identify appropriate data sources for using the methods
3) Apply the methods, interpret the results, and fine-tune the parameters
4) Integrate multiple methods for their research projects
Prerequisites:
Intermediate level GIS and Remote Sensing Courses
Basics Statistics and Linear Algebra
Required readings:
There is no text book for this course. Readings will be provided by the instructor and assigned along with the class schedule.
Grading:
1) Final exam (30%)
2) Lab assignments (50%)
3) Individual class projects starting from the end of the class and due in a month. (20%)
2、授课安排（2014年12月8日~12月19日）
Day 1（12月8日）
Morning class: lecture on Kriging
Afternoon class: Exercise on Kriging
Reading assignment: Kriging interpolation
Day 2（12月9日）
Morning class: lecture on co-kriging
Afternoon class: Exercise on co-kriging using ArcGIS
Reading assignment: Cokriging
Day 3（12月10日）
Morning class: lecture on Space-time Kriged Kalman Filtering
Afternoon class: Exercise on Space-time Kriged Kalman Filtering software
Reading assignment: Kriged Kriging methods
Day 4（12月11日）
Morning class: Regression and spatial regression
Afternoon class: Exercise on Regressions
Reading assignment: Regression
Day 5（12月12日）
Morning class: Geographically Weighted Regression
Afternoon class: GWR exercises
Reading assignment: GWR analysis
Day 6（12月15日）
Morning class: lecture on Image matching
Afternoon class: Exercise on Image matching in ArcGIS
Reading assignment: Image matching technique
Day 7（12月16日）
Morning class: Lecture on machine learning
Afternoon class: Exercise on Classification and Regression Tree
Reading assignment: Machine learning methods
Day 8（12月17日）
Morning class: Lecture on Random Forest method
Afternoon class: Exercise on Random Forest method
Reading assignment: Random Forest
Day 9（12月18日）
Morning class: lecture on Subpixel spectral mixture analysis
Afternoon class: Exercise on Linear Spectral Mixture Analysis
Reading assignment: Remote sensing image spectral mixture
Day 10（12月19日）
Summarize the class and project assignment; Final exam