Course Description

This graduate-level course is an    introduction to the rapidly changing field of remote sensing. The course    covers various remote sensing techniques such as passive visible and    infra-red imaging systems and active radar/LiDAR systems in 3D mapping, land    cover change detection and urban management. BigData technologies (Hadoop    MapReduce) and Artificial Intelligent (AI) technologies such as    classification model and deep learning (e.g. artificial neural network) in    the remote sensing data processing and feature extraction will be discussed    as well. We will also review the hot research topics in remote sensing and    their applications in natural resources, environmental monitoring, digital    mapping and urban management.

Objectives

After completing this course, students will    understand various latest remote sensing techniques, their applications and    the hot research topics.

Reference Books (optional)

Jensen, John R., 2015, Introductory    Digital Image Processing, 4th Ed., Pearson Education, Glenview, IL    60025, 544 pages, ISBN: 013405816X

Jensen, John R., 2007, Remote Sensing of    the Environment: An Earth Resource Perspective, 2nd Ed., Upper Saddle    River, NJ: Prentice Hall, 592 pages. ISBN: 0-13-188950-8

Lillesand, T.M, R. Kiefer, J.W., Chipman,    2004, Remote Sensing and Image Interpretation, 5^th Ed.    John Wiley & Sons, Inc, 763 pages, ISBN: 0-471-45153-5

7月13日8:30-12:00

Introduction to the Course

Remote Sensing Techniques   and Hot Research Topics Overview

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教一楼406

7月14日8:30-12:00

Integrating Multispectral   Remote Sensing and GIS in Vegetation Mapping, Change Detection and Landscape   Change Prediction

Dynamic Modeling Approach for Simulation of   Socioeconomic Effects on Landscape Change, 2001, Ecological Mode 140(1-2):   141-162

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7月15日8:30-12:00

SAR and LiDAR in 3D   Mapping and DEM Development

Airborne Dual-Band IFSAR DTM Processing, ASPRS   2011 Annual Conference, Milwaukee, Wisconsin, May 1-5, 2011

4

7月16日8:30-12:00

Integrated LiDAR Full   Waveform and Hyperspectral Remote Sensing Data for Feature Extraction

Fusion of High Spatial Resolution WorldView-2   Imagery and LiDAR Pseudo-Waveform for Object-Based Image Analysis. ISPRS   Journal of Photogrammetry and Remote Sensing, 2015, 101: 221-232.

ICESat Waveform-Based Landcover Classification   using a Curve Matching Approach. International Journal of Remote Sensing,   2015, 36 (1): 36-60.

5

7月17日8:30-12:00

BigData Technologies in   Remote Sensing Data Processing using Hadoop

Cloud Hadoop Map Reduce For Remote Sensing Image   Analysis, Journal of Emerging Trends in Computing and Information Sciences,   VOL. 3, NO. 4, April 2012.

CMUNE: A   Clustering using Mutual Nearest Neighbors Algorithm. “Information Science, Signal Processing and their Applications (ISSPA) , 2012 7:1192-1197

4

7月18日8:30-12:00

Thematic Remote Sensing   Information Extraction with Artificial Intelligence (AI) Using Apache Spark   Machine Learning (Deep Learning ANN and Classification model)

A SPLIT model for extraction of subpixel   impervious surface information, 2004, PE&RS, 70(7): 821-828

Demonstration: AI (with Spark and Hadoop) in   remote sensing data processing

4

7月19日8:30-12:00

Group Discussion and   Presentation

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Dr. Zhang is currently working as a GIO / Chief   Solution Architect in Spatial Front Inc. His research interests focus on   spatial dynamic mechanism, GIS, and various remote sensing technologies   (multispectral, hyperspectral, SAR, LiDAR, and Photogrammetry) used in urban   management, digital mapping, environmental monitoring, ecological change, and   land use/cover change detection. One of his main research interests is   developing a theory and methodology that can bridge driving factor and land   use/cover change by defining a chain spatial dynamic mechanism and   establishing a spatial dynamic model with the use of remote sensing and GIS.   Dr. Zhang earned his Ph.D from the State Key Laboratory of Resources and   Environmental Information System (LERIS) at the Chinese Academy of Sciences.   During his Ph.D study, he was involved in the State Key Laboratory’s   “Integrated Systems of Natural Disaster Monitoring and Assessment with Remote   Sensing and GIS" project during an Eighth Five-Year-Plan Period in   China. He subsequently accomplished his post-doc program at the University of   Illinois at Chicago and the University of Rhode Island. As the lead   scientist, he has been involved in 3 NASA (National Aeronautics and Space   Administration) funded remote sensing research projects as well as 7 other   federal government sponsored research projects as part of his post-doctoral   researcher position. As the senior Geospatial Architect and Remote Sensing   Scientist at Fugro Inc, Dr. Zhang led the R&D team to design and develop   the enterprise SAR factory -- an automatic radar data processing system and   Fugro’s PanoramiX solution – a comprehensive, efficient oblique mapping from   multiple viewing angles combining with powerful 3D mapping and visualization   software for easy analysis of imagery. Dr. Zhang has also been involved in 28   large-scale remote sensing and photogrammetry projects such as electric   corridor mapping with LiDAR, the Tennessee State-wide mapping project which   utilized photogrammetry and LiDAR, NOAA Coastal   Geospatial Services with airborne digital and hyperspectral imagery, LiDAR,   and IFSAR, and Alaska’s State-wide GeoSAR mapping project which used a   combination of IFSAR and LiDAR. As the Chief Technology Officer (CTO) in Data   Enhancement Services, LLC, Kevin led numerous national and state -wide GIS,   photogrammetry and LiDAR projects such as the State of Delaware NHD   development using LiDAR and remote sensing. As the GIO in Spatial Front Inc,   Dr. Zhang is currently leading the R&D on Artificial Intelligence (AI) in   remote sensing data processing, digital mapping, urban build-up inventory and   feature extractions. In addition, Dr. Zhang led 15 enterprise application,   system integration, GIS and remote sensing projects contracted by various   organizations in the Public and Private Sectors. Additionally, Dr. Zhang has   published 28 papers in peer-reviewed professional journals, 3 book chapters,   and has held 25 presentations in both national and international conferences.   Dr. Zhang's articles appeared in the top international journals in remote   sensing and GIS fields such as Photogrammetric Engineering & Remote   Sensing and Ecological Modeling, and his book chapters were published by   the top academic publishers such as Science China Press and Taylor &   Francis Group. Furthermore, he has received numerous honors, which   include the prestigious ESRI Award (First Place) for the Best Scientific   Paper from the American Society of Photogrammetry and Remote Sensing, the   Elide Award (First Place) from the Chinese Academy of Sciences, and the   Presidential Scholarship (Second Place) from the Institute of Geography,   Chinese Academy of Sciences.