1. Home
  2. NOVA Information Management School
  3. Master in Information Management
  4. Specialization in Knowledge Management and Business Intelligence
  5. Remote Sensing

Remote Sensing

Objectives

The curricular unit targets the fundamentals of remote sensing, the main Earth Observation satellite and satellite image processing methods. The final goal is that the student after completion of the unit will be able, in an autonomous way, to design and implement a project to produce information (e.g. thematic cartography) based on satellite images and classification algorithms.

General characterization

Code

200115

Credits

7.5

Responsible teacher

Mário Sílvio Rochinha de Andrade Caetano

Hours

Weekly - Available soon

Total - Available soon

Teaching language

Portuguese. If there are Erasmus students, classes will be taught in English

Prerequisites

There are no specific course prerequisites.

Bibliography

Caetano, M., and H. Costa, 2019. Remote Sensing Practicals, [e-book].

Jensen, J.R., 2016. Introductory Digital Image Processing: a Remote Sensing Perspective, 4a Ed. Glenview : Pearson https://ebookcentral.proquest.com/lib/novaims/detail.action?docID=5831484

CCRS, Canadian Centre for Remote Sensing, 2007. Fundamentals of Remote Sensing , [Online].

Jensen, J.R., 2013. Remote sensing of the environment: an earth resource perspective, 2a Edic¿a¿o. NewJersey: Prentice Hall.

Teaching method

The course has lectures and laboratory sessions. In the lectures, the instructor uses slides to illustrate the theory. The laboratory sessions consists on the use of a image processing software for deriving a thematic map based on spectral and/or temporal pattern analysis. The course also includes seminars to discuss the socioeconomic benefits of remote sensing.
 
The professor promotes an active and collaborative learning based on real world problem solving

Evaluation method

The professor in one of the first classes will discuss with the student the evaluation method to be applied in this course. As a starting point for the discussion it is proposed:

·       Test (individual) - 20%;

·       Practical Project  - 30%. Group project to solve a real problem by using satellite images and an image software chosen by the group. The problem has to be defined by the group. (Presentation and Report)

·       Essay A - 30% - Group project on research and development trends in satellite image classification for thematic mapping. (Presentation and Report)

·       Essay B - 20%.  Individual essay on socio-economic benefits of remote sensing within a specific theme selected by the student from a list provided by the Professor. (Just presentation  - no report)

Dates:

·       Test: 1 de october

·       Essay A: 13 october

·       Practical Project: 20 october

·       Essay B - 22 october

Rules:

·       Minimum grade in each component - 8

·       Projects not delivered within deadline will be penalized (1 point per day, until 7 points)

.       Projects not delivered through platform are not considered

 

In the first class the students agreed to implement the following weights:

  • Test: 20%
  • Essay A: 25%
  • Practical Project: 35%
  • Essay B: 20%

Subject matter

The Curricular Unit has the following Learning Units (LU):

  • LU 1 Introduction to remote sensing and to the course
  • LU 2 Remote sensing principles
  • LU 3 Characteristics of Earth observation satellites and sensors
  • LU 4 Exploratory analysis
  • LU 5 Image pre-processing
  • LU 6 Band transformations
  • LU 7 Image information extraction (image classification)
  • LU 8 Multi-temporal image analysis
  • LU 9 Error analysis in thematic maps
  • LU 10 Socioeconomic benefits of remote sensing
  • LU 11 Practical exercises on satellite image processing
  • LU 12 Real world problem solving based on satellite image processing