On successful completion of the course students are expected to be able to (i.e. learning outcomes):
- Describe the types of measurements in remote sensing and explain why satellite images can be used to produce geographic information
- Develop in an autonomous way a project to produce information based on satellite images with a spatial resolution from 1m to 1000m
- Select the satellite and sensor more adequate to use on the production of different types of information on different spatial scales
- Describe and apply classification algorithms of spectral, spatial and temporal patterns of satellite images in order to derive information
- Assess and interpret the error within information derived from satellite images
- Describe and evaluate the social economic benefits of remote sensing
Mário Sílvio Rochinha de Andrade Caetano
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
There are no specific course prerequisites.
Caetano, M., 2018. Teoria de Deteção Remota, [e-book]. Instituto Superior de Estatística e Gestão da Informação, Universidade Nova de Lisboa.
Caetano, M., 2012. Prática de Deteção Remota, [e-book]. Instituto Superior de Estatística e Gestão da Informação, Universidade Nova de Lisboa.
Jensen, J.R., 2004. Introductory Digital Image Processing: a Remote Sensing Perspective, 3ª Edição. New Jersey: Prentice-Hall.
Jensen, J., 2006. Remote sensing of the environment: an earth resource perspective, 2ª Edição. New Jersey: Prentice Hall
Warner, T., M. Nellis e G. Foody (Eds.), 2009. The SAGE Handbook of Remote Sensing. London: SAGE Publications Ltd.
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
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:
- Online Test (individual) – 30%. The test has two parts: (1) a set of 40 sentences and the student has to identify the true and the false ones, (2) short essay on a topic to be provided on the day of the exam;
- Essay – 30%. Essay on socio-economic benefits of remote sensing within a specific theme selected by the student from a list provided by the Professor (individual) – 30%
- Project - 40%. Group project (2 to 3 persons) 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.
The project and the essay are to be presented in the final seminar of the course. The students have to deliver one report for each work. The delivery date is defined by the coordination of the master program.
- Introduction to remote sensing and to the course
- Remote sensing principles
- Characteristics of Earth observation satellites and sensors
- Exploratory analysis
- Image pre-processing
- Band transformations
- Image information extraction
- Multi-temporal image analysis
- Socioeconomic benefits of remote sensing
- Practical exercises on satellite image processing
- Real world problem solving based on satellite image processing
Programs where the course is taught: