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.
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 Detec¿a¿o Remota, [e-book]. NOVA Information and Management School, Universidade Nova de Lisboa.
Caetano, M., and H. Costa, 2019. Remote Sensing Practicals, [e-book].
Jensen, J.R., 2016. Introductory Digital Image Processing: a Remote Sensing Perspective, 4a Edic¿a¿o. Glenview : Pearson
CCRS, Canadian Centre for Remote Sensing, 2007. Fundamentals of Remote Sensing , [Online].
Remote sensing of land use and land cover: principles and applications, 2012. In Series in Remote Sensing Applications, Ed: Chandra P. Giri . Boca Raton : CRC, 425 p.
9781420070743. ACESSO NOVA: https://doi.org/10.1201/b11964
Jensen, J.R., 2013. Remote sensing of the environment: an earth resource perspective, 2a Edic¿a¿o. NewJersey: Prentice Hall.
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:
- Test (individual) - 30%;
- Practical Project - 40%. 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 and participation in class - 30%. Group essay on socio-economic benefits of remote sensing within a specific theme selected by the student from a list provided by the Professor. The essay topic may be focused in another domain agreed between students group and professor (Just presentation - no report)
- Test: 23rd March
- Essay: 6th to 15th April
- Project and essays presentation and discussion: 18th and 20th May
- 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 second moment or any other moment of evaluation, the components of evaluation are exactly the same and all projects have to be individual and have to be different from the ones presented during the semester (if existing).
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