Remote Sensing and Digital Image Processing


a) To deepen understanding of the principles and fundamentals of Remote Sensing;
b) To deepen knowledge of digital image processing algorithms;
c) To explore satellite images for analysis of spatial issues;
d) To develop methodologies for digital image processing
e) To understand the principles and foundations of planning and project management in Remote Sensing.

General characterization





Responsible teacher

José António Pereira Tenedório


Weekly - 3

Total - 280

Teaching language





Blaschke, T., Lang, S., Hay, G. J., (Eds.) (2008). Object-Based Image Analysis. Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Berlin: Springer. (pp. 1-271)

Clark Labs (2015). IDRISI GIS - Geospatial software for monitoring and modeling the Earth system [em linha]. Clark Labs Web site. Acedido Julho 10, 2015, em

Jensen, J. R. (2005). Introductory Digital Image Processing: A Remote Sensing Perspective (3rd ed.). London: Prentice Hall. (pp. 1-515).

Trimble (2015). eCognition Essentials [em linha]. Trimble Web site. Acedido Julho 10, 2015, em

U.S. Geological Survey (2015). LANDSAT 8 [em linha]. USGS Web site. Acedido Junho 20, 2015, em

Teaching method

Lectures, laboratory classes/computer, project, presentations by students

The teaching methodologies are focused on independent work of the student and practice (hands-on). This work is tutorial for the application of theory to practice remote sensing and digital image processing. At the end each student must be able to propose a project using images obtained by remote sensing revealing knowledge and skills accumulated over the course.

Evaluation method

1 practical exam: [25%](25%), 1 project report: [50%] + 1 presentation of the project: [pass / no pass].(50%), 1 theoretical exam: [25%](25%)

Subject matter

1) Advanced topics in Remote Sensing (RS): i) RS data collection; ii) RS process; iii) digital image processing hardware and software considerations; iv) projects development.
2) Image quality and image correction: i) univariate and multivariate image statistics ii) radiometric corrections; iii) geometric corrections.
3) Image enhancement: i) color theory; ii) enhancement; iii) filtering and filters; iv) image fusion; v) principal components analysis; vi) morphological transformations.
4) Indices: i) vegetation indices; ii) built-up area index; iii) texture transformations; iv) fractal dimension.
5) Images classification: i) unsupervised classifications \"pixel-by-pixel\"; ii) supervised classifications \"pixel-by-pixel\"; iii) object-based image analysis (GEOBIA); vi) thematic map accuracy assessment.
6) Applications: project about geographic information extraction from satellite images and integration in GIS.