Remote Sensing and Digital Image Processing - 2nd semester
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.
José António Tenedório
Weekly - 3 letivas + 1 tutorial
Total - Available soon
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 http://www.clarklabs.org/products/idrisi-gis.cfm
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 http://www.ecognition.com/
U.S. Geological Survey (2015). LANDSAT 8 [em linha]. USGS Web site. Acedido Junho 20, 2015, em http://landsat.usgs.gov/landsat8.php
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.
1 theoretical exam: [25%]
1 practical exam: [25%]
1 project report: [50%[
1 presentation of the project: [pass / no pass].
[According to the FCSH Assessment Standards, the proposed evaluation elements to introduce students in the first class may suffer readjustments, particularly in the percentage of each element.]
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.