Remote Sensing and Digital Image Processing - 2nd semester

Objectives

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

Code

722041036

Credits

10

Responsible teacher

José António Tenedório

Hours

Weekly - 3 letivas + 1 tutorial

Total - Available soon

Teaching language

Portuguese

Prerequisites

None.

Bibliography

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

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 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.]

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.