### Working language
Português - Suitable for English-speaking students
### Goals
The objective of this curricular unit is to present the main concepts and techniques of Digital Image Processing (PDI) with emphasis on images acquired by remote sensing sensors.
### Learning outcomes and skills
It is intended that students:
\- Know and understand the main concepts and methods used in remote sensing image processing.
\- Be able to select and use the appropriate PDI tools to extract relevant information from remote sensing images.
\- Be able to apply the knowledge acquired in the effective analysis of simulated and experimental data, using advanced computational means.
### Working mode
In person
### Program
1\. Basic concepts of digital image processing.
two\. Spot operations / Image calibration.
3\. Spatial Filters / Noise Reduction.
4\. Color representation models.
5\. Image segmentation.
6\. Morphological Operations.
7\. Geometric Corrections / Image referencing.
8\. Multi-Spectral Imaging / Principal Components.
9\. Classification of multi-spectral images.
10\. Clustering / Unsupervised sorting.
11\. Operations in frequency space.
### Mandatory Bibliography
Richards J.A. Jia X.; [Remote Sensing Digital Image Analysis](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000227774 "Remote Sensing Digital Image Analysis (Opens in a new window)")
Gonzalez Rafael C.; [Digital image processing](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000260721 "Digital image processing (Opens in a new window)"). ISBN: 0-13-008519-7
Rafael C. Gonzalez; [Digital image processing using MATLAB](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000278836 "Digital image processing using MATLAB (Opens in a new window)"). ISBN: 0-13-008519-7
### Complementary Bibliography
Szeliski, R.; Computer Vision: Algorithms and Applications, Springer, 2010
Sonka Milan; [Image processing, analysis, and machine vision](http://catalogo.up.pt/F/-?func=find-b&local_base=FCUP&find_code=SYS&request=000281201 "Image processing, analysis, and machine vision (Opens in a new window )"). ISBN: 0-495-08252-X
### Teaching methods and learning activities
The TP classes are used in part for the presentation of theoretical material, illustrated with varied examples, and there is another part dedicated to the execution of small practical (computational) projects. The “Other” type classes are used to support the realization of practical computational work as well as clarify any doubts that students may have.
### Software
SNAP
MATLAB
### Type of evaluation
Distributed evaluation with final exam
### Assessment Components
Oral test: 20.00%
Written work: 30.00%
Practical or project work: 50.00%
**Total:**: 100.00
### Occupation Components
Self-study: 60.00 hours
Frequency of classes: 42.00 hours
Project development: 40.00 hours
Written work: 20.00 hours
**Total:**: 162.00
### Get Frequency
Realization of Practical Works, with the delivery of the respective reports within the established deadlines, and with a classification of not less than 40% of the corresponding quotation (8 values in the 0-20 scale).
Students may have to answer questions related to the practical work carried out, during classes or in an oral exam.
### Final classification calculation formula
The final classification will be determined based on the performance in practical work (50%) and in an individual mini-project (50%), none of these components being able to be less than 40% of the corresponding quotation (8 values in the 0-20 scale).
More information at: https://sigarra.up.pt/fcup/pt/ucurr_geral.ficha_uc_view?pv_ocorrencia_id=479404
