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Remote Sensing
Abbreviation: B35A03Load: 30(L) + 0(P) + 0(FE) + 0(LE) + 0(S) + 0(PEE) + 0(E) + 30(DE)
Lecturers in charge: doc. dr. sc. Andrija Krtalić
Lecturers: dr. sc. Mateo Gašparović ( Design exercises )
Course description:

Students through lectures acquire knowledge about the following topics:

Overview and definition of remote sensing. Features of the physical fields that are used in remote sensing. Sensors and systems for recording, the impact of platforms and environments. Usable characteristics of sensors. Electro - optical digital matrix cameras, line scanner, thermal cameras, multi-spectral cameras, hyperspectral scanner. Spatial resolution, modulation transfer function, the minimum discriminable contrast, the minimum resolved temperature difference, calibration. Synthetic aperture radar, interferometric and polarimetric mode, usable features. Improving of images. Enhencement, ranking and reduce the amount of features. The method of principal components. Unsupervised classification. Supervised classification. Evaluation of the classification results. Registration and geocoding. Joining of images. Using of softwers for remote sensing in geoscience. Analysis and evaluation of interpretation results. Confusion matrix.

Students through practical work on exercies neet to acquire proficiency in the following skills:

Using of softwer tools (TNTlite, ImageJ, MiltiSpec) for remote sensing. Improving the images. Geometric transformations, joining of images, geocoding. Feature enhencement. Segmentation. Transformation of images in principal components (PCA). Unsupervised and supervised classification. Interpretation of multispectral images (visible, infrared, thermal). Interpretation of hyperspectral and radar images.


Learning outcomes at the level of the programme to which the course contributes
  • The research process in geography.
  • Theoretical basis of remote sensing in regional and spatial planning, characteristic of remote sensing, principles, methods and technology of data acquiring and interpretation of images.
  • Softwer tools for remote sensing.
  • Applying knowledge in determining, defining and solving spatial problems of high complexity.
  • The skills needed for evaluation, interpretation and synthesis of relevant information.
  • The skills needed for presenting scientific contents and stances in written and oral form.
  • Applying mapping of geografical contents, georeferencing.
  • Applying corrresponding maps and cartografical methods in analysis and presentation of the results.
  • Applying corrresponding skills needed for acquiring and interpretation of creation conclusions which include relevant socially, scientific and etical theme.
  • Problem solving related to qualitative and quantitative geographic information.
  • Information-technology skills.
  • Functioning effectively as an individual and as a team member.
  • Autonomous continuous professional improvement needed in professional development.
  • Appying skills of learning needed for entire-llife education.

Learning outcomes expected at the level of the course
  • know and distinguish the features of physical fields which were base of remote sensing, characteristics of remote sensing features in different wavelength regions (multi-spectral, radar, hyperspectral, thermal), principles, methods and technology of the recording, interpretations
  • apply knowledge and understanding of the scene based on multisensor recordings, data processing and interpretation by addressing selected problems within the independent assignments in the remote sensing
  • applying initial skills for interpretation of multisensor, multispectral and hyperspectral images
  • independently drawing the conclusions about the quality and reliability of interpretation
  • publicly present selected problem and its solution through the example from remote sensing
  • identify areas, methods and techniques where necessary lifelong learning
  • used independently one of leading software tool for remote sensing

Course content broken down in detail by weekly class schedule (syllabus)

LECTURES
  1. Introdaction, review and definitions.
  2. Features of physical fields which are using in remote sensing.
  3. Sensors and systems for aerial image aquisition, impact of platform and environment, effectiveness. Electro - optical digital sensors, line scanners, matrix CCD cameras, thermal cameras, multi-spectral cameras, hyperspectral scanner; usable features.
  4. Spatial resolution, modulation transfer function, the minimum discriminable contrast, the minimum resolved temperature difference, calibration. Synthetic aperture radar, interferometric and polarimetric mode, usable features.
  5. Interpretation techniques in remote sensing.
  6. Subjective interpretation, characteristics and limitations.
  7. Interactive interpretation with partially automated functions.
  8. Improving of images. Enhencement, ranking and reduce the amount of features.
  9. Method of principal components
  10. Segmentation.
  11. Automatic classification. Supervised classification.
  12. Registration and geocoding.
  13. Joining of images.
  14. Using software tools for remote sensing.
  15. Presentation of independent assignments.

EXERCISES
  1. Digital multispectral camera, thermovision camera, hyperspectral scanner.
  2. Softwer tools for remote sensing.
  3. Improving of images.
  4. Geometric transformation, joining of images, geocoding.
  5. Feature enhencement.
  6. Segmentation.
  7. Transformation of images in principal components (PCA).
  8. Unsupervised and supervised classification.
  9. Interpretation of multispectral images (visible, infrared, thermal).
  10. Interpretation of hyperspectral and radar images.

Screening student work
  • Class attendance - 0.2
  • Practical training - 0.15
  • Seminar essay - 0.35
  • Tests - 0.3
  • Oral exam - 3 ECTS
  • Written exam - 1 ECTS
Lecture languages: - - -
Compulsory literature:
1. M. Bajić (preradio A. Krtalić), Daljinska istraživanja, rukopis predavanja (2011).
2. A Canada Centre for Remote Sensing, Remote Sensing Tutorial: Fundamentals of Remote Sensing (2011) (http://www.ccrs.nrcan.gc.ca/resource/tutor/fundam/pdf/fundamentals_e.pdf)
Recommended literature:
3. T.M. Lillesand, R.W. Kiefer, Remote sensing and image interpretation, IH-rd edition, John Wiley and Sons, New York, 2000.
4. M. Oluić, Snimanje i istraživanje Zemlje iz svemira, sateliti, senzori, primjena. HAZU i GEOSAT, Zagreb, 2001.
Prerequisit for enrollment:
Passed : Physics
Passed : Basics of Statistics
Legend
L - Lectures
P - Practicum
FE - Field exercises
LE - Laboratory exercises
S - Seminar
PEE - Physical education excercises.
E - Exercises
DE - Design exercises
* - Not graded
Copyright (c) 2006. Ministarstva znanosti, obrazovanja i športa. Sva prava zadržana.
Programska podrška (c) 2006. Fakultet elektrotehnike i računarstva.
Oblikovanje(c) 2006. Listopad Web Studio.
Posljednja izmjena 2017-09-26