International Teaching | REMOTE SENSING
International Teaching REMOTE SENSING
Back
cod. 0622700076
REMOTE SENSING
| 0622700076 | |
| DEPARTMENT OF INFORMATION AND ELECTRICAL ENGINEERING AND APPLIED MATHEMATICS | |
| EQF7 | |
| COMPUTER ENGINEERING | |
| 2023/2024 |
| YEAR OF COURSE 2 | |
| YEAR OF DIDACTIC SYSTEM 2022 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| ING-INF/03 | 3 | 24 | LESSONS | |
| ING-INF/03 | 3 | 24 | EXERCISES |
| Objectives | |
|---|---|
| THE COURSE AIMS TO PROVIDE THE ELEMENTS TO COMPREHEND AND UTILIZE THE METHODS EXPLOITED IN REMOTE SENSING AND TO OUTLINE ITS MAIN APPLICATIONS KNOWLEDGE AND UNDERSTANDING: REMOTE SENSING SYSTEMS; CLASSIFICATION METHODS; IMAGE PROCESSING APPLYING KNOWLEDGE AND UNDERSTANDING: ANALYSIS OF RADAR SYSTEMS AND SATELLITE SYSSTEMS; FEATURE EXTRACTION; APPLICATIONS TO ENVIRONMENTAL CONTROL |
| Prerequisites | |
|---|---|
| FOR THE SUCCESSFUL ACHIEVEMENT OF THE OBJECTIVES BASIC METHODOLOGICAL TOOLS IN MATHEMATICS AND STATISTICS ARE REQUIRED, AS WELL AS THE FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING. |
| Contents | |
|---|---|
| Didactic unit 1: Introduction to Remote Sensing and to IDL / ENVI software (Hours of lecture / practice / laboratory 6/0/2) -1 (2 hours lecture): Presentation of the course. Introduction to remote sensing, general concepts on the electromagnetic spectrum. -2 (2 hours lecture): General concepts: active and passive sensors. Land and aerial platforms. Satellite platforms: orbital parameters -3 (2 hours lecture): General concepts: sensors and their spatial, spectral and temporal resolution. Some examples of sensors currently in orbit. Ground stations. -4 (2 hours laboratory): IDL Basics: data types, loops, conditional instructions, functions, scripts. Knowledge and understanding Basic concepts of remote sensing and introduction to the peculiarities of remote sensing data and their applications. Application knowledge and understanding Introductory concepts of the IDL software environment and first tools for the manipulation of remote sensing data. Didactic unit 2: Passive Remote Sensing (8/0/6) -5 (2 hours lecture): Radiation Models -6 (2 hours lecture): Passive Sensors: Spatial and radiometric resolution. -7 (2 hours lecture): Passive Sensors: Spectral and temporal resolution. PSF and MTF function. -8 (2 hours laboratory): IDL: opening of remote sensing images in IDL and their visualization. "Formatted" and non-formatted files, HDF and GeoTIFF formats, outline of graphic functions. -9 (2 hours lecture): PSF component functions. Amplification, sampling and quantization of the signal. Geometric distortions. -10 (2 hours laboratory): Introduction to ENVI. Visualization and interpretation of multispectral data in ENVI. Use of "custom function" in ENVI. -11 (2 hours laboratory): Reslehm ground station: HW and SW architecture, main operational tips and received data. Knowledge and understanding Basic concepts related to radiation models. Operation of passive sensors and information content of the data remotely sensed by them. Application knowledge and understanding First tools for visualizing and interpreting remote sensing data. Key elements for understanding the practical operations of a Ground Station. Didactic unit 3: Processing of Remote Sensing Images by Passive Sensors (12/0/8) -12 (2 hours lecture): Pre-processing of remotely sensed images: introduction. Noise reduction. Radiometric and geometric corrections. Hints to other techniques (e.g. mosaicking). -13 (2 hours lecture): Image Enhancement: introduction. Contrast stretching. Spatial filtering. -14 (2 hours lecture): Multi-spectral image processing. Spectral ratioing. Modulation Ratio. NDVI, NDWI and NDSI indices. -15 (2 hours laboratory): Use of image enhancement techniques in ENVI. Use of the "band math functions" in ENVI: calculation of the NDVI, NDWI and NDSI indices in ENVI. -16 (2 hours lecture): Techniques for dimensionality reduction: PCA. Color spaces: basics and IHS transformation. -17 (2 hours lecture): Classification for remote sensing. Spectral and semantic classes. Thematic maps. SAM classification technique. Supervised classification: identification of the most suitable datasets for training. Separability analysis: basics. Unsupervised classification: basics. "Soft" classifiers: introduction to "unmixing" techniques. -18 (2 hours laboratory): Classification in ENVI: definition of ROIs and use of the most popular supervised algorithms. -19 (2 hours lecture): Heterogeneous Data Fusion: an introduction. Pansharpening: CS and MRA algorithms. Multi-temporal data fusions: some hints. -20 (2 hours laboratory): Data fusion: introduction to Pansharpening toolboxes in Matlab. -21 (2 hours laboratory): Data fusion: comprehension and use of CS and MRA algorithms in Matlab. Knowledge and understanding Techniques and methods for processing remote sensing data, with particular attention to multispectral images. Understanding issues related to classification techniques when applied to remote sensing images. Motivations and methods for data fusion. Application knowledge and understanding Use of tools for image enhancement, spectral ratioing and classification in ENVI. Understanding and use of data fusion algorithms. Didactic unit 4: Active remote sensing: foundations (4/0/2) -22 (2 hours lecture): Microwave sensors. Radar Fundamentals. SAR Fundamentals. -23 (2 hours lecture): SAR images processing: basics. Interpretation of SAR images: geometric distortions. SAR interferometry: introduction and applications. -24 (2 hours laboratory): Processing of SAR data in IDL. Analysis of a pair of interferometric images in IDL, generation of the interferogram and removal of the flat earth contribution. The "Phase unwrapping" problem: a short introduction. Knowledge and understanding Introduction to SAR sensors and to the applications of SAR interferometry. Application knowledge and understanding Ability to visualize and understand SAR data and interferometric data. TOTAL HOURS LECTURE / PRACTICE / LABORATORY 30/0/18 |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES THORETICAL LECTURES AND CLASSROOM EXERCISES. SOME COMPUTER ASSISTED EXERCISES ARE DEVOTED TO LANGUAGES FOR IMAGE PROCESSING (IDL, ENVI). LABORATORY PRACTICES ARE DEVELOPED IN A GROUND STATION (RESLEHM CENTER) |
| Verification of learning | |
|---|---|
| THE GOAL OF THE FINAL EXAM IS, FIRST, THE EVALUATION OF THE KNOWLEDGE AND UNDERSTANDING OF THE CONCEPTS PRESENTED IN THE COURSE AND, SECOND, THE PERSONAL JUDGEMENT, THE COMMUNICATION SKILLS, AND THE LEARNING ABILITIES. THE FINAL EXAM CONSISTS OF A DISCUSSION ON A PROJECT WORK (MAY BE A GROUP WORK) AND AN ORAL INTERVIEW: THE DISCUSSSION ABOUT THE PROJECT WORK IS AIMED TO ASCERTAIN THE CAPACITY TO APPLY THE SIGNAL AND IMAGE PROCESSING METHODS PRESENTED IN THE COURSE TO SOME TYPICAL REMOTE SENSING PROBLEM. THE ORAL INTERVIEW AIMS TO ASSESS THE ACQUIRED KNOWLEDGE ALSO ON THE TOPICS NOT COVERED BY THE PROJECT. THE ORAL EXPOSITION AND THE MATHEMATICAL ARGUMENTS ARE CREDITED WITH HIGHER SCORES. CONCERNING THE FINAL SCORE, EXPRESSED OUT OF THIRTY, THE PROJECT CONTRIBUTES FOR 50% WHILE THE ORAL INTERVIEW FOR 50%. FULL MARKS WITH DISTINCTION MAY BE GIVEN TO STUDENTS WHO DEMONSTRATE THAT THEY CAN APPLY THE ACQUIRED KNOWLEDGE WITH CONSIDERABLE AUTONOMY TO EXERCISES AND THEORETICAL ISSUES. IN CASE OF NO PASS, THE PROJECT WORK CAN BE SAVED TOWARD THE REPETITION OF THE EXAM. |
| Texts | |
|---|---|
| T.M. LILLESAND, R.W.KIEFER, J.W. CHIPMAN: REMOTE SENSING AND IMAGE INTERPRETATION, 7TH ED., J. WILEY, 2015 R.A. SCHOWENGERDT: REMOTE SENSING: MODELS AND METHODS FOR IMAGE PROCESSING. ELSEVIER, 2006 A.K. JAIN: FUNDAMENTALS OF DIGITAL SIGNAL PROCESSING, PRENTICE HALL, 1989 C. OLIVER- S. QUEGAN: UNDERSTANDING SYNTHETIC APERTURE RADAR IMAGES, ARTECH HOUSE, 1998 SUPPLEMENTARY TEACHING MATERIAL WILL BE AVAILABLE ON THE ON THE UNIVERSITY E-LEARNING PLATFORM (HTTP://ELEARNING.UNISA.IT) ACCESSIBLE TO STUDENTS USING THEIR OWN UNIVERSITY CREDENTIALS. |
| More Information | |
|---|---|
| THE COURSE IS HELD IN ENGLISH. |
BETA VERSION Data source ESSE3
