Remote Sensing | Introductory Exercise Course

Remote Sensing &
Image Processing

Exercise course [UE] - 655.352

Assoc-Prof Dr Stefan LANG

title2 start

Welcome to this course!


This is an introductory exercise course to remote sensing. The course material has been developed using tools, concepts and guidelines under the framework of the EO4GEO project. Unless stated otherwise, all rights for figures and additional material are with the author(s).

(c) Z_GIS (S Lang) | 2020

You can navigate through vertical slides by pressing the Down / Up keys on your keyboard or the navigation arrows at the bottom of each slide.

Table of content (ToC)

1Organisation and introduction
2Image repositories and data access
3Specifics of image data
4Visualising and exploring image data
5Spatial referencing
6Radiometric correction
7Image pre-processing
8Image classification
9Validation (accuracy assessment)
10Publishing results

01 | Organisation and introduction

U1 overview image
Lesson-#Content# of topics
01.1Course overview and syllabus7
01.2Earth observation in practice6
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02 | Image repositories and catalogues

U2 overview image
Lesson-#Content# of topics
02.1Space and ground infrastructure6
02.2Data access6
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03 | Specifics of image data

U3 overview image
Lesson-#Content# of topics
03.1Image data model7
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04 | Visualising and exploring image data

U4 overview image
Lesson-#Content# of topics
04.1Sensors and metadata2
04.2Image handling3
04.3Band combination3
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05 | Spatial referencing

U5 overview image
Lesson-#Content# of topics
05.1Spatial vs. geometric correction6
05.2GCP collection and referencing5
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06 | Radiometric correction

U6 overview image
Lesson-#Content# of topics
06.1Radiometric measurement3
06.2Atmospheric correction2
06.3Topographic correction3
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07 | Image pre-processing

U7 overview image
Lesson-#Content# of topics
07.1Band maths3
07.3Image fusion and PCA3
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08 | Image classification

U8 overview image
08.1Multi-spectral classification5
08.2Supervised classification5

09 | Validation (accuracy assessment)

U9 overview image
09.1Ground reference and error matrix4
09.2Product validation3
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10 | Publishing results

U10 overview image
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01 | Organisation and introduction

Remote Sensing & Image Processing

U1 image
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01.1 | Course overview and structure

Learning objectives [lesson 1.1]

  • Understand the structure and content of the course, as well as its requiements and objectives

  • Get an overview of the software environment and datasets being used

01.1 | Course overview and structure

Content and topics [lesson 1.1]

iOverview course structure and learning objectives
iiExperience and prior knowledge
iiiRoad map and syllabus
ivWorkload, assignments and grading
vLiterature and reference material
viSoftware and data being used

01.1 (i) Course structure and learning objectives

The course is structured in the following way:

  • Units

  • Lessons

  • Topics

Learning objectives are specified on the level of lessons, meaning there is one learning objective (or more related ones) provided for each lesson.

Altogether, there are 10 units with 2-4 lessons each. Around one-hundred topics are covered by this course. Each topic is linked, when applicable, with a concept from the EO4GEO Body of Knowledge (BoK) of the EO*GI sector in the following form:

#ContentBoK concept
Topic numberTopicPermalink to BoK

01.1 (ii) Experience and prior knowledge

The course relies on general interest and prior knowledge in remote sensing. While not a formal prerequisite, it is recommended to having completed or being enrolled in parallel to the introductory lecture course “Remote Sensing and Image Processing” (VL 655.351).

This course conveys specific technical skills. If you want to familiarize even better with the software environment, you may enroll to "Introduction to GIS" (UE 655.332).

General skills are acquired according to Bloom's taxonomy of skills levels (see below, @Assignments).

01.1 (iii) Course schedule and syllabus

Time and place

This course takes place Wednesdays, 16.00 - 18.00 in the GI Lab. Press "show schedule" for more details on the current syllabus and schedule for this semester. Updates or any occuring changes you may find in PlusOnline.

Time plan
Depending on the Covid-19 regulations, the course is offered in hybrid or online mode.

01.1 (iv) Workload, assignments and grading


UE = "continuous assessment course". What does that mean? The course implies mandatory presence, active participation, and submission of all assignments; on the other hand, no final examination takes place!

The course consists of 7-8 present dates with 30 min theoretical background and revision assignments; 60 min joint practical exercise including discussion of relevant concepts. Tutorials are offered 2-3 times per semester.

2 ECTS credits = 50 h student workload (~ 1/3 course, 2/3 homework)

01.1 (iv cont.) Workload, assignments and grading

Assignments (A1 … A5)

  • Assignments will be announced in the respective class
  • ... need to be delivered in time to receive feedback (watch your calendar in BlackBoard)
  • ... one attempt at a time

Please obey the following naming convention:
A[n]_[lastname], e.g. A1_Johnson. Please use PFD format exclusively!

Grading: < 50% = insufficient |...| > 85% = very good. Grade distribution:


01.1 (iv cont.) Workload, assignments and grading

Assignments (A1 … A5)

Assignments and the respective learning outcomes follow the Bloom's taxonomy of skills levels (Anderson 2001). That means skills acquired in the assignments range from basic (remember) in the earlier, to advanced (create) in the later, assignments.

Bloom's taxonomy

01.1 (v) Literature and reference material

Selected text books are available in the joint library (Material Science building, Booth #32)

RS books

Please see the following table of recommended books!

01.1 (vi) Software and data sets used

  • Satellite data
    • All data are located in the MyFiles share (see Blackboard)
    • Data are real data, no ‘fake’ or fabricated data
    • NB: data may be subject to license agreements restricting their usage to research or educational purposes. Copying or using those data for any other purpose is prohibit.
  • Software: ArcGIS Pro by ESRI (Image Analysis components)
    • It is easy to use, convenient look & feel
    • Workflows are pre-defined and guided (‚one-click‘)
    • You can stay within one environment (no change of software, transfer of data, etc.)
    • But: ArcGIS Pro is a commercial software package
Software and data

01.1 (vii) Tutorial

Tutor: Christina Zorenböhmer

Assignments and tutorials will give you the opportunity to get some hands-on experience with the platforms, software, and tools used in remote sensing.
Assignment 1 on image acquisition and online platforms will be handed out next week!

EO Browser
A1: We will be exploring different platforms for satellite image acquisition, e.g. Sentinel Hub.

01.2 | Earth observation in practice

Learning objectives [lesson 1.2]

  • Get to know the role of remote sensing in Earth observation and Copernicus
  • Familiarize with the practical dimension of satellite Earth observation by examples
  • Learn the principle of the image analysis workflow and image processing chain
  • Understand the difference between pixel- and object (or region-) based approach

01.2 | Earth observation in practice

Content and topics [lesson 1.2]

#ContentBoK concept
iSatellite remote sensingRemote Sensing Platforms and Systems
iiWhat is Copernicus?Copernicus Programme
iiiEO @ Z_GISn/a
ivImage processing chainImage Processing
vPixel- vs. object-based approachObject-based image analysis (OBIA)

01.2 (i) Satellite remote sensing

"From time immemorial people have used vantage points high above the landscape to view the terrain below. From these lookouts they could get a ‘bird’s eye view’ of the region. They could study the landscape and interpret what they saw. The advantage of collecting information about the landscape from a distance was recognized long ago. Remote sensing as we know it today is the technique of collecting information from a distance. By convention, ‘from a distance’ is generally considered to be large relative to what a person can reach out and touch, hundreds of meters, hundreds of kilometers, or more. The data collected from a distance are termed remotely sensed data." (Stan Aronoff, 1989)
Fig. 012_1
Our ancestors practiced remote sensing from hills or trees (© XXX)

01.2 (i cont.) Satellite remote sensing and Earth observation

Earth observation is one out of three main space assets.

  • Satellite navigation
  • Satellite communication
  • Satellite / Earth observation (EO)
Fig. 012_2
The three components of space assets. Combining (at least) two out of three is termed Integrated applications by ESA

01.2 (1 cont.) Satellite navigation

The European satellite navigation (SatNav) programme is called Galileo. With EGNOS, the European differential global positioning system, it belongs to the world spanning global navigation satellite systems (GNSS).

Galileo-enabled smartphone devices can be used in many different applications.

01.2 (1 cont.) Satellite communication

Communication via satellites (SatCom) allows telecommunication all around the globe. Communication satellites receive signals from Earth and retransmit them by a transponder.

The satellite as a self-contained communications system: the example of IntelSat.

01.2 (1 cont.) Earth observation

Earth observation (EO) is the systematic usage of satellite remote sensing technology for continuous or on-demand imaging or measuring of the Earth surface

A view from the Cupola, the observation deck of the International Space Station (ISS). Photograph taken by German astronaut A. Geerst.

01.2 (ii) What is Copernicus?

Copernicus is the conjoint Earth observation programme by the European Space Agency (ESA) and the European Commission (EC) as laid out in the European Space Policy.
Copernicus logo

Copernicus uses satellite technology to support monitoring the state of our environment and our social well-being. Here are some of the main aspects observed by Copernicus (see lesson 2.2):

  • the conditions of the atmosphere or our land and marine ecosystems
  • the use of the land for forestry and agriculture
  • the process of urbanisation
  • the likely climate change impacts
  • the natural and man-made disasters
  • our human security and public health

01.2 (ii cont.) What is Copernicus?

Fig. 012_6
New EO data, depicted by the database symbol on the left-hand side of the seesaw, make 'Mr or Mrs Copernicus' better positioned for their observations. Thus, EO data leverage a better overview and understanding (© concept: S. Lang, drawing: A. Traun, 2016).

01.2 (ii cont.) Copernicus idea

Copernicus comprises three major components.

  • Space segment
  • Integrated ground segment
  • Data access and information services

(© XXX)

01.3 (iii) EO @ Z_GIS



01.4 (iv) Image processing chain



01.5 (v) Pixel- vs. object-based approach



02 | Image repositories and data access

Image Processing & Analysis

U2 image
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02.1 | Space and ground infrastructure

Learning objectives [lesson 2.1]

  • Get an overview of different types of satellite sensors and platforms

  • Compare two flagship satellite programmes (Landsat vs. Sentinel)

  • Conceptualise the term big EO data

  • Understand the principle of data acquisition and retrieval

  • Get to know the Copernicus data and information access service (DIAS)

02.1 | Space and ground infrastructure

Content and topics [lesson 2.1]

#ContentBoK concept
iSpace segment - Earth observation satellitesPlatforms and sensors
iiExamples: NASA Landsat & Copernicus Sentinel*
iiiBig EO data*
ivIntegrated ground segmentNext-generation SDIs
vData and information access service (DIAS)*
vi(Semantic) content-based image retrievalSemantic enrichment

02.1 (i) Space segment - EO satellites

EO for societal (and environmental) benefit: GEO/SS Many initiatives are in place to promote the peaceful use of EO satellites. The intergovernmental Group on Earth Observations (GEO) is a strong driver behind the integration of various kinds of EO systems globally into a system of systems (GEOSS). Copernicus is the European contribution to GEO.
EO for sustainable development: EO4SDGEO (and GNSS) is crucial in supporting the achievement of the development goals (SDGs) as recognised by the UN. UNOOSA provides an overview on the potential of space in supporting the SDGs.

Fig. 021_1
The UN-SPIDER knowledge portal, EO for SDGs, GEO/SS

02.1 (i) Space segment - EO satellites

Copernicus is like a stage where different actors with their favourite instruments play together in a band.

Fig. 021_1b
Copernicus stage with its instruments - a galery of artistic satellite portraits (compilation: S Lang). An allegoric music video you can find here: Bell' Earth

02.1 (ii) Examples: NASA Landsat & Copernicus Sentinel

Landsat-8 is the flagship of the long-lasting Landsat programme by NASA and USGS in the United States. The onboard instruments (sensors) of Landsat-8 are called OLI (optical) and TIRS (thermal). The Sentinel family is the backbone of the Copernicus EO space asset with Sentinel-2 carrying the optical instrument MSI.

Fig. 021_2
Sentinel-2 vs. Landsat-8. A good overview can be found at the Airbus global monitoring page.

02.1 (iii) Big EO data

The 4 "Vs" (volume, velocity, veracity, variety) usually describe the big data paradigm. A 5th V ('value') in EO asummes the investment in Space infrastructure stimulates an emerging market in the downstream sector.

Fig. 021_3
The 4+1 "Vs" describing the new paradigm of 'big EO data' as discussed by Sudmans et al. (2018).

02.1 (iii cont.) Big EO data

The Copernicus space infrastructure programme is mainly represented by a suite of satellites called the 'Sentinels'.

Fig. 021_3b
Deployment schedule of the Copernicus Sentinel family (as by 2015)

02.1 (iv) Integrated ground segment

The Copernicus integrated ground segment (IGS) includes

  • Receiving stations
  • Satellite data storage systems
  • Information access services

Fig. 021_4
An integrated view of Copernicus and the related ground segment including existing data infrastructures, DIAS, and the user uptake segment (focus: knowledge & skills). Links to the mentioned projects: CopHub.AC | EO4GEO.

02.1 (v) Data and information access service (DIAS)

Fig. 021_5
The five DIAS online platforms allow users to discover, manipulate, process and download Copernicus data and information. All DIAS platforms provide access to Copernicus Sentinel data, as well as to the information products from Copernicus’ six operational services. Examples: CREODIAS (left) | ONDA DIAS (right)

02.1 (vi) (Semantic) content-based image retrieval

How to find suitable imagery in a data catalogue (see lesson 2.2)? Consider the following scenario.

A public authority needs to report on water quality of large swimming lakes (could be in Austria, Switzerland, northern Italy, southern Germany, etc.) during high season over the past three years. For this purpose a service provider (EO company) is contracted to deliver value-adding information products based on satellite images. The EO expert in the company needs to search for a suitable set of images. (More Copernicus use cases you can find here.)
The expert may consider the following search criteria: (1) time window, (2) geographical focus (AOI), (3) cloud cover, (4) thematic focus, (5) monitoring period, more specifically:

  • Time window, e.g. seasonal coverage, as well as geographical location can be derived from standard metadata.
  • The cloud cover can be an retrieved from image wide (i.e. ‘global’) meta-data as well, but it does make a difference a. how well clouds are detected (i.e. which algorithm is applied) and b. whether a 5% cloud coverage just hovers over the lake(s).
  • The thematic focus requires the operator to apply a general (i.e. low level semantic) concept of the presence and imaged representation of (swimming) lakes. Ideally, this information is extracted automatically and added to the images's metadata (e.g, 'Existence.Lake = 1')
  • The assessment over time (constancy or change of quality) implies the notion for changes in critical indicators, e.g. revealed by indices utilizing infrared reflectance recorded by the multi-temporal imagery.
Fig. 021_6

02.2 | Data & information access

Learning objectives [lesson 2.2]

  • Get to know different sources and options for data search via free platforms

  • Understand how to search and acquire VHR satellite imagery ('tasking) from commercial data providers

  • Learn how to access information layers from Copernicus services

02.2 | Data & information access

Content and topics [lesson 2.2]

#ContentBoK concept
iEarth explorer (USGS)*
iiSentinel hub and playground*
iiiCommercial VHR data portals *
ivGoogle Earth Engine *
vProba-V mission exploitation platform*
viCopernicus service platforms*

02.2 (i) Earth explorer (USGS)

The USGS Earth Explorer contains the full Landsat repository and data from other NASA missions as well as the Sentinel-2 archive. Click image (2 steps) or here for further instructions - searching for locations and download requires registration

02.2 (ii) Sentinel hub and playground

sentinel playground
The so-called Sentinel playground that allows for real-time data browsing and embedding of OGC services in GIS applications.

02.2 (ii cont.) Copernicus Open Access Hub

sentinel hub
The Copernicus Open Access Hub ...

02.2 (iii) Commercial VHR data portals

commercial portals
The DigitalGlobe ImageFinder (left) provides the data archive for Worldview-2 and other VHR missions. The Airbus Space & Defence GeoStore (right) offers Pléiades images. Click image (2 steps) or here for further instructions

02.2 (iii cont.) Commercial VHR data portals

A general note on pricing schemes

Freely available data (Landsat, Sentinel, etc.) involve billions of public investment. Commercial satellites are run by private companies that seek for return of investment.

Cost model factors include:
  • Spatial resolution (VHR data are usually commercial)
  • Versatility / flexibility (revisiting time)
  • Archive vs. tasking
  • Image quality (cloud coverage, incidence angle)
  • Window of acquisition / delivery mode

An example of Airbus / Pléiades is shown on the right
Fig. 022_4b

02.2 (iv) Google Earth Engine

Fig. 022_4
Google Earth Engine (GEE) is the flagship in cloud-based EO data processing. Over recent years, it has revolutionized the way how satellite data are being processed, meaning it has shifted the paradigm of desktop image processing to cloud solution. The image repository is enourmous and steadily growing and together with a toolbox (such as TimeLapse) and the palette of Python-based tools and scripts allows for an inspiring and easy-to-use data processing globally. NB: getting results out of the system is not as straight-forward.

02.2 (v) Proba-V mission exploitation platform

Fig. 022_5
The Proba-V mission exploitation platform (MEP) is a mission-specific data access platform for the exploratory satellite Proba-V (V = Vegetation). Orginially designed for data access, it evolved to a widely usable cloud infrastructured for time-series analysis and spatially-constraint (i.e. polygon-based) paramater calculations, such as NDVI per country. Next to Proba-V also Sentinel-2 data are accessible. Pre-fabricated Jupyter notebooks are available for customized use.

02.2 (vi) Copernicus service platforms

The fundamental objective of Copernicus is to support policies, regulations, conventions, and directives with a defined portfolio geospatial information services in the following areas:
Fig. 022_6
The overall Copernicus structure including data and services (© XXX, Copyright Info)

02.2 (v) cont. Copernicus service platforms

Copernicus services

There are six so-called Copernicus [core] services in six major thematic areas.

  • Land monitoring
  • Ocean monitoring
  • Atmosphere monitoring
  • Climate change
  • Emergency management
  • Human security
Fig. 022_6b
(© XXX, Copyright Info)

Land monitoring

The Copernicus Land Monitoring service (CLMS) aims at monitoring the Earth.

Fig. 022_6s1
The four land monitoring service components (© XXX, Copyright Info)

Ocean monitoring

The Copernicus Marine Environment Monitoring service (CMEMS) focuses on the global ocean.

Fig. 022_6s2
The marine monitoring service portfolio (© XXX, Copyright Info)

Atmosphere monitoring

The Copernicus Marine Environment Monitoring service (CAMS) focuses on the global ocean.

Fig. 022_6s3
The atmosphere monitoring service portfolio (© XXX, Copyright Info)

Climate change

The Copernicus Climate Change service (C3S)

Fig. 022_6s4
The C3S key products and services. See also the Climate Adapt platform of the European Environment Agency (EEA)

Emergency management

The Copernicus Emergency Management service (CEMS)

Fig. 022_6s5
The emergency management service portfolio (© XXX, Copyright Info)

Human security

The Copernicus Security service contains of three subservices, (1) Maritime surveillance, (2) Border surveillance and the (3) support to European External Action (SEA)

Fig. 022_6s6
The security SEA service (© XXX, Copyright Info)

The Copernicus ecosystem

Copernicus engages different actor groups.

  • Data providers
  • Service providers
  • Users and stakeholders
Fig. 022_5c
A subset of the Copernicus ecosystem

Data providers

Next to the Sentinel satellites there is a series of contributing missions, including commercial providers of very high (spatial) resolution (VHR) data.

Fig. 022_5d

Copernicus contributing missions - the example of Worldview-2 (© XXX, Copyright Info)

Include the example of Planet and Skysat (completion of constellation such that every location on Earth is visible by 7 satellites)

Service providers


Users and stakeholders


03 | Specifics of image data

Image Processing & Analysis

U3 image
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03.1 | Image data model

Learning objectives [lesson 3.1]

<<<<<<< HEAD

  • Understand the specifics of image data as a special case of raster data

  • Familiarize with top-view characteristics of image data as well as the difference between panchromatic and multispectral images

  • Learn to distinguish between different resolution types

  • Learn how to use spectral profiles as an analysis tool

  • Understand different image storage strategies

  • Understand the specifics of image data as a special case of raster data

  • Familiarize with top-view characteristics of image data as well as the difference between panchromatic and multispectral images

  • Learn to distinguish between different resolution types

  • Learn how to use spectral profiles as an analysis tool

  • Understand different image storage strategies
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03.1 | Image data model

Content and topics [lesson 3.1]

#ContentBoK concept
iWhat are image data?Properties of digital imagery
iiVector vs. raster data representationThe raster model
iiiResolution typesSpectral resolution
ivImage matrix and image regions*
vPanchromatic vs. multi-band images, feature spaceRadiometric resolution
viSpectral profiles*
viiStorage optionsData storage

03.1 (i) What are image data?

Image data are a specific type of raster data representing a subset of the Earth's surface by recording the continous phenomenon of radiation reflectance. Image data are captured by sensors mounted on different platforms, such as ballons, drones, aircrafts, and satellites.

Fig. 031_1a
From oblique view to top-view (© Photos: S. Lang)

03.1 (ii) Vector vs. raster data representation

Vector data are used to represent spatial discreta, while raster data are the appropriate data model for spatial continua.

Fig. 031_2
Object-based image analysis (OBIA) has been developed to bridge between continous observations and object representations.

03.1 (iii) Resolution types

There are four different resolution types: spectral, spatial, radiometric, temporal resolution

Fig. 031_3
Pléiades image of the city of Salzburg with 0.5m spatial resolution.

03.1 (iv) Image matrix and image regions

An image matrix (or pixel array) is defined by (i) coordinates of origin, (ii) resolution (pixel size), (iii) extent (dimension)

Fig. 031_4
Image matrix as defined by origin, resolution, extent (left). Image regions as defined by pixel similarity (right)

03.1 (v) Panchromatic vs. multi-band images, feature space

Satellite imagery consists of several co-registered pixel arrays representing a combination of different spectral bands.

Fig. 031_5
The visual effect of combining different spectral bands can be interpreted by humans or machines in order to derive semantic information (e.g. land cover classes).

03.1 (vi) Spectral profiles

Spectral profiles help determine the spectral properties of a given pixel (i.e., at a specific location)

Fig 031_6
A spectral profile of a selected pixel showing the DNs of the sequential Landsat-8 bands (© XXX, Copyright Info)

03.1 (vii) Storage options

There are different options for storing multi-band images, band-interleaved by line (BIL), band-interleaved by pixel (BIP), and band sequential (BSQ)

Fig 031_7
Different storage options BIL, BIP, and BSQ (© XXX, Copyright Info)

03.2 | Histograms

Learning objectives [lesson 3.2]

  • Understand the frequency distribution of pixels in images

  • Learn how to generate and to read histograms

03.2 | Histograms

Content and topics [lesson 3.2]

#ContentBoK concept
iValue frequency distribution*
iiInterpretation of histogramsHistograms
iiiHistogram generation*

03.2 (i) Value frequency distribution


What is a histogram? A histogram is a statistical chart technique providing a compact overview of the data in an image than knowing the exact value of every pixel.

(© XXX, Copyright Info)

03.2 (ii) Interpretation of histograms

How to interpret different types histograms? (© XXX, Copyright Info)

03.2 (iii) Histogram generation

Interactive excercise

histogram Jupyter

Here you find a interactive Jupyter notebook on how to convert a sequence of digital numbers stored in a BIL format into a histogram

04 | Visualising and exploring image data

Image Processing & Analysis

U4 image
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04.1 | Sensors and metadata

Learning objectives [lesson 4.1]

  • Learn what typical metadata are produced by satellite imagery
  • Understand how metadata of image collections (e.g. Sentinel-2) can be used to better understand the availabilty and quality of satellite data

04.1 | Sensors and metadata

Content and topics [lesson 4.1]

#ContentBoK concept
iSensor characteristics (Landsat/Sentinel-2 vs. Worldview-2)*
iiSatellite image metadata analysis (webtool EO Compass)*

04.1 (i) Sensor characteristics (Landsat/Sentinel-2 vs. Worldview-2)

Typical features of HR vs. VHR sensors

Using the examples of two representatives of the high-resolution (HR) optical satellites family (Landsat-8 & Sentinel-2) and a VHR satellite (Worldview-2)

Fig. 041_1
Sensor characteristcs Sentinel-2 vs. Landsat-8. See link.

04.1 (ii) Satellite image metadata analysis

The EO Compass

A stand-alone webtool to understand the global coverage and quality of Sentinel-2 image acquisition based on image metadata analysis.

Fig. 041_2
EO Compass (Sudmanns et al., 2019) | © Z_GIS

04.2 | Image handling

Learning objectives [lesson 4.2]

  • Practice the handling of image data in dedicated software environment
  • Learn how to use the ArcGIS Pro Image Analysis module

04.2 | Image handling

Content and topics [lesson 4.2]

#ContentBoK concept
iArcGIS Pro Image Analysis module*
iiOrganising and loading image data*

04.2 i ArcGIS Pro Image Analysis module

Visualising images in ArcGIS Pro

Fig. 042_1
Use the navigation pane to control interaction with image (left). The Swipe function allows to visualise and explore several images simultanously (right).

04.2 iii Organising and loading image data

Visualising images in ArcGIS Pro

Fig. 042_2 Open multi-band image via container file (left). Make background transparent (right).

04.3 | Band combinations

Learning objectives [lesson 4.3]

  • Understand how to visualise, manipulate, and interact with, image data
  • Learn what general principles apply when interpretating them (including change of band combinations)

04.3 | Band combinations

Content and topics [lesson 4.3]

#ContentBoK concept
iVisualising image dataLayer stack
iiRange adjustment and resampling for viewingRaster resampling
iiiBand combinations (incl ArcGIS Pro presets)Visual interpretation

04.3 (i) Visualising image data

What do the colours mean?

Going back to the starting slide of this unit, we can see a scene which is dominated by red tones.

Fig. 043_1
Different band combinations are used for specific analytical purposes (© XXX, Copyright Info)

04.3 (iii) Range adjustments


Fig. 043_3

04.3 (iii) Band combinations (incl ArcGIS Pro presets)

Visualising visible and non-visible spectral ranges

Fig. 043_3

05 | Spatial referencing

Image Processing & Analysis

U5 image
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05.1 | Spatial referencing

Learning objectives [lesson 5.1]

  • Understand the general rationale for spatial referencing and geometric correction
  • Learn the purpose of orthorectification and georeferencing
  • Understand geodetic and geometric principles as well as the process of (mathematical) transformation and (technical) resampling

05.1 | Spatial referencing

Content and topics [lesson 5.1]

#ContentBoK concept
iRationale of spatial referencingSpatial referencing
iiGeoreferencing vs. geometric correction*
iiiHow to obtain real-world coordinates*
ivOrthorectification via RPCOrthorectification
vGeodetic principles, projections [recap]Map projections
viTransformations & resamplingCoordinate transformations

05.1 (i) Rationale of spatial referencing

The purpose is to transform image data into a spatial reference frame. The process is carried out according to geodetic principles like the geodetic datum, projections, etc.

There are several ways to 'add' spatial coordinates to an image. This process also compensates for distortion effects that occur during image capturing (applies in particular to ortho-rectification). Usually, images obtained through one of the discussed image portals, are pre-registered or even 'ortho-ready'.

Registration is mandatory if one or more of the following conditions apply: (1) data sets from different sensors or time slots to be integrated (e.g., for time series / monitoring); (2) image scenes to be merged with adjacent scenes (mosaicing); (3) image data to be combined with existing geodata (e.g., background map); (4) image data to be transformed to another reference system (re-projection); (5) Size and distance measurements to be taken

05.1 (ii) Georeferencing vs. geometric correction

Geometric correction is a 'super-concept' comprising georeferencing (external orientation) and ortho-rectification (internal orientation, depending on camera or sensor calibration). The latter is of particular importance for VHR satellite images or air photos.

Resampling is the actual process of re-calculating an image matrix during geometric correction.

screenshot taken from EO4GEO BoK in the EO*GI sector BoK visualization and search Tool

05.1 (iii) How to obtain real-world coordinates?

You can obtain real-world coordinates (lat-long [DMS/DD] or metric) from various sources including field visits (GNSS measurements), Google Maps, Online map portals (e.g. Austrian Map), or any other co-registered image.

src coord
Sources of real-world coordinates (© S. Lang, PLUS)

05.1 (iii cont.) How to obtain real-world coordinates?

On-the-fly projection (e.g. in ArcGIS Pro), only if spatial reference information exists.

Image co-registration requires well registered reference image in appropriate resolution

img coreg
Image-to-image coregistration of different sensors (© xxx/ xxx)

05.1 (iii cont.) How to obtain real-world coordinates?

The geometric quality of an image (pre-registration) depends on the purpose. The pre-registered level 1c / 1g products of Landsat-8 or Sentine-2 are usually sufficient for a typical 'Landsat-like' application case.

geom qual
Spatial accuracy of Sentinel-2 and Landsat-8 imagery

05.1 (iv) Orthorectification via RPC

Purpose and procedure VHR Satellite date delivered pre-registered (z.B. UTM-33 / WGS-84) Realized using sensor models (RPC) and differential rectification with DGM Prerequisite: ‘Ortho-Ready‘ products Interpolation of elevation over pixel and re-calculation into image matrix

Components for ortho-rectification (© xxx/ xxx)

05.1 (v) Geodetic principles, projections

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05.1 (vi) Transformation & resampling

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05.2 | GCP collection and referencing

Learning objectives [lesson 5.2]

  • Practice the procedure of GCP collection and spatial referencing in the software environmen
  • Learn how to assess and improve the quality of the result

05.2 | GCP collection and referencing

Content and topics [lesson 5.2]

#ContentBoK concept
iSpatial referencing in ArcGIS Pro*
iiImage co-registrationImage co-registration
ivGCP collectionGround Control Points (GCP)
vCalculate and assess RMS errorRoot mean square error (RMSE)

06 | Radiometric correction

Image Processing & Analysis

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06.1 | Radiometric measurement

Learning objectives [lesson 6.1]

  • Understand the concept of radiometric resolution
  • Differentiate the terms reflectance and radiance, and understand the concept of image calibration

06.1 | Radiometric measurement

Content and topics [lesson 6.1]

#ContentBoK concept
iQuantisation and radiometric resolutionRadiometric resolution
iiImage calibration (radiance vs. reflectance)Radiometric correction
iiiPrinciple of atmospheric correctionAtmospheric correction

06.1 (i) Quantisation and radiometric resolution

The principle of quantisation is the transformation of a continous brightness range according to a specfied sampling distance, the so-called ground sample distance (GSD). The radiometric resolution corresponds to the number of quantisation levels.

In an 8-bit image we have digital numbers ranging from 0 ... 255. This corresponds to (2^8=) 256 quantisation levels and a radiometric resolution of 8-bit (see histograms). (© S. Lang, PLUS/xxx)

06.1 (ii) Image calibration (radiance vs. reflectance)

Depending on the radiometric resolution (quantisation levels), image calibration converts digital numbers (DN) into physical units of measurement (spectral radiance)

Image calibration makes image data comparable (© S. Lang, PLUS/xxx)

06.1 (ii cont.) Image calibration (radiance vs. reflectance)

The general workflow of radiometric image enhancement contains radiometric calibration and radiometric correction. Radiometric correction can be separated in atmospheric correction and topographic correction.

(© F. Albrecht, Z_GIS/xxx)

06.1 (ii cont.) Image calibration (radiance vs. reflectance)

Image calibration converts digital numbers into radiance at the sensor.

Spectral radiance as measured by a spectroradiometer (© xxx/xxx)

06.1 (iii) Atmospheric correction

Atmospheric correction is a part of radiometric correction compensating for interactions of electromagnetic radiation with gas absorption and aerosol scattering in the atmosphere.

Several steps are involved with the final goal to achieve surface reflectance values (reflectance on the ground). (© xxx/xxx)

06.2 | Atmospheric correction

Learning objectives [lesson 6.2]

  • Understand the purpose of atmospheric image correction to generate TOA or surface reflectance values
  • 06.2 | Atmospheric correction

    Content and topics [lesson 6.2]

    #ContentBoK concept
    iTop-of-atmosphere correctionConverting DN to TOA reflectance
    iiSurface correction*

    06.2 (i) Top-of-atmosphere correction

    Top-of-atmosphere (TOA) correction is the most straight-forward radiometric correction based on orbit parameters and celestal constellation. The latter is approximated by the specific sun-earth distance on the particular Iulian day of image acquisition.

    06.2 (ii) Surface correction

    Surface (SURF) correction is a more advanced correction procedure.

    06.3 | Topographic correction

    Learning objectives [lesson 6.3]

    • Understand the routine of topographic image correction
    • Get an overview of different techniques

    06.3 | Topographic correction

    Content and topics [lesson 6.3]

    #ContentBoK concept
    iWhy topographic correction?Topographic correction
    iiLambertian correction*
    iiiNon-Lambertian correction*

    06.3 (i) Why topographic correction?

    Topographic correction is the process of compensating over- or under-reflectance due to topographic effects and illumination angle. This leads to so-called image 'flattening', because any shading effect etc. disappears.

    06.3 (ii) Lambertian correction

    Lambertian correction ...

    06.3 (iii) Non-Lambertian correction

    Non-Lambertian correction ...

    07 | Image pre-processing

    Image Processing & Analysis

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    07.1 | Band maths

    Learning objectives [lesson 7.1]

    • Understand the purpose of arithmetic band combinations
    • Learn how to interpret index values (such as NDVI, NDSI) and their ranges

    07.1 | Band maths

    Content and topics [lesson 7.1]

    #ContentBoK concept
    iArithmetic combination of band valuesBand maths
    iiSpectral ratioing and indicesSpectral indices
    iiiVegetation indicesNormalized Difference Vegetation Index (NDVI)

    07.2 | Filtering

    Learning objectives [lesson 7.2]

    • Understand the principle of contextual focal analysis of images using convolution kernels
    • Learn how to apply pre-defined filtering routines for dedicated purposes (sharpening, smoothing, edge detection, etc.)
    • Get an idea how filters are used for convolutional neural networks (CNNs)

    07.2 | Filtering

    Content and topics [lesson 7.2]

    #ContentBoK concept
    iPrinciple of filtering*
    iiLow-pass vs. high-pass filters*
    iiiWeighted filters (Gaussian)*
    ivDirectional filters (edge enhancement)*
    vConvolutional neural networks (CNN)*

    07.3 | Image fusion & PCA

    Learning objectives [lesson 7.3]

    • Understand the principle of image fusion
    • Learn how to perform pan-sharpening techniques (e.g. Gram-Schmid)

    07.3 | Image fusion & PCA

    Content and topics [lesson 7.3]

    #ContentBoK concept
    iGeneral idea of image fusionImage fusion
    iiiComputing principle (image) componentsPCA

    08 | Image classification

    Image Processing & Analysis

    U8 image
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    08.1 | Multi-spectral classification

    Learning objectives [lesson 8.1]

    • Understand the purpose of multi-spectral image classification
    • Comprehend the difference between supervised and non-supervised classification
    • Get to know different classifiers and how they work

    08.1 | Multi-spectral classification

    Content and topics [lesson 8.1]

    #ContentBoK concept
    iPurpose of digital image classificationImage understanding
    iiSpectral characteristics of geographic features [recap]Spectral signatures
    iiiMulti-spectral classificationImage classification
    ivSupervised vs. non-supervised classification (clustering)*
    vSample-based vs. knowledge-based classifiers*

    08.1 (i) Purpose of digital image classification

    Why image classification?

    Classification, the conversion of digital numbers into semantic, i.e. meaningful categories (e.g., forest, water, cultivated fields), is the ultimate aim of image analysis. Whenever we look at an image, we instantly ...

    In pixel-based classification each single pixel is assigned to a given categorical class. This is done according its location in a feature space, but irrespective of its spatial location or surrounding. The general workflow of digital image classification includes:

    • ...
    • ...
    • ...
    • ...
    • ...

    08.1 (ii) Spectral characteristics of geographic features


    08.2 | Supervised classification

    Learning objectives [lesson 8.2]

    • Learn how to conduct the image classification workflow in ArcGIS Pro
    • Practice the collection of samples, the creation of a classification scheme and the selection of a suitable classifier

    08.2 | Supervised classification

    Content and topics [lesson 8.2]

    #ContentBoK concept
    iSelection of training areas*
    iiClass separability*
    iiiSampling strategiesSampling strategies
    ivClassification schemesClassification schemes (taxonomies)
    vClassifiers in ArcGIS Pro*

    09 | Quality assessment

    Image Processing & Analysis

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    09.1 | Ground reference & accuracy assessment

    Learning objectives [lesson 9.1]

    • Understand the meaning of accuracy assessment and how it is performed
    • Know what post-process routines exist to improve results

    09.1 | Ground reference & accuracy assessment

    Content and topics [lesson 9.1]

    #ContentBoK concept
    iPost-processing (majority filtering, reclass, merging classes)Map algebra (focal analysis)
    iiMerging classes and reclass*
    iiiGround validation vs. reference layerGround reference
    ivSite-specific accuracy assessmentAccuracy assessment
    vError matrixError matrix

    09.2 | Product validation

    Learning objectives [lesson 9.2]

    • Discuss the quality and usabilty of a classification product (and potentially a map generated out of it)

    09.2 | Product validation

    Content and topics [lesson 9.2]

    #ContentBoK concept
    iParadoxon of validation*
    iiUsability aspects*

    10 | Publishing results

    Image Processing & Analysis

    U10 image
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    10.1 | Geodata integration

    Learning objectives [lesson 10.1]

    • Combine image data and with other layers from geodata infrastructures
    • Understand the importance of proper spatial referencing

    10.1 | Geodata integration

    Content and topics [lesson 10.1]

    #ContentBoK concept
    iData compilationdata integration
    iiHarvest OGC compliant sourcesintegrating data from OGC web services

    10.2 | Map making

    Learning objectives [lesson 10.2]

    • Create a map in the appropriate scale to the image resolution and classification depth

    10.2 | Map making

    Content and topics [lesson 10.2]

    #ContentBoK concept
    iCreate a mapTraditional map making
    iiWeb mappingWeb map making
    *** The End ***