Welcome to this course!

Disclaimer

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)

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

01 | Organisation and introduction

02 | Image repositories and catalogues

03 | Specifics of image data

04 | Visualising and exploring image data

05 | Spatial referencing

06 | Radiometric correction

07 | Image pre-processing

08 | Image classification

09 | Validation (accuracy assessment)

10 | Publishing results

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]

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:

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.

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

Show schedule

01.1 (iv) Workload, assignments and grading

Workload

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)

01.1 (v) Literature and reference material

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

01.1 (vii) Tutorial

Tutor: Christina Zorenböhmer
Email: christina.zorenboehmer@sbg.ac.at

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!

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]

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)

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)

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).

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.

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

01.2 (ii) What is Copernicus?

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?

01.2 (ii cont.) Copernicus idea

Copernicus comprises three major components.

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

01.3 (iii) EO @ Z_GIS

...

01.4 (iv) Image processing chain

...

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

...

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]

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.

02.1 (i) Space segment - EO satellites

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

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.

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.

02.1 (iii cont.) Big EO data

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

02.1 (iv) Integrated ground segment

The Copernicus integrated ground segment (IGS) includes

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

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

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.

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]

02.2 (i) Earth explorer (USGS)

02.2 (ii) Sentinel hub and playground

02.2 (ii cont.) Copernicus Open Access Hub

02.2 (iii) Commercial VHR data portals

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

02.2 (iv) Google Earth Engine

02.2 (v) Proba-V mission exploitation platform

02.2 (vi) Copernicus service platforms

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

Land monitoring

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

Ocean monitoring

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

Atmosphere monitoring

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

Climate change

The Copernicus Climate Change service (C3S)

Emergency management

The Copernicus Emergency Management service (CEMS)

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)

The Copernicus ecosystem

Copernicus engages different actor groups.

• Data providers
• Service providers
• Users and stakeholders

Data providers

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

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

...

03.1 | 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

03.1 | Image data model

Content and topics [lesson 3.1]

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.

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.

03.1 (iii) Resolution types

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

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.

03.1 (vi) Spectral profiles

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

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)

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]

03.2 (i) Value frequency distribution

Histograms

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.

03.2 (ii) Interpretation of histograms

03.2 (iii) Histogram generation

Interactive excercise

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

04.1 | 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]

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)

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.

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]

04.2 i ArcGIS Pro Image Analysis module

Visualising images in ArcGIS Pro

04.2 iii Organising and loading image data

Visualising images in ArcGIS Pro

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]

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.

04.3 (iii) Range adjustments

...

...

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

Visualising visible and non-visible spectral ranges

...

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]

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'.

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.

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.

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

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.

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

05.1 (v) Geodetic principles, projections

[add content here]

05.1 (vi) Transformation & resampling

[add content here]

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]

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]

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.

>

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)

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.

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

Image calibration converts digital numbers into radiance at the sensor.

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.

06.2 | Atmospheric correction

Learning objectives [lesson 6.2]

06.2 | Atmospheric correction

Content and topics [lesson 6.2]

06.2 (i) Top-of-atmosphere correction

06.2 (ii) Surface correction

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]

06.3 (i) Why topographic correction?

06.3 (ii) Lambertian correction

06.3 (iii) Non-Lambertian correction

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]

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]

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]

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]

08.1 (i) Purpose of digital image classification

Why image classification?

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]

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]

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]

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]

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]