Change detection Paul Aplin School of Geography, University of Nottingham, UK Chairman, Remote...

Change detection

Paul Aplin

School of Geography, University of Nottingham, UK

Chairman, Remote Sensing and Photogrammetry Society

4th ISPRS Student Consortium and WG VI/5 Summer School, Warsaw 13-19 July 2009.

Outline

Remote sensing background Multitemporal analysis Preprocessing requirements Change detection Image differencing Further reading Introduction to practical exercise

There are meaningful distinctions between remote sensing ‘platforms’, ‘sensors’ and ‘images’

Platform The craft on which a sensing

device is mounted

Sensor The sensing device or instrument

itself

Image The image data acquired by the

sensing device


Platforms, sensors and images

There are three main categories of remote sensing platforms

Spaceborne- Satellite- Shuttle Ground-based

- Hand-held- Raised platform

Airborne- Aeroplane- Helicopter- Hot air balloon- Air ship- Tethered balloon

Commonestplatforms


Remote sensing platforms

http://uk.youtube.com/watch?v=jGQJ1_6WAMM

Satellite path

Field of view

Ground track (imaged area)

Advantages

Continuous data acquisition Permanent orbit

High geometric accuracy Stable orbit (no atmosphere)

Wide area of coverage High vantage point

Low data cost?

Disadvantages

Geometric distortion Earth curvature

High operation cost Launch, etc.

Low spatial detail? High vantage point

Cloud cover?

High data cost?


Satellite platforms

Generally, remote sensing satellites are in low Earth orbits (LEOs), at altitudes of several hundreds of kilometres

These satellites orbit the Earth approximately every hour

Most remote sensing satellites follow a ‘polar’ orbital path (approximately north-south)

Polar orbits maximise the area of data acquisition, exploiting the Earth’s rotation

Satellite orbit


As the Earth rotates eastwards, the satellite passes North-South (or South-North) acquiring a ‘swath’ of imagery


Polar orbit


As the Earth continues to rotate, another image swath is acquired


Polar orbit


As the Earth continues to rotate, another image swath is acquired

And again…


Polar orbit

Some remote sensing satellites follow a ‘geostationary’ orbital path

This means they constantly view the ‘same’ area of coverage

By orbiting in coincidence with the Earth’s rotation

All geostationary satellites orbit around the Earth’s equator

A common example is meteorological satellites

Other non-remote sensing satellites also use geostationary orbits

E.g., communications


Geostationary orbit

Advantages

High spatial detail Low vantage point

On-demand acquisition Requested flights

Low operation cost?

Avoid cloud cover?

Low data cost?

Disadvantages

Narrow area of coverage? Low vantage point

Sporadic acquisition Occasional flights

Low geometric accuracy Yaw, pitch, roll

High data cost?

Field of view

Ground track (imaged area)

Flight path


Aeroplane platforms

There are various types of sensors or instruments

Any type of sensor can be operated from any remote sensing platform Satellite, aircraft or ground-based

Digital- Sensor- Camera- Video- Radar- LiDAR

Analogue- Camera

Described in lecture


Sensors

The most common types of remote sensing instruments are the digital sensors introduced previously

Hyperspectral sensors are also fairly common

Remote sensing at these parts of the electromagnetic spectrum (visible, infrared) is collectively known as ‘optical’ remote sensing

Panchromatic sensors

Create images comprising one broad spectral waveband

Multispectral sensors

Create images comprising several spectral wavebands


Digital sensors

Incoming solar radiation

Atmospheric distortion

Reflected radiationScattered

radiation

Received radiation

Sensor

Data download

User

Data supply

Sun

Earth’s surface

Ground receiving station

Absorbed/transmitted radiation


Image acquisition

Radar = ‘Radio Detection And Ranging’

Radars are well known as navigational instruments, but also operate as remote sensing instruments

Using ‘microwave’ (long wavelength) part of electromagnetic spectrum

Radar remote sensing is very unlike optical remote sensing…

Radars are ‘active’, generating their own energy source Optical sensors are ‘passive’, using the sun’s reflected energy

Each radar operates at a specific spectral wavelength Optical sensors average reflectance across spectral wavebands

Common radar sensors Synthetic Aperture Radar (SAR) – generally spaceborne Side-Looking Airborne Radar (SLAR) – generally airborne


Radar sensors

Radar instruments are ‘active’ and generate their own energy

Radars emit microwaves and record the ‘return’ from features

Radar images distinguish features on the basisof ‘texture’

Rough surfaces – high return (light image) Smooth surfaces – low return (dark image)

Radars have strong viewing capabilities Penetrate cloud due to long wavelength Operate at night due to own energy source

Emitted microwaves

Some return

Scattered microwaves

Radar

Little/ no return

Forest‘rough’ surfaceWater

‘smooth’ surface

Radar image acquisition


Three main image characteristics are considered here

The term ‘resolution’ is only partly appropriate in some of these cases, but is used for convenience

Spectral resolution

Temporal resolution

Spatial resolution


Remotely sensed images

Strictly speaking, spectral ‘resolution’ refers to the ‘width’ of a spectral waveband used to generate an image band

It is perhaps more useful to consider the ‘number’, ‘position’ and ‘width’ of all spectral wavebands comprising an image

• Spectral image characteristics have been covered in earlier description of electromagnetic spectrum and optical/radar imagery

Typical spectral reflectance curves

Ban

d 1

- b

lue

Ban

d 2

- g

reen

Ban

d 3

red

Ban

d 4

– n

ear

infr

are

d

Ban

d 5

– m

id in

frare

d

Ban

d 7

– m

id in

frare

d

Multispectral image


Spectral resolution

In simple terms, spatial resolution means ‘pixel size’

The concept is simple…

Fine spatial resolution image = fine spatial detailsmall feature identification

Coarse spatial resolution image =coarse spatial detaillarge feature identification

As spatial resolution is degraded progressively, features become increasingly blurred and harder to identify

4 m

8 m

16 m

32 m

64 m

128 m

256 m

512 mSkukuza,Skukuza,Kruger National Park, Kruger National Park, South AfricaSouth Africa


Spatial resolution

Spatial resolution is related to the area of coverage of an image (the ‘swath width’ of the sensor)

Both spatial resolution and swath width may be affected by the altitude of the platform…

High altitude (e.g., satellite) = wide swath = coarse spatial resolution

Low altitude (e.g., aircraft) = narrow swath = fine spatial resolution

However…


Area of coverage

Swath width = 3 kmSpatial resolution = 500 m

No. pixels = 24Storage space = e.g., 6 Mb

Swath width = 6 kmSpatial resolution = 1 km

No. pixels = 24Storage space = e.g., 6 Mb

…altitude is not the only factor that determines spatial resolution and swath width

E.g., certain satellite sensors have fine spatial resolutions and narrow swath widths

Another problem is the limited processing and storage capacity of the instruments

Each image pixel requires a certain amount of storage space, regardless of spatial resolution

For instance, two images… with ‘different’ swath widths/spatial resolutions but the ‘same’ number or pixels (rows, columns) will require the ‘same’ amount of storage space

Therefore, there is a ‘play off’ between spatial resolution and swath width

Fine spatial resolution = narrow swath width Wide swath width = coarse spatial resolution


Data processing and storage

In practical terms, swath widths and spatial resolutions can vary considerably

From images extending across 1,000s kilometres with pixels measuring 10s kilometres…

…through images extending across 100s kilometres with pixels measuring 10s metres…

…to images extending across 100s metres (or less) with pixels measuring 10s centimetres (or less)


Practical examples

• Temporal resolution refers to the frequency of image acquisition at a constant location Other, synonymous terms are ‘repeat cycle’ or ‘revisit time’ This concept is most applicable to satellite sensors, since airborne remote sensing occurs

on an irregular basis

A collection of images over time of a constant location are known as a ‘multitemporal’ image or data set

As with spatial resolution, the concept is simple… Short temporal resolutions enable frequent observation of dynamic features Long temporal resolutions enable occasional observation of long-term change

The chief determinant of temporal resolution is satellite orbit, but also significant are tilting capabilities, latitude and swath width

Certain sensors can ‘tilt’ away from their normal orbital ground track, observing targeted features relatively frequently

‘Polar’ orbits lead to frequent coverage at high latitudes (e.g., polar regions) Wide swath sensors cover large areas, returning relatively frequently to any location

Therefore, by extension, there is also a ‘play off’ between temporal resolution and spatial resolution!


Temporal resolution

In practical terms, temporal resolutions can vary considerably

From every half hour for meteorology sensors… …to weeks or even months for narrow swath sensors The key is to select suitable imagery related to the

feature to be monitored Hurricane FrancesOrbView-2 image

1 Sep 20041 Sep 2004

6 Sep 20046 Sep 2004

5 Sep 20045 Sep 2004 4 Sep 20044 Sep 20043 Sep 20043 Sep 2004 2 Sep 20042 Sep 2004


Monitoring example

September 1972 December 1978 March 1987 June 1990

Multitemporal Landsat imagery


Multitemporal imagery Multispectral image = image comprising multiple spectral

wavebands of a common area Multitemporal image data set = multiple images acquired at

different times of a common area

September 1972 December 1978 March 1987 June 1990

Multitemporal Landsat imagery

Normalized difference vegetation index


Multitemporal analysis

Any form of analysis involving multitemporal imagery

To enable multitemporal analysis, images acquired at different times must share certain common properties

Often image preprocessing is necessary Preprocessing refers to initial procedures conducted to prepare imagery

for subsequent analysis Main types of preprocessing:

Radiometric calibration Cosmetic operations (correcting for missing lines, etc.) Atmospheric correction Illumination correction Geometric correction (and/or registration) Terrain correction

For multitemporal analysis Geometric registration is essential to ensure that the images overlay each

other precisely Other corrections may be useful to ensure that any differences in the

images are not a result of instrument deterioration, atmospheric interference, variations in illumination (time of day/year), and geometric and topographic distortion


Preprocessing

Atmospheric distortion affects remotely-sensed imagery, contributing (erroneously) to pixel values

Atmospheric correction is necessary where reflectance values are desired (as opposed to simple DNs) and where images are being compared over time

There are many methods for atmospheric correction, none of which is perfect and some of which are very complex

Relatively simple and common methods include: Dark object subtraction Histogram matching


Atmospheric correction

The most common atmospheric effect on remotely-sensed imagery is an increase in DN values due to haze, etc.

This increase represents error and should be removed

Dark object subtraction simply involves subtracting the minimum DN value in the image from all pixel values

This approach assumes that the minimum value (i.e. the darkest object in the image) ‘should be’ zero

The darkest object is typically water or shadow

Range (DN)

Fre

quen

cy

40(Min)

204 (Max)

67.12 (Mean) 0 DNs 255

Band 2


Dark object subtraction

Histogram matching is a very simple and fairly crude method of atmospheric correction This involves adjusting one image to approximate another by aligning their histograms

This approach assumes that the ‘general’ differences between the images are due to external (e.g., atmospheric) effects Histogram matching nullifies these general differences, and remaining differences represent ‘real’ change between images

2001 1992Hist mtch 2001to1992

Band 1 Band 1Band 1

Min 42

Max 254

Mean 67

SD 10

Min 59

Max 254

Mean 74

SD 8

Min 59

Max 254

Mean 75

SD 8


Histogram matching

Geometric registration involves registering an image without locational information to a known map coordinate system

This can be done by co-registering one image to another This often involves: 1. Identifying ground control points

2. Resampling the image

Unregistered image

1

2 3

N

Scale

12

3

Registered image


Geometric registration

One common type of multitemporal analysis is change detection

Change detection involves the direct comparison of two or more images to identify how areas change over time

Various methods of change detection are possible…


Change detection

• Image ratioing Division of one image by another Operates on individual image bands Areas of ‘change’ may be thresholded (e.g., > +25% and < -25%)

Post-classification comparison Each image is classified independently, reducing preprocessing need Resulting classifications are compared to identify change Change detection results affected by accuracy of input classifications

Change vector analysis Compares spectral properties of image pixels between dates Expresses change as vectors in spectral space

Composite multitemporal image analysis Images are combined to create a single multi-band image E.g., two 4-band images would create an 8-band composite image Various forms of analysis may be employed (classification, PCA)

Perhaps the most common and simple method, though, is…4th ISPRS Student Consortium and WG VI/5 Summer School, Warsaw 13-19 July 2009.

Change detection methods

Image differencing or image subtraction simply involves the subtraction of one image from another

No change = zero, change = +ve or –ve values

Operates on individual image bands

Areas of ‘change’ may be thresholded (e.g., > +25% and < -25%)


Image differencing


Reading for further information

Aplin, P., 2006, On scales and dynamics in observing the environment, International Journal of Remote Sensing, 27, 2123-2140.

Camps-Valls, G., et al., 2008, Kernel-based framework for multitemporal and multisource remote sensing data classification and change detection, IEEE Transactions on Geoscience and Remote Sensing, 46, 1822-1835.

Canty, M.J., 2007, Image Analysis, Classification and Change Detection in Remote Sensing, with algorithms for ENVI/IDL, CRC Press.

Chuvieco, E. (editor), 2008, Earth Observation of Global Change: The Role of Satellite Remote Sensing in Monitoring the Global Environment, Springer.

Coppin, P., et al., 2004, Digital change detection methods in ecosystem monitoring: a review, International Journal of Remote Sensing, 25, 1565-1596.


Practical exercise Change detection using Idrisi image processing system


Data Time series of NDVI imagery of Africa from the NOAA AVHRR sensor

Dec 87 Mar 88 Jun 88 Sep 88 Dec 88


Image differencing


Image thresholding


Image regression


Help – replacement files

Backup files are provided

File to be created

Backup file provided

Description

DIFF8877 PADF8877 Difference image: AFDEC88 – AFDEC87

CHG8877 PACH8877 Thresholded image: AFDEC88 – AFDEC87

CHGMAP1 PACHMP1 Change map using thresholded image

SAMP87 PASAMP87 Contracted (degraded) image of AFDEC87

SAMP88 PASAMP88 Contracted (degraded) image of AFDEC88

TMP PATMP Step one (first scalar process) towards creating predicted map

PRED88 PAPRED88 Predicted map derived through regression

DIFFADJ PADFADJ Difference image: AFDEC88 – PRED88

CHGADJ PACHADJ Thresholded image: AFDEC88 – PRED88

CHGMAP2 PACHMP2 Change map using regression-derived thresholded image

Change detection

Paul Aplin

School of Geography, University of Nottingham, UK

Chairman, Remote Sensing and Photogrammetry Society

End of le

cture

During the 2005 Ashes, the betfair Blimp showed aerial television coverage throughout the series

Old Trafford, Manchester

11-15 August

Match drawn

But lest we forget…

England win series

12 September


The betfair blimp

The earliest known examples of remote sensing involved taking photographs from (un-manned) balloons tethered above the area of interest

Boston

13 October 1860

Photographed by James

Wallace Black


Historical remote sensing

For years, Meteosat images were downloaded by a ground receiving station in The University of Nottingham

The station is located at the top of the Tower building

The images were provided free to the meteorological/remote sensing community, and accounted for a large proportion of the university’s website traffic

After years of service, the station was deactivated in late 2003 due largely to lack of support and funding

Europe

11 August 1999


University of Nottingham METEOSAT ground receiving station

Aircraft movement can lead to severely geometrically distorted imagery

In this case, aircraft ‘roll’ has caused sensor movement ‘across’ the image swath, resulting in irregular edges

River Arun, West Sussex

22 July 1997

Compact Airborne Spectrographic Imager (CASI)


Aerial image distortion

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Change detection Paul Aplin School of Geography, University of Nottingham, UK Chairman, Remote...

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