1. SEMINAR
ON
A REVIEW OF CHANGE DETECTION TECHNIQUES
INDIAN INSTITUTE OF TECHNOLOGY
ROORKEE
PRESENTED BY:-
ABHISHEK BHATT
RESEARCH SCHOLAR
abhishekbhatt.iitr@gmail.com
2. OUTLINE
This seminar is organized into eight sections as follows:
1. Background and applications of change detection techniques
2. Considerations before implementing change detection
3. A review of seven categories of change detection techniques
4. Comparative analyses among the different techniques
5. A global change analyses
6. Threshold selection
7. Accuracy assessment
8. Summary and recommendations
References
3. Background
• In general, change detection involves the application of multi-
temporal datasets to quantitatively analyze the temporal effects
• Change detection can be defined as the process of identifying
differences in the state of an object or phenomenon by observing it
at different times. This process is usually applied to Earth surface
changes at two or more times.
• understanding relationships and interactions to better manage and
use resources
• Change detection is useful in many applications such as land use
changes, habitat fragmentation, rate of deforestation, coastal change,
urban sprawl, and other cumulative changes
4.
5.
6. Change detection
► Two main categories of land
cover changes:
▪ Conversion of land cover from
one category to a different category.
▪ Modification of the condition of
the land cover type within the same
category (thinning of trees,
selective cutting, pasture to
cultivation, etc.)
source; Norsk Regnesentral website
7. Applications of change detection
techniques
• land-use and land-cover (LULC) change
• forest or vegetation change
• forest mortality, defoliation and damage assessment
• deforestation, regeneration and selective logging
• wetland change
• forest fire and fire-affected area detection
• landscape change
• urban change
• environmental change, drought monitoring, flood monitoring, monitoring
coastal marine environments, desertification, and detection of landslide
areas
• other applications such as crop monitoring, shifting cultivation monitoring,
road segments, and change in glacier mass balance and facies.
8. Considerations before implementing
change detection
• Before implementing change detection
analysis, the following conditions must be
satisfied:
i. precise registration of multi-temporal images;
ii. precise radiometric and atmospheric calibration
or normalization between multi-temporal
images;
iii. selection of the same spatial and spectral
resolution images if possible
9. Good change detection research should
provide the following information:
i. area change and change rate
ii. spatial distribution of changed types
iii. Change trajectories of land-cover types
iv. accuracy assessment of change detection results.
10. A review of change detection
techniques
• Because digital change detection is affected by
spatial, spectral, radiometric and temporal constraints.
• Many change detection techniques are possible to
use, the selection of a suitable method or algorithm
for a given research project is important, but not easy.
11. The seven change detection technique categories
1. Algebra Based Approach 4. Advanced Models
• image differencing • Li-Strahler Reflectance Model
• image regression • Spectral Mixture Model
• image ratioing • Biophysical Parameter Method
• vegetation index differencing 5. GIS
• change vector analysis • Integrated GIS and RS Method
• GIS Approach
2. Transformation 6. visual Analysis
• PCA • Visual Interpretation
• Tasseled Cap (KT) 7. other Change Detection Techniques
• Gramm-Schmidt (GS) • Measures of spatial dependence
• Chi-Square • Knowledge-based vision system
• Area production method
3. Classification Based • Combination of three indicators: vegetation
• Post-Classification Comparison indices, land surface temperature, and
• Spectral-Temporal Combined Analysis spatial structure
• EM Transformation • Change curves
• Unsupervised Change Detection • Generalized linear models
• Hybrid Change Detection • Curve-theorem-based approach
• Artificial Neural Networks (ANN) • Structure-based approach
• Spatial statistics-based method
12. Category I
Algebra Based Approach
• The algebra category includes
– image differencing,
– image regression
– Image ratioing
– vegetation index differencing
– change vector analysis (CVA)
13. Algebra based Approach……
• These algorithms have a common characteristic, i.e. selecting
thresholds to determine the changed areas. These methods
(excluding CVA) are relatively simple, straightforward, easy to
implement and interpret, but these cannot provide complete
matrices of change information.
• In this category, two aspects are critical for the change
detection results:
– selecting suitable image bands
– selecting suitable thresholds
14. Image Differencing
• Concept
– Date 1 - Date 2
– No-change = 0
– Positive and negative values interpretable
– Pick a threshold for change
16. Image Differencing
• Image differencing: Pros
– Simple (some say it’s the most commonly used method)
– Easy to interpret
– Robust
• Cons:
– Difference value is absolute, so same value may have
different meaning
– Requires atmospheric calibration
17. Image regression
► Relationship between pixel values of two dates is
established by using a regression function.
► The dimension of the residuals is an indicator of where
change occurred.
► Advantage
▪ Reduces impact of atmospheric, sensor and environmental
differences.
► Drawback
• Requires development of accurate regression functions.
• Does not provide change matrix.
19. Image Ratioing
• Concept
– Date 1 / Date 2
– No-change = 1
– Values less than and greater than 1 are interpretable
– Pick a threshold for change
• Pros
– Simple
– May mitigate problems with viewing conditions, esp. sun angle
• Cons
– Scales change according to a single date, so same change on the ground
may have different score depending on direction of change; I.e. 50/100
= .5, 100/50 = 2.0
21. Change vector analysis
• In n-dimensional spectral
space, determine length
and direction of vector
between Date 1 and Date2 Date 1
Band 4
• No-change = 0 length
Date 2
• Change direction may be
interpretable
• Pick a threshold for
change
Band 3
22. Change vector analysis
► Determines in n-dimensional spectral space,
the length and direction of the vector between
Date 1 and Date 2.
► Produces an intensity image and a direction
image of change. The direction image can be
used to classify change.
► Typically used when all changes need to be
investigated.
► Advantage
▪ Works on multispectral data.
• Allows designation of the type of change
occurring
► Drawback
source; Norsk Regnesentral website ▪ Shares some of the drawbacks of algebra based
techniques but less severe
24. Category I. Algebra Based Approach
Techniques Characteristics Advantages Disadvantages Examples Key factors
1. Image Subtracts the first date Simple and Cannot provide Forest Identifies
differencing image from a second- Straight forward, a detailed change defoliation, suitable image
date image, pixel by easy to interpret matrix, requires land-cover bands and
pixel the results selection of Change and thresholds
thresholds irrigated
crops
monitoring
2. Image Establishes relationships Reduces impacts Requires to develop Tropical forest Develops the
regression between bitemporal of the atmospheric, accurate regression change and regression
images, then estimates sensor and functions for the forest function;
pixel values of the environmental selected bands conversion identifies
second-date image by use differences between before suitable bands
of a regression function, two-date images implementing and thresholds
subtracts the regressed change detection
image from the first-date
image
3. Image Calculates the ratio of Reduces impactsof Non-normal Land-use Identifies the
ratioing registered images of two Sun angle, shadow distribution of the mapping image bands
dates, band by band and topography result is often and thresholds
criticized
25. Techniques Characteristics Advantages Disadvantages Examples Key factors
4. Vegetation Produces vegetation index separately, Emphasizes random noise or Vegetation Identifies suitable
Index then subtracts the differences in the coherence noise change vegetation index
differencing second-date vegetation index spectral response and forest and thresholds
from the first-date vegetation index of different features canopy
and reduces impacts change
of topographic effects Enhances
and illumination.
5. Change Generates two outputs: (1) the Ability to process Difficult to landscape Defines
vector analysis spectral change vector describes the any number of identify land variables thresholds
(CVA) direction and magnitude of change spectral bands cover change land-cover and identifies
from the first to the second date; and desired and to trajectories changes change
(2) the total change magnitude per produce detailed disaster trajectories
pixel is computed by determining the change detection assessment
Euclidean distance between end information and conifer
points through n-dimensional change forest change
space
27. Transformations
► Principal Component Analysis
► Alt1: Perform PCA on data from both
dates and analyse the component
images.
► Alt2: Perform PCA separately on each
image and subtract the second-date PC
image from that of the first date.
► Advantage
▪ Reduces data redundancy.
► Drawback
▪ Results are scene dependent and can be difficult to interpret.
▪ Does not provide change matrix.
28. Kauth Thomas Transformation
• Described the temporal spectral patterns derived from Landsat MSS imagery
for crops. As crops grow from seed to maturity, there is a net increase in
NIR and decrease in Red Reflectance. This effect varies based on soil Color
• Brightness Greenness Wetness
• The Brightness, Greenness, Wetness transform was first developed for use
with the Landsat MSS system and called the “Tasseled Cap” transformation.
• The transform is based on a set of constants applied to the image in the form
of a linear algebraic formula.
• Brightness – primary axis calculated as the weighted sum of reflectances of
all spectral bands.
• Greenness – perpendicular to the axis of the Brightness component that
passes through the point of maturity of all plants
• Yellow Stuff – perpendicular to both Greenness and Brightness axis
representing senesced vegetation.
29. Kauth Thomas Transformation
Typically the first few
components contain most of the
information in the data so that
four channels of LANDSAT MSS
data or the six channels of the
http://www.sjsu.edu/faculty/watkins/tassel.htm Thematic Mapper data may be
reduced to just three principal
components. The components
higher than three are usually
treated as being information less.
Source; www.sjsu.edu/faculty/watkins/tassel.htm
30. Category II. Transformation
Techniques Characteristics Advantages Disadvantages Examples Key factors
1. Principal Assumes that multitemporal data Reduces data PCA is scene dependent, Land-cover Analyst’s skill in
component are highly correlated and change Redundancy thus the change detection change urban identifying which
analysis (PCA) information can be highlighted in between bands results between different expansion component best
the new components. Two ways to and emphasizes dates are often difficult ,tropical forest represents the
apply PCA for change detection different to interpret and label. It conversion , change and
are: (1)put two or more dates of information in cannot provide a forest selecting
images into a single file, then the derived complete matrix of mortality and thresholds
perform PCA and analyse the components change class information forest
minor component images for and requires determining defoliation
change information; and thresholds to identify the
(2) perform PCA separately, then changed areas
subtract the second-date PC image
from the corresponding PC image
of the first date
2. Tasselled cap The principle of this method is Reduces data Difficult to interpret and Monitoring Analyst’s skill is
(KT) similar to PCA. The only difference redundancy label change forest needed in
from PCA is that PCA depends on between bands information, cannot mortality , identifying
the image scene, andKT and emphasizes provide a complete monitoring which
transformation is independent of different change matrix; requires green biomass component best
the scene. The change detection is information in determining thresholds and represents the
implemented based on the three the derived to identify the changed land-use change and
components: brightness, greenness components. areas. Accurate change thresholds
and wetness KT is scene atmospheric calibration
independent. is required
31. Techniques Characteristics Advantages Disadvantages Examples Key factors
3. The GS method The association It is difficult to extract Monitoring Initial
Gramm– orthogonalizes spectral of transformed more than one single forest identification of
Schmidt vectors taken directly from components component related to a mortality the stable
(GS) bi-temporal images, as does with scene given type of subspace of the
the original KT method, characteristics change. The GS multi-date data is
produces three stable allows the process relies on required
components corresponding extraction of selection of spectral
to multitemporal analogues information that vectors from multi-date
of KT brightness, greenness would not be image typical of the
and wetness, and a change accessible using type of change being
component other examined
techniques
4. Chi- Y=(X-M)T ∑-1*(X-M) Multiple bands The assumption that a Urban Y is distributed
square Y:digital value of change Are value of Y~0 environmen as a Chi-square
image simultaneously represents a pixel of no tal random variable
X:vector of the difference considered to change is not true when change with p degrees
of the six digital values produce a a large portion of the of freedom ( p is
between the two dates single change image is the number of
M:vector of the mean image. changed. Also the bands)
residual of each band change related to
T:transverse of the matrix specific spectral
∑-1= inverse covariance direction not identified
matrix
33. Post-classification
• Post-classification (delta classification)
– Classify Date 1 and Date 2 separately, compare class values
on pixel by pixel basis between dates
• Post-classification: Pros
– Avoids need for strict radiometric calibration
– Favors classification scheme of user
– Designates type of change occurring
• Cons
– Error is multiplicative from two parent maps
– Changes within classes may be interesting
34. Composite Analysis
• Composite Analysis
– Stack Date 1 and Date 2 and run unsupervised
classification on the whole stack
• Composite Analysis: Pros
– May extract maximum change variation
– Includes reference for change, so change is anchored at
starting value, unlike change vector analysis and image
differencing
• Cons
– May be extremely difficult to interpret classes
35. Unsupervised techniques
► Objective
▪ Produce a change detection map in which
changed areas are separated from unchanged
ones.
► The changes sought are assumed to result in
larger changes in radiance values than other
factors.
► Comparison is performed directly on the
spectral data.
► This results in a difference image which is
analysed to separate insignificant from
significant changes.
source; Norsk Regnesentral website
36. Supervised techniques
Objective
Generate a change detection map
where changed areas are
identified and the land-cover
transition type can be identified.
The changes are detected and
labelled using supervised
classification approaches.
Main techniques:
• Post-classification comparison
• Multidate direct classification
source; Norsk Regnesentral website
37. Post classification comparison
► Standard supervised classifiers are used to
classify the two images independently.
► Changes are detected by comparing the two
classified images.
► Advantage
▪ Common and intuitive.
▪ Provides change matrix.
► Drawback
▪ Critically depends on the accuracy of the
classification maps. Accuracy close to the
product of the two results.
▪ Does not exploit the dependence between
the information from the two points in time.
source; Norsk Regnesentral website
39. Multidate direct classification
► Two dates are combined into one multitemporal
image and classified.
► Performs joint classification of the two images
by
using a stacked feature vector.
► Change detection is performed by considering
each
transition as a class, and training the classifier to
recognize all classes and all transitions.
► Advantage
▪ Exploits the multitemporal information.
▪ Error rate not cumulative.
▪ Provides change matrix.
► Drawback
▪ Ground truth required also for transitions.
source; Norsk Regnesentral website
40. Supervised vs. Unsupervised
Supervised Unsupervised
Level of change Change detection at Change detection at data
decision level. level.
detection
Change Provides explicit labeling Separates ‘change’ from
of change and class ‘no change’.
information transitions
Change Obtained directly from Obtained through
the classified images. interpretation of the
computation difference image.
Ground truth Requires ground truth. Requires no ground truth.
Spectral Multispectral. Most methods work on
one spectral band.
information.
Data requirements Not sensitive to Sensitive to atmospheric
atmospheric conditions conditions and sensor
and sensor differences. differences.
41. Category III. Classification based approach
Techniques Characteristics Advantages Disadvantages Examples Key factors
1. Post Separately classifies multi- Minimizes Requires a great LULC change, Selects sufficient
classification temporal images into thematic impacts of amount of time and wetland training sample
comparison maps, then implements comparison atmospheric, expertise to create change data for
of the classified images, pixel by sensor and classification and urban classification
pixel environmental products. The final expansion
differences accuracy depends on
between the quality of the
multitemporal classified image of
images; provides a each date
complete matrix of
change
information
2. Spectral– Puts multi-temporal data into a Simple and Difficult to identify Changes in Labels the
temporal single file, then classifies the timesaving and label the change coastal zone change classes
combined combined dataset and identifies and in classification classes; cannot environments
analysis labels the changes provide a complete and forest
matrix of change change
information
3. EM The EM detection is a This method was Requires estimating Land-cover Estimates the
detection classification-based method using reported to the a priori joint change a priori joint
an expectation maximization (EM) provide higher class probability. class probability
algorithm to estimate the a priori change detection
joint class probabilities at two accuracy than
times. These probabilities are other change
estimated directly from the images detection methods
under analysis
42. Techniques Characteristics Advantages Disadvantages Examples Key factors
4. Unsupervised Selects spectrally similar groups of This method Difficulty in Forest hange Identifies the
change pixels and clusters date 1 image makes use of the identifying and spectrally similar
detection into primary clusters, then labels unsupervised labelling change or relatively
spectrally similar groups in date 2 nature and trajectories homogeneous
image into primary clusters in date automation of the units
2 image, and finally detects and change analysis
identifies changes and outputs process
results
5. Hybrid Uses an overlay enhancement from This method Requires selection LULC change Selects suitable
change a selected image to isolate changed Excludes of thresholds to , vegetation thresholds to
detection pixels, then uses Supervised unchanged implement change identify the
classification. A binary change pixels from classification; and change and non-
mask is constructed from the classification to somewhat complicated monitoring change areas and
classification results. This change reduce to eelgrass develops accurate
mask sieves out the classification identify change classifi’n output
changed themes from the LULC errors trajectories
maps produced for each date
6. Artificial The input used to train the neural ANN is a The nature of hidden Mortality The architecture
neural network is the spectral data of the nonparametric layers is poorly detection in used such as the
networks period of change. A Supervised known; a long training Lake , land- number of hidden
(ANN) backpropagation algorithm is often method and has time is required. ANN cover change, layers, and
used to train the multi-layer the ability to is often sensitive to the forest change, training samples
perceptron neural network model estimate the amount of training Urban hange
properties of data data used. ANN
based on the functions are not
training samples common in image
processing software
43. Category IV. Advanced models
Techniques Characteristics Advantages Disadvantages Examples Key factors
1. Li–Strahler The Li–Strahler canopy model This method combines This method Mapping Develops the
reflectance is used to estimate each conifer the techniques of digital requires a large and Stand crown
model stand crown cover for two dates image processing of number of field monitoring cover images
of imageries separately. remotely sensed data Measurement data. conifer and identifies
Comparison of the stand crown with traditional sampling It is complex and mortality the crown
covers for two dates is and field observation not available in characteristics
conducted to produce the methods. It provides commercial image of vegetation
change detection results statistical results and processing types
maps showing the software. It is only
geometric distribution of suitable for
changed patterns vegetation change
2. Spectral Uses spectral mixture analysis to The fractions have This method is Land-cover Identifies
mixture derive fraction images. biophysical meanings, regarded as an change, suitable
model Endmembers are selected from representing the areal advanced image seasonal endmembers;
training areas on the image or proportion of each processing vegetation defines suitable
from spectra of materials endmember within the analysis and is patterns and thresholds for
occurring in the study area or pixel. The results are somewhat Vegetation each land-
from a relevant spectral library. stable, accurate and complex change cover
Changes are detected by repeatable using TM class based on
comparing the ‘before’ and data fractions
‘after’ fraction images of each
end member. The quantitative
changes can be measured by
classifying images based on the
endmember fractions
44. Category V. GIS based approach
Techniques Characteristics Advantages Disadvantages Examples Key factors
3. Incorporates image data and Allows access of Different data LULC The accuracy of
Integrated GIS data, such as the ancillary data to quality from and different data
GIS and overlay of GIS layers aid interpretation various sources urban sources and their
remote directly on image data; and analysis and often degrades the sprawl registration
Sensing moves results of image has the ability to results of LULC accuracies
method processing into GIS system directly update change detection between the
for land-use thematic images
further analysis information in GIS
4. GIS Integrates past and current This method Different GIS data Urban The accuracy of
approach maps of land use with allows with different change different data
topographic and geological incorporation of geometric And sources and their
data. The image overlaying aerial accuracy and landscape registration
and binary masking photographic data classification change accuracies
techniques are useful in of current and past system degrades between the
revealing quantitatively the land-use data with the quality of thematic images.
change dynamics in each other map data results
category
45. Category VI. Visual analysis
Techniques Characteristics Advantages Disadvantages Examples Key factors
1. Visual One band (or VI) from date1 Human experience Cannot provide Land-use Analyst’s
interpretation image as red, the same band and knowledge are detailed change change, skill and
(or VI) from date2 image as useful during visual information. forest familiarit
green, and the same band (or interpretation. Two or The results change , y with
VI) from date3 image as blue three dates of images depend on the monitoring the study
if available. Visually can be analysed at analyst’s skill in selectively area
interprets the colour one time. The analyst image logged
composite to identify the can incorporate interpretation. areas and
changed areas. An alternative texture, shape, size Time- land cover
is to implement on-screen and patterns consuming and change
digitizing of changed areas intovisual difficulty in
using visual interpretation interpretation to updating the
based on overlaid images of make a decision on results
diff. dates the LULC change
46. Category VII. Other change detection
techniques
1. Measures of spatial dependence (Henebry 1993)
2. Knowledge-based vision system (Wang 1993)
3. Area production method (Hussin et al. 1994)
4. Combination of three indicators: vegetation indices, land surface
temperature, and spatial structure (Lambin and Strahler 1994b)
5. Change curves (Lawrence and Ripple 1999)
6. Generalized linear models (Morisette et al. 1999)
7. Curve-theorem-based approach (Yue et al. 2002)
8. Structure-based approach (Zhang et al. 2002)
9. Spatial statistics-based method (Read and Lam 2002)
47. Factors to consider when choosing a method
► Objective of the change detection?
▪ Monitor/identify specific changes
▪ More efficient mapping at T2
▪ Improved quality of mapping at T2
► What type of change information to extract?
▪ Spectral changes
▪ Land cover transitions
▪ Shape changes
▪ Changes in long temporal series
► What type of changes to be considered?
▪ Land use and land cover change
▪ Forest and vegetation change
▪ Wetland change
▪ Urban change
▪ Environmental change
48. Factors to consider…
► Expected amount of changes
► Available data at date 1 and date 2
• Remote sensing data
• Temporal, spatial and spectral characteristics.
• Differences in characteristics btw. date 1 and date 2.
• Classified maps
• Ground truth
► Environmental considerations
• Atmospheric conditions
• Soil moisture conditions
• Phenological states
► Accuracy requirements
49. Comparing the Different Techniques
Two types of change detection either detect binary change/non-change, or
the detailed “from-to” change between different classes.
Different change detection techniques are often tested and compared
based on an accuracy assessment or qualitative assessment.
no single method is suitable for all cases.
A combination of two change detection techniques can improve the
change detection results (image differencing/PCA, NDVI/PCA,
PCA/CVA).
The most common change detection methods: image differencing, PCA,
CVA, and post-classification comparison.
50. Global change analyses and image
resolution
For change detection at high or moderate spatial resolution: use Landsat
TM, SPOT, or radar.
For change detection at the continental or global scale, use coarse
resolution data such as MODIS and AVHRR.
AVHRR has daily availability at low cost; it is the best source of data for
large area change detection.
NDVI and land surface temperatures derived from MODIS or AVHRR
thermal bands are especially useful in large area change detection.
51. Threshold Selection
Many change detection algorithms require threshold selection to determine whether a
pixel has changed.
Thresholds can be adjusted manually until the resulting image is satisfactory, or they
can be selected statistically using a suitable standard deviation from a class mean.
Both are highly subjective methods.
Other methods exist for improving the change detection results, such as using fuzzy
set and fuzzy membership functions to replace the thresholds.
However, threshold selection is simple and intuitive, so it is still the most
extensively applied method for detecting binary change/no-change information.
52. Accuracy Assessment
Accuracy assessments are important for understanding the change detection
results and using these results in decision-making.
However, they are difficult to do because reliable temporal field-based datasets are
often problematic to collect.
The error matrix is the most common method for accuracy assessment. To
properly generate one, the following factors must be considered:
1. ground truth data collection,
2. classification scheme,
3. sampling scheme,
4. spatial autocorrelation, and
5. sample size and sample unit.
53. Summary and Recommendations
The binary change/no-change threshold techniques all have difficulties in
distinguishing true changed areas from the detected change areas. Single-
band image differencing and PCA are the recommended methods.
Classification-based change detection methods can avoid such problems,
but requires more effort to implement. Post-classification comparison is a
suitable method when sufficient training data is available.
When multi-source data is available, GIS techniques can be helpful.
Advanced techniques such as LSMA, ANN, or a combination of change
detection methods can produce higher quality change detection results.
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