a review of change detection techniques
TRANSCRIPT
SEMINARON
A REVIEW OF CHANGE DETECTION TECHNIQUES
PRESENTED BY:-ABHISHEK BHATT
RESEARCH [email protected]
INDIAN INSTITUTE OF TECHNOLOGYROORKEE
OUTLINEThis 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
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 identifyingdifferences in the state of an object or phenomenon by observing itat different times. This process is usually applied to Earth surfacechanges at two or more times.
• understanding relationships and interactions to better manage anduse resources
• Change detection is useful in many applications such as land usechanges, habitat fragmentation, rate of deforestation, coastal change,urban sprawl, and other cumulative changes
Change detection
► Two main categories of landcover changes:
▪ Conversion of land cover fromone category to a different category.
▪ Modification of the condition ofthe land cover type within the samecategory (thinning of trees,selective cutting, pasture tocultivation, etc.)
source; Norsk Regnesentral website
Applications of change detectiontechniques
• 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, monitoringcoastal marine environments, desertification, and detection of landslideareas
• other applications such as crop monitoring, shifting cultivation monitoring,road segments, and change in glacier mass balance and facies.
Considerations before implementingchange detection
• Before implementing change detectionanalysis, the following conditions must besatisfied:i. precise registration of multi-temporal images;
ii. precise radiometric and atmospheric calibrationor normalization between multi-temporalimages;
iii. selection of the same spatial and spectralresolution images if possible
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.
Good change detection research shouldprovide the following information:
A review of change detectiontechniques
• Because digital change detection is affected byspatial, spectral, radiometric and temporal constraints.
• Many change detection techniques are possible touse, the selection of a suitable method or algorithmfor a given research project is important, but not easy.
The seven change detection technique categories
1. Algebra Based Approach• image differencing
• image regression
• image ratioing
• vegetation index differencing
• change vector analysis
2. Transformation• PCA
• Tasseled Cap (KT)
• Gramm-Schmidt (GS)
• Chi-Square
3. Classification Based• Post-Classification Comparison
• Spectral-Temporal Combined Analysis
• EM Transformation
• Unsupervised Change Detection
• Hybrid Change Detection
• Artificial Neural Networks (ANN)
4. Advanced Models• Li-Strahler Reflectance Model
• Spectral Mixture Model
• Biophysical Parameter Method
5. GIS• Integrated GIS and RS Method
• GIS Approach
6. visual Analysis• Visual Interpretation
7. other Change Detection Techniques• Measures of spatial dependence
• Knowledge-based vision system
• Area production method
• Combination of three indicators: vegetationindices, land surface temperature, andspatial structure
• Change curves
• Generalized linear models
• Curve-theorem-based approach
• Structure-based approach
• Spatial statistics-based method
Category IAlgebra Based Approach
• The algebra category includes
– image differencing,
– image regression
– Image ratioing
– vegetation index differencing
– change vector analysis (CVA)
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 toimplement and interpret, but these cannot provide completematrices of change information.
• In this category, two aspects are critical for the changedetection results:
– selecting suitable image bands
– selecting suitable thresholds
Image Differencing
• Concept
– Date 1 - Date 2
– No-change = 0
– Positive and negative values interpretable
– Pick a threshold for change
Image Differencing
8 10 8 11
240 11 10 22
205 210 205 54
220 98 88 46
5 9 7 10
97 9 8 22
98 100 205 222
103 98 254 210
3 1 1 1
143 2 2 0
107 110 0 -168
117 0 -166 -164
Image Date 1
Image Date 2Difference Image =Image 1 - Image 2
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 havedifferent meaning
– Requires atmospheric calibration
Image regression► Relationship between pixel values of two dates isestablished by using a regression function.
► The dimension of the residuals is an indicator of wherechange occurred.
► Advantage▪ Reduces impact of atmospheric, sensor and environmental
differences.
► Drawback• Requires development of accurate regression functions.
• Does not provide change matrix.
Image regression
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 groundmay have different score depending on direction of change; I.e. 50/100= .5, 100/50 = 2.0
Change Detectionsource: CCRS website, CANADA
Image Difference (TM99 – TM88) Image Ratio (TM99 / TM88)
Change vector analysis
• In n-dimensional spectralspace, determine lengthand direction of vectorbetween Date 1 and Date2
• No-change = 0 length
• Change direction may beinterpretable
• Pick a threshold forchange
Band 3
Ban
d 4
Date 1
Date 2
Change vector analysis
► Determines in n-dimensional spectral space,
the length and direction of the vector betweenDate 1 and Date 2.
► Produces an intensity image and a directionimage of change. The direction image can beused to classify change.
► Typically used when all changes need to beinvestigated.► Advantage▪ Works on multispectral data.• Allows designation of the type of change
occurring► Drawback▪ Shares some of the drawbacks of algebra basedtechniques but less severe
source; Norsk Regnesentral website
Change vector analysis
Category I. Algebra Based ApproachTechniques Characteristics Advantages Disadvantages Examples Key factors
1. Imagedifferencing
Subtracts the first dateimage from a second-date image, pixel bypixel
Simple andStraight forward,easy to interpretthe results
Cannot providea detailed changematrix, requiresselection ofthresholds
Forestdefoliation,land-coverChange andirrigatedcropsmonitoring
Identifiessuitable imagebands andthresholds
2. Imageregression
Establishes relationshipsbetween bitemporalimages, then estimatespixel values of thesecond-date image by useof a regression function,subtracts the regressedimage from the first-dateimage
Reduces impactsof the atmospheric,sensor andenvironmentaldifferences betweentwo-date images
Requires to developaccurate regressionfunctions for theselected bandsbeforeimplementingchange detection
Tropical forestchange andforestconversion
Develops theregressionfunction;identifiessuitable bandsand thresholds
3. Imageratioing
Calculates the ratio ofregistered images of twodates, band by band
Reduces impactsofSun angle, shadowand topography
Non-normaldistribution of theresult is oftencriticized
Land-usemapping
Identifies theimage bandsand thresholds
Techniques Characteristics Advantages Disadvantages Examples Key factors
4. VegetationIndexdifferencing
Produces vegetation index separately,then subtracts thesecond-date vegetation indexfrom the first-date vegetation index
Emphasizesdifferences in thespectral responseof different featuresand reduces impactsof topographic effectsand illumination.
random noise orcoherence noise
Vegetationchangeand forestcanopychangeEnhances
Identifies suitablevegetation indexand thresholds
5. Changevector analysis(CVA)
Generates two outputs: (1) thespectral change vector describes thedirection and magnitude of changefrom the first to the second date; and(2) the total change magnitude perpixel is computed by determining theEuclidean distance between endpoints through n-dimensional changespace
Ability to processany number ofspectral bandsdesired and toproduce detailedchange detectioninformation
Difficult toidentify landcover changetrajectories
landscapevariablesland-coverchangesdisasterassessmentand coniferforest change
Definesthresholdsand identifieschangetrajectories
Category II.
Transformation of data sets
Transformations► Principal Component Analysis
► Alt1: Perform PCA on data from bothdates and analyse the componentimages.
► Alt2: Perform PCA separately on eachimage and subtract the second-date PCimage 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.
Kauth Thomas Transformation
• Described the temporal spectral patterns derived from Landsat MSS imageryfor crops. As crops grow from seed to maturity, there is a net increase inNIR and decrease in Red Reflectance. This effect varies based on soil Color
• Brightness Greenness Wetness
• The Brightness, Greenness, Wetness transform was first developed for usewith 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 formof a linear algebraic formula.
• Brightness – primary axis calculated as the weighted sum of reflectances ofall spectral bands.
• Greenness – perpendicular to the axis of the Brightness component thatpasses through the point of maturity of all plants
• Yellow Stuff – perpendicular to both Greenness and Brightness axisrepresenting senesced vegetation.
Typically the first fewcomponents contain most of theinformation in the data so thatfour channels of LANDSAT MSSdata or the six channels of theThematic Mapper data may bereduced to just three principalcomponents. The componentshigher than three are usuallytreated as being information less.
Kauth Thomas Transformation
http://www.sjsu.edu/faculty/watkins/tassel.htm
Source; www.sjsu.edu/faculty/watkins/tassel.htm
http://www.sjsu.edu/faculty/watkins/tassel.htm
Category II. TransformationTechniques Characteristics Advantages Disadvantages Examples Key factors
1. Principalcomponentanalysis (PCA)
Assumes that multitemporal dataare highly correlated and changeinformation can be highlighted inthe new components. Two ways toapply PCA for change detectionare: (1)put two or more dates ofimages into a single file, thenperform PCA and analyse theminor component images forchange information; and(2) perform PCA separately, thensubtract the second-date PC imagefrom the corresponding PC imageof the first date
Reduces dataRedundancybetween bandsand emphasizesdifferentinformation inthe derivedcomponents
PCA is scene dependent,thus the change detectionresults between differentdates are often difficultto interpret and label. Itcannot provide acomplete matrix ofchange class informationand requires determiningthresholds to identify thechanged areas
Land-coverchange urbanexpansion,tropical forestconversion ,forestmortality andforestdefoliation
Analyst’s skill inidentifying whichcomponent bestrepresents thechange andselectingthresholds
2. Tasselled cap(KT)
The principle of this method issimilar to PCA. The only differencefrom PCA is that PCA depends onthe image scene, andKTtransformation is independent ofthe scene. The change detection isimplemented based on the threecomponents: brightness, greennessand wetness
Reduces dataredundancybetween bandsand emphasizesdifferentinformation inthe derivedcomponents.KT is sceneindependent.
Difficult to interpret andlabel changeinformation, cannotprovide a completechange matrix; requiresdetermining thresholdsto identify the changedareas. Accurateatmospheric calibrationis required
Monitoringforestmortality ,monitoringgreen biomassandland-usechange
Analyst’s skill isneeded inidentifyingwhichcomponent bestrepresents thechange andthresholds
Techniques Characteristics Advantages Disadvantages Examples Key factors
3.Gramm–Schmidt(GS)
The GS methodorthogonalizes spectralvectors taken directly frombi-temporal images, as doesthe original KT method,produces three stablecomponents correspondingto multitemporal analoguesof KT brightness, greennessand wetness, and a changecomponent
The associationof transformedcomponentswith scenecharacteristicsallows theextraction ofinformation thatwould not beaccessible usingothertechniques
It is difficult to extractmore than one singlecomponent related to agiven type ofchange. The GSprocess relies onselection of spectralvectors from multi-dateimage typical of thetype of change beingexamined
Monitoringforestmortality
Initialidentification ofthe stablesubspace of themulti-date data isrequired
4. Chi-square
Y=(X-M)T ∑-1*(X-M)Y:digital value of changeimageX:vector of the differenceof the six digital valuesbetween the two datesM:vector of the meanresidual of each bandT:transverse of the matrix∑-1
= inverse covariancematrix
Multiple bandsAresimultaneouslyconsidered toproduce asingle changeimage.
The assumption that avalue of Y~0represents a pixel of nochange is not true whena large portion of theimage ischanged. Also thechange related tospecific spectraldirection not identified
Urbanenvironmentalchange
Y is distributedas a Chi-squarerandom variablewith p degreesof freedom ( p isthe number ofbands)
Category-III
Classification based approach
Post-classification
• Post-classification (delta classification)
– Classify Date 1 and Date 2 separately, compare class valueson 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
Composite Analysis
• Composite Analysis
– Stack Date 1 and Date 2 and run unsupervisedclassification on the whole stack
• Composite Analysis: Pros
– May extract maximum change variation
– Includes reference for change, so change is anchored atstarting value, unlike change vector analysis and imagedifferencing
• Cons
– May be extremely difficult to interpret classes
Unsupervised techniques
► Objective▪ Produce a change detection map in whichchanged areas are separated from unchangedones.
► The changes sought are assumed to result inlarger changes in radiance values than otherfactors.
► Comparison is performed directly on thespectral data.
► This results in a difference image which isanalysed to separate insignificant fromsignificant changes.
source; Norsk Regnesentral website
Supervised techniques
ObjectiveGenerate a change detection mapwhere changed areas areidentified and the land-covertransition type can be identified.
The changes are detected andlabelled using supervisedclassification approaches.
Main techniques:• Post-classification comparison• Multidate direct classification
source; Norsk Regnesentral website
Post classification comparison
► Standard supervised classifiers are used toclassify the two images independently.
► Changes are detected by comparing the twoclassified images.
► Advantage▪ Common and intuitive.▪ Provides change matrix.
► Drawback▪ Critically depends on the accuracy of theclassification maps. Accuracy close to theproduct of the two results.▪ Does not exploit the dependence betweenthe information from the two points in time.
source; Norsk Regnesentral website
Post classification comparison
Multidate direct classification
► Two dates are combined into one multitemporalimage and classified.► Performs joint classification of the two imagesbyusing a stacked feature vector.► Change detection is performed by consideringeachtransition as a class, and training the classifier torecognize 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
Supervised vs. UnsupervisedSupervised Unsupervised
Level of changedetection
Change detection atdecision level.
Change detection at datalevel.
Changeinformation
Provides explicit labelingof change and classtransitions
Separates ‘change’ from‘no change’.
Changecomputation
Obtained directly fromthe classified images.
Obtained throughinterpretation of thedifference image.
Ground truth Requires ground truth. Requires no ground truth.
Spectralinformation.
Multispectral. Most methods work onone spectral band.
Data requirements Not sensitive toatmospheric conditionsand sensor differences.
Sensitive to atmosphericconditions and sensordifferences.
Category III. Classification based approachTechniques Characteristics Advantages Disadvantages Examples Key factors
1. Postclassificationcomparison
Separately classifies multi-temporal images into thematicmaps, then implements comparisonof the classified images, pixel bypixel
Minimizesimpacts ofatmospheric,sensor andenvironmentaldifferencesbetweenmultitemporalimages; provides acomplete matrix ofchangeinformation
Requires a greatamount of time andexpertise to createclassificationproducts. The finalaccuracy depends onthe quality of theclassified image ofeach date
LULC change,wetlandchangeand urbanexpansion
Selects sufficienttraining sampledata forclassification
2. Spectral–temporalcombinedanalysis
Puts multi-temporal data into asingle file, then classifies thecombined dataset and identifies andlabels the changes
Simple andtimesavingin classification
Difficult to identifyand label the changeclasses; cannotprovide a completematrix of changeinformation
Changes incoastal zoneenvironmentsand forestchange
Labels thechange classes
3. EMdetection
The EM detection is aclassification-based method usingan expectation maximization (EM)algorithm to estimate the a priorijoint class probabilities at twotimes. These probabilities areestimated directly from the imagesunder analysis
This method wasreported toprovide higherchange detectionaccuracy thanother changedetection methods
Requires estimatingthe a priori jointclass probability.
Land-coverchange
Estimates thea priori jointclass probability
Techniques Characteristics Advantages Disadvantages Examples Key factors4. Unsupervisedchangedetection
Selects spectrally similar groups ofpixels and clusters date 1 imageinto primary clusters, then labelsspectrally similar groups in date 2image into primary clusters in date2 image, and finally detects andidentifies changes and outputsresults
This methodmakes use of theunsupervisednature andautomation of thechange analysisprocess
Difficulty inidentifying andlabelling changetrajectories
Forest hange Identifies thespectrally similaror relativelyhomogeneousunits
5. Hybridchangedetection
Uses an overlay enhancement froma selected image to isolate changedpixels, then uses Supervisedclassification. A binary changemask is constructed from theclassification results. This changemask sieves out thechanged themes from the LULCmaps produced for each date
This methodExcludesunchangedpixels fromclassification toreduceclassificationerrors
Requires selectionof thresholds toimplementclassification;somewhat complicatedtoidentify changetrajectories
LULC change, vegetationchangeandmonitoringeelgrass
Selects suitablethresholds toidentify thechange and non-change areas anddevelops accurateclassifi’n output
6. Artificialneuralnetworks(ANN)
The input used to train the neuralnetwork is the spectral data of theperiod of change. Abackpropagation algorithm is oftenused to train the multi-layerperceptron neural network model
ANN is anonparametricSupervisedmethod and hasthe ability toestimate theproperties of databased on thetraining samples
The nature of hiddenlayers is poorlyknown; a long trainingtime is required. ANNis often sensitive to theamount of trainingdata used. ANNfunctions are notcommon in imageprocessing software
Mortalitydetection inLake , land-cover change,forest change,Urban hange
The architectureused such as thenumber of hiddenlayers, andtraining samples
Category IV. Advanced modelsTechniques Characteristics Advantages Disadvantages Examples Key factors
1. Li–Strahlerreflectancemodel
The Li–Strahler canopy modelis used to estimate each coniferstand crown cover for two datesof imageries separately.Comparison of the stand crowncovers for two dates isconducted to produce thechange detection results
This method combinesthe techniques of digitalimage processing ofremotely sensed datawith traditional samplingand field observationmethods. It providesstatistical results andmaps showing thegeometric distribution ofchanged patterns
This methodrequires a largenumber of fieldMeasurement data.It is complex andnot available incommercial imageprocessingsoftware. It is onlysuitable forvegetation change
Mappingandmonitoringconifermortality
Develops theStand crowncover imagesand identifiesthe crowncharacteristicsof vegetationtypes
2. Spectralmixturemodel
Uses spectral mixture analysis toderive fraction images.Endmembers are selected fromtraining areas on the image orfrom spectra of materialsoccurring in the study area orfrom a relevant spectral library.Changes are detected bycomparing the ‘before’ and‘after’ fraction images of eachend member. The quantitativechanges can be measured byclassifying images based on theendmember fractions
The fractions havebiophysical meanings,representing the arealproportion of eachendmember within thepixel. The results arestable, accurate andrepeatable
This method isregarded as anadvanced imageprocessinganalysis and issomewhatcomplex
Land-coverchange,seasonalvegetationpatterns andVegetationchangeusing TMdata
Identifiessuitableendmembers;defines suitablethresholds foreach land-coverclass based onfractions
Category V. GIS based approachTechniques Characteristics Advantages Disadvantages Examples Key factors
3.IntegratedGIS andremoteSensingmethod
Incorporates image data andGIS data, such as theoverlay of GIS layersdirectly on image data;moves results of imageprocessing into GIS systemforfurther analysis
Allows access ofancillary data toaid interpretationand analysis andhas the ability todirectly updateland-useinformation in GIS
Different dataquality fromvarious sourcesoften degrades theresults of LULCchange detection
LULCandurbansprawl
The accuracy ofdifferent datasources and theirregistrationaccuraciesbetween thethematic images
4. GISapproach
Integrates past and currentmaps of land use withtopographic and geologicaldata. The image overlayingand binary maskingtechniques are useful inrevealing quantitatively thechange dynamics in eachcategory
This methodallowsincorporation ofaerialphotographic dataof current and pastland-use data withother map data
Different GIS datawith differentgeometricaccuracy andclassificationsystem degradesthe quality ofresults
UrbanchangeAndlandscapechange
The accuracy ofdifferent datasources and theirregistrationaccuraciesbetween thethematic images.
Category VI. Visual analysisTechniques Characteristics Advantages Disadvantages Examples Key factors
1. Visualinterpretation
One band (or VI) from date1image as red, the same band(or VI) from date2 image asgreen, and the same band (orVI) from date3 image as blueif available. Visuallyinterprets the colourcomposite to identify thechanged areas. An alternativeis to implement on-screendigitizing of changed areasusing visual interpretationbased on overlaid images ofdiff. dates
Human experienceand knowledge areuseful during visualinterpretation. Two orthree dates of imagescan be analysed atone time. The analystcan incorporatetexture, shape, sizeand patternsintovisualinterpretation tomake a decision onthe LULC change
Cannot providedetailed changeinformation.The resultsdepend on theanalyst’s skill inimageinterpretation.Time-consuming anddifficulty inupdating theresults
Land-usechange,forestchange ,monitoringselectivelyloggedareas andland coverchange
Analyst’sskill andfamiliarity withthe studyarea
Category VII. Other change detectiontechniques
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 surfacetemperature, 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)
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
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
Comparing the Different Techniques
Two types of change detection either detect binary change/non-change, orthe detailed “from-to” change between different classes.
Different change detection techniques are often tested and comparedbased on an accuracy assessment or qualitative assessment.
no single method is suitable for all cases.
A combination of two change detection techniques can improve thechange detection results (image differencing/PCA, NDVI/PCA,PCA/CVA).
The most common change detection methods: image differencing, PCA,CVA, and post-classification comparison.
Global change analyses and imageresolution
For change detection at high or moderate spatial resolution: use LandsatTM, SPOT, or radar.
For change detection at the continental or global scale, use coarseresolution data such as MODIS and AVHRR.
AVHRR has daily availability at low cost; it is the best source of data forlarge area change detection.
NDVI and land surface temperatures derived from MODIS or AVHRRthermal bands are especially useful in large area change detection.
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 theycan 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 fuzzyset and fuzzy membership functions to replace the thresholds.
However, threshold selection is simple and intuitive, so it is still the mostextensively applied method for detecting binary change/no-change information.
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 areoften problematic to collect.
The error matrix is the most common method for accuracy assessment. Toproperly 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.
Summary and Recommendations
The binary change/no-change threshold techniques all have difficulties indistinguishing 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 asuitable 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 changedetection methods can produce higher quality change detection results.
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