image fusion for land cover change detection

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Image fusion for land cover changedetectionYu Zeng a , Jixian Zhang a , J.L. van Genderen b & Yun Zhang ca Chinese Academy of Surveying and Mapping , 28 LianhuachixiRoad, Beijing 100830, P.R. Chinab Faculty of Geo-Information Science and Earth Observation(ITC), University of Twente , PO Box 6, 7500 AA Enschede, TheNetherlandsc Department of Geodesy and Geomatics Engineering , Universityof New Brunswick , Fredericton, New Brunswick, Canada E3B 5A3Published online: 18 May 2010.

To cite this article: Yu Zeng , Jixian Zhang , J.L. van Genderen & Yun Zhang (2010) Image fusionfor land cover change detection, International Journal of Image and Data Fusion, 1:2, 193-215, DOI:10.1080/19479831003802832

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International Journal of Image and Data FusionVol. 1, No. 2, June 2010, 193–215

Image fusion for land cover change detection

Yu Zenga*, Jixian Zhanga, J.L. van Genderenb and Yun Zhangc

aChinese Academy of Surveying and Mapping, 28 Lianhuachixi Road, Beijing 100830,P.R. China; bFaculty of Geo-Information Science and Earth Observation (ITC),

University of Twente, PO Box 6, 7500 AA Enschede, The Netherlands; cDepartment ofGeodesy and Geomatics Engineering, University of New Brunswick, Fredericton,

New Brunswick, Canada E3B 5A3

(Received 30 August 2009; final version received 15 November 2009)

Image fusion is an effective approach for enriching multi-source remotely sensedinformation. In order to compensate the insufficiency of single-source remotesensing data during the change detection process, and to combine the comple-mentary features from different sensors, this article presents the results ofdifferent temporal synthetic aperture radar (SAR) and optical image fusionalgorithms for land cover change detection. First, pixel-level image fusion isperformed, and its applicability for change detection is assessed by a quantitativeanalysis method. Second, change detection at the decision-level is put forward,which comprises object-oriented image information extraction from high-resolution optical image, multi-texture feature and support vector machines(SVM)-based information extraction from single band and single polarisationSAR image, and hard- and soft-decision based change detection. Changedetection uncertainty is also evaluated at the scale of pixel using the extendedprobability vector and probability entropy model. The imagery used in this imagefusion research was SPOT5 and RADARSAT-1 SAR data.

Keywords: hard-decision; soft-decision; texture analysis; grey level co-occurrencematrix; fractal

1. Introduction

Image fusion is an effective approach for enriching multi-source remotely sensedinformation (Hall 1992, Pohl and van Genderen 1998). When images with a similaracquisition time are used, the expected result is to obtain a fused image that retains thespatial resolution from the panchromatic image and colour content from the multi-spectralimage; when images with different dates are used, the main purpose is to detect the changesover a period of time. In the case that image fusion is used for the latter, most previousstudies have used the data from the sensors with the same working mode, for example,different temporal optical images from the same or different sensors have been used.Optical images and data from synthetic aperture radar (SAR) sensor are complementaryin terms of capability of data acquisition and image characteristics. When they are usedtogether, the deficiency of single remote sensing data source during change informationextraction can be compensated, and the complementary features from different sensors

*Corresponding author. Email: [email protected]

ISSN 1947–9832 print/ISSN 1947–9824 online

� 2010 Taylor & Francis

DOI: 10.1080/19479831003802832

http://www.informaworld.com

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can be combined. To address this need, a study of land cover change detection by fusionof different temporal SAR and optical imagery has been carried out.

First, a series of pixel-based image fusion experiments with the purpose of changedetection were conducted and a quantitative evaluation method is presented. Based onthe abovementioned analysis, a decision-based image fusion methodology for changedetection is put forward. In order to realise a high accuracy change information extractionat the decision-level, land cover classification for these two types of imagery wereperformed, respectively, while taking the different imaging mechanisms and informationcharacteristics into account. During this process, uncertainty of the classification resultsand the change detection result at the scale of pixel are also analysed and evaluated.

2. Study area and data

SPOT5 imagery has a spatial resolution of 2.5m; it can supply abundant data for large-scale image mapping and environmental monitoring. This makes it the major remotesensing data source for large-scale land use survey and land use information systemupdated by the Ministry of Land and Resources of China. Therefore, choosing a SPOT5image as the optical image data source for land use/land cover change detection hasa practical significance. At the time when this research was carried out, the operationalradar satellite systems were: the European ERS-1/2 and ENVISAT-1, the CanadianRADARSAT-1 and the Japanese ALOS PALSAR. Because RADARSAT-1 can providesteady data and its fine beam image has higher spatial resolution, it was selected as theSAR data source for this research.

Test site is located at Jinnan district, Tianjin, China. This area has experienced rapideconomic development. A SPOT5 Pan/XS image acquired on 16 October 2004, and aRADARSAT-1 fine beam image acquired on 19 October 2005 were used for this study.Additionally, a digital elevation model (DEM), topographic maps, land use maps, annualland use change investigation data, fieldwork results, etc. were collected as auxiliary data.

3. Methods

3.1 Pixel-level image fusion for change detection and the evaluation

Pixel-based image fusion methods are the most widely used methods in the field of imagefusion, especially for optical imagery. Data co-registered with sub-pixel accuracy aremergedwith each other. When compared with feature-based and decision-based image fusionmethods, these methods can make the best use of the original imagery and well retain itsdetailed information. Based on this fact, pixel-based image fusion analysis with the purposeof change detection was carried out using SPOT5 image and RADARSAT-1 image.

Speckle suppression was applied on the RADARSAT-1 image before the image fusion.Fifteen image fusion algorithms were tested: image difference, multiplicative, intensity-hue-saturation (IHS) transformation, Brovey transformation, colour fidelity transforma-tion, weighted fusion, smoothing filter-based intensity modulation (SFIM), block-basedsynthetic variable ratio (Block-SVR), high-pass filtering (HPF), wavelet theory basedfusion, multi-band principal component (PC) transformation, principal componentanalysis (PCA) differentia, differentia PCA, pseudocolour composition and componentsubstitution. Algorithms selected here cover almost all commonly used image fusionalgorithms applicable for change detection, where image difference is performed based

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on the panchromatic band of the SPOT5 image and the RADARSAT-1 image, PCAdifferentia and component substitution is performed based on RADARSAT-1 image andthe fusion result of SPOT5 pan and XS, pseudocolour composition is performed based onSPOT5 pan, SPOT5 XS and RADARSAT-1 image, and the remaining algorithms areperformed based on the SPOT5 XS and RADARSAT-1 images. In terms of the bandselection for the algorithms that allow three multi-spectral input bands to be fused(e.g. IHS, Brovey), according to the Optimum Index Factor (OIF) developed by Chavezet al. (1982), band 4(R)1(G)3(B) of the SPOT5 image with the maximum OIF valuewas selected. This combination has the additional benefit that it can give the effect of atrue colour composition.

Different from the evaluation methods using mean, deviation, entropy, mean gradient,correlation coefficient, and so on for assessing image fusion algorithms with the purposeof information enhancement, this article proposes an evaluation method for image fusionalgorithms with the purpose of change detection. This method integrates spectral featuresand spatial texture features which constitute the most important visual content of animage. The main idea of this method is to compare the image similarity between theregions where changed parcels are located and the regions where there are no changesusing the similarity measure. If the difference is big enough, it means that the changed areacan be extracted using a template for further analysis; otherwise, it implies that the fusionalgorithm goes against change detection. The similarity measure is calculated according tothe distance of the integrated feature vector F between two images to be compared, where,F¼ {Fspectral,Ftexture}. F can be further rewritten as F31� 1¼ {x1, x2, x3, . . . , x9, y1, y2,y3, . . . , y24}

T, where x1–x9 are the mean, median and standard deviation (SD) of eachband, which are selected to represent the spectral feature of the fused image; y1–y24 themean and SD of homogeneity, contrast, entropy and correlation of each band derivedfrom grey level co-occurrence matrix (GLCM) and selected to represent the texturalfeature of the fused image. GLCM is an effective texture analysis method, andomnidirectional textural features are used by considering the input image characteristics.In order to make the similarity measures comparable, interior normalisation is employedwithin each feature component by Gaussian normalisation, and then exterior normal-isation is employed between spectral feature and textual feature by extremum normal-isation. After normalisation, the similarity measure between image Q and I can bedefined as:

DðQ, I Þ ¼ �spectraldspectralðQ, I Þ þ �texturedtextureðQ, I Þ ð1Þ

where dspectral and dtexture are the Euclidian spectral distance and textural distance betweenQ and I, and �spectral and �texture the weight.

3.2 Land cover classification

In decision-level image fusion, accurate information obtained from each input image is thebasis for further joint decision. To address this need, the studies on land cover informationextraction from each type of imagery are carried out in this section.

3.2.1 Object-oriented image analysis for SPOT5 image

While high spatial resolution imagery provides more detailed information on groundobjects, it increases the intra-class spectral variability. Thus the traditional pixel-based

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classification approaches are no longer applicable, and object-oriented image analysistechniques have shown their effectiveness under these circumstances. In object-orientedimage analysis, spectral information, shape, size, texture as well as contextual informationcan be utilised together to perform information extraction at the level of objects(eCognition 4.0 User Guide 2002, Benz et al. 2004). For this technique, Definiens imageanalysis software provides sound solutions. An object-based approach to image analysis iscomposed of four steps:

(1) multi-resolution segmentation to generate image objects and to create objecthierarchy;

(2) image object feature extraction and parameter assessment;(3) classification which uses iterative steps to classify image objects; and(4) accuracy analysis and evaluation.

3.2.2 Multi-textural analysis for RADARSAT-1 image based on support vector machines

Texture is an inherent spatial characteristic of an image. Because SAR backscatter issensitive to the type, orientation, homogeneity and spatial relationship of ground objects,it represents certain texture features in the image. Due to the influence of speckle noise,and limited information in single band and single polarisation SAR imagery, texture playsan important role for class discrimination (Guo 2000). Nowadays, there are four groups oftexture analysis methods (Tuceryan and Jain 1993), which are statistical, geometrical,model-based and signal processing. Each method has its own characteristics andcapabilities, and there is no general agreement on an overall best analysis method,which outperforms all the others on various tasks. Among these methods, the statisticalmethods based on GLCM appear to be the most commonly used and are the mostpredominant; while they use spatial correlated characteristic of grey values for texturedescription, they are not sensitive to SAR speckle noise (Soh and Tsatsoulis 1999, Clausi2000, Franklin 2001, Maillard 2003, Clausi and Yue 2004). The fractal model is a model-based method which makes use of the self-similarity of complex phenomena that occurin nature and has specific capability in the description of spatial structure informationand detailed texture features. Additionally, it takes multi-scale effects of spatial patternsinto consideration (Chaudhuri and Sarkar 1995). In view of this, texture features derivedfrom GLCM and fractal model were studied and combined together for SAR imageryinformation extraction. In order to keep the original texture information, specklesuppression is not advised in SAR image before texture analysis (Clausi 2000, ERDASField Guide 2005).

Several variables including the number of quantisation levels, the number and typeof measurements, the window size to analyse, the pixel pair sampling distance andorientations need to be considered in order to properly use the GLCM-based method forimage texture analysis. Different parameter selection and combination lead to differenttexture features and different classification accuracy. In this research, a 64-levelquantisation was adopted because of its computational efficiency and sufficiency fortexture mapping, and directional invariant texture measures which are the average amongtexture measures for four directions (0�, 45�, 90� and 135�) were used. Different groundobjects having different scales determine textures. In the GLCM-based method, thewindow size to be processed determines the ability to capture the texture features atdifferent spatial extents. In general, a smaller window size could be easily influenced by

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SAR noise, while it can describe small texture features; a larger window size could describe

the whole scenery better, is not easily influenced by SAR noise, but cannot describe small

texture features. Therefore, a method to extract multi-scale texture features is proposed

in this research, which is composed of two steps:

(1) selection of the number and type of measurements to be analysed based on the

commonly used seven statistics and(2) feature image selection for different scale ground objects. Here, the semi-variogram

model is introduced to assist the estimation of processing window size.

In the first step, classify the texture measurements into three groups according to the

structure they reveal and their inter-feature correlations. The first group contains

homogeneity, angular second moment and entropy, which are the homogeneity statistics.

The second group contains SD, contrast and dissimilarity, which measure the degree of

smoothness of the texture. Within each group, features are highly correlated. The third

group contains only correlation statistics, which is an independent measure and not

correlated with any of the other textual statistics. Second, choose the ‘stable’ statistics,

which is not sensitive to pixel pair sampling distance under a given statistical window

size. Entropy, SD and correlation are selected after the abovementioned analysis.

Different texture features have their own interpretation ability for different ground

objects, and different ground objects have a different scale. In the second step, by

experiments and with reference to the estimated scale of ground objects by using the semi-

variogram, entropy processed by window size 13 was selected for recognition of residential

area, SD processed by window size 21 was selected for recognition of water body and

bare land, and correlation processed by window size 11 was selected for recognition of

vegetation.Fractal dimension is the key parameter describing a fractal surface. Among the

methods for the computation of fractal dimension, the differential box-counting (DBC)

model (Chaudhuri and Sarkar 1995) was selected in this research because of its accuracy

and the capability to cover the full dynamic range of fractal dimension. However, due to

the fact that fractals in the natural world are not strict fractals in mathematical terms,

whilst they present an approximate self-similarity in statistics, many images with obviously

different textures have a close fractal dimension. Consequently, multi-fractal analysis

and second-order statistic lacunarity were further studied as supplements to fractal

dimension. Multi-fractal dimension and lacunarity are derived from the box counting

algorithms. In this research, using image samples of typical ground objects, and by plotting

the multi-fractal q–D(q) curve and the lacunarity L–C(L) curve, parameters for

extracting multi-fractal feature image and lacunarity feature image were quantitatively

determined. When q¼ 8 and �8, there is a good separability among ground objects for

multi-fractal feature, and when L¼ 2, there is a good separability among ground objects

for lacunarity.Using small training samples, support vector machines (SVM) can produce reliable

classifications when the feature space is nonlinear and high-dimensional. In addition,

unlike spectral features, texture features do not necessarily have normal distributions

(Duda et al. 2001). Hence, the nonparametric classifier SVM was selected for SAR texture

analysis, where the radial basis function (RBF) was chosen as the kernel function and the

one-against-one technique was adopted. Multi-scale GLCM features and fractal model-

based features were incorporated and analysed by SVM.


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3.3 Decision-level image fusion for change detection and the uncertainty

3.3.1 Soft-decision change detection based on rules

Decision-level is the highest image fusion level, where independent information ordeclarations of identity acquired from each sensor are combined via a fusion process.For change detection, decision-level image fusion can avoid the normalisation processrequired when using different temporal images; it can not only determine the spatial extentof changes, but can also provide ‘from–to’ information of change types. The limitationof this method is that individual classification is needed and the change detection result isdirectly determined by classification results. By traditional post-classification comparison,change is often overestimated because of error propagation (Fuller et al. 2003, Gallego2004). In China, land use/cover change usually occurs at the urban fringe areas. With thedevelopment of the economy and population growth, the Chinese government puts mostemphasis of land use change monitoring on urban expansion and farmland reduction.Based on the research focus, a soft-decision approach based on the rules for changedetection is proposed in this study. It takes the status of pixels, which are changedor unchanged, change trajectory, as well as shape, size and spatial location of changesinto consideration, to decrease change overestimation by evaluating the rationality of thedetected changes. For class Ci, here, C1¼ ‘built-up area’, C2¼ ‘water’, C3¼ ‘vegetation’,C4¼ ‘bare land’, Num denotes the number of detected changes; and TðCi, Cj Þ the changetrajectory. Let N refers to the case that ‘there is no change’, W the ‘wrong classificationand applying masking’ and Y the ‘correctly detected changes’. Series of logic rulesemployed in turn are:

Rule 1: if Num ¼ 0 then N:Rule 2: if Num ¼ 1 and TðCi, Cj Þði 6¼ 1; i 6¼ j Þ then Y:Rule 3: if Num ¼ 1 and TðC1, Cj Þ ð j 6¼ 1Þ then W:Rule 4: River, lake, canal and its affiliated works (including built-up area and vegetation)

are regarded as N.Rule 5: Land use types in between large area farmlands are regarded as N.Rule 6: Isolated 3� 3 detected change regions are regarded as W.

Rule 1 means that if the pixel is classified as the same land cover type in the two dates,the pixel is regarded as correctly classified and there is no change. At urban fringe areas,most land use/cover changes are caused by urban growth. Thus, change to built-up areafrom other land use/cover types can be regarded irreversible. Rules 2 and 3 are establishedbased on this assumption. Rule 2 implies that if the change is not from built-up areato other types, it is regarded correctly detected; rule 3 indicates that if the change frombuilt-up area to other types is detected, the change is unlikely to have happened and pixelsare masked for further analysis. The meaning of rules 4–6 is self-explanatory; they areperformed by spatial analysis. By separating the unchanged areas, falsely detected changesand possible changes, the change overestimation can be reduced, and accordingly, thechange detection accuracy is improved.

3.3.2 Uncertainty in the change detection result

Understanding the nature and spatial distribution of uncertainty when analysing changedetection results can reduce the risk of making wrong decisions based on uncertain data(Shi and Ehlers 1996). Uncertainty of change in the detection result is the propagation

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of uncertainty of classification results. When there are limited training samples, we develop

the idea of ‘extended probability vector’ (Xu and Krzyzak 1992, Bo and Wang 2003),

then extend the uncertain evaluation method which was originally based on ‘probability

vector’ generated during maximum likelihood (ML) classification (Foody et al. 1992,

Goodchild et al. 1992, Shi and Ehlers 1996) to object-oriented fuzzy classification and

SVM-based classification. This method is briefly described as follows.For object-oriented fuzzy classifier, a fuzzy membership vector, which is resembling

‘probability vector’, is first created based on the membership of each pixel x to each

class Ci:

½mðC1=xÞ,mðC2=xÞ, . . . ,mðCi=xÞ, . . . ,mðCM=xÞ�T

ð2Þ

Unlike posterior probabilities generated during ML classification indicate the

probability that a pixel belongs to a class, fuzzy memberships generated during fuzzy

classification present the possibility that a pixel belongs to a class. Therefore, the

following transformation is applied to (1) to make it meet the requirements of probability

definition:

pmðCi=xÞ ¼mðCi=xÞPMi¼1 mðCi=xÞ

ð3Þ

where M is the number of classes. The ‘extended probability vector’ can then be

constructed:

½ pmðC1=xÞ, pmðC2=xÞ, . . . , pmðCi=xÞ, . . . , pmðCM=xÞ�T

ð4Þ

For SVM using one-against-one technique to tackle multi-class division, a vote vector

is created according to the votes of each pixel obtained for each class. The ‘extended

probability vector’ is then constructed by applying the transformation (3). After that,

the classification uncertainty for each pixel is measured by probability entropy, which can

be derived from ‘extended probability vector’:

Hð pÞ ¼ �XM

i¼1

pðCi=xÞ� log2 pðCi=xÞ ð5Þ

Different temporal image classifications can be regarded as independent. In other

words, the posterior probability vector of a pixel at time T2 is calculated irrespective of the

class or feature vector at the previous time T1. Thus, according to Shannon’s information

theory, there is

H ¼ �XM

j¼1

XM

i¼1

Pij log2ðPijÞ ¼ �XM

i¼1

PðCi,T1=XT1Þ log2ðPðCi,T1=XT1ÞÞ

�XM

j¼1

PðCj,T2=XT2Þ log2ðPðCj,T2=XT2ÞÞ ð6Þ

Equation (6) indicates that change detection uncertainty is the sum of classification

uncertainty in the two dates. The range of H is from 0 to log2ðMMÞ, which indicates

uncertainty varies from absolute certain to absolute uncertain.


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4. Results and discussion

4.1 Applicability analysis on pixel-based image fusion for change detection

Among the 15 algorithms mentioned above used for pixel-level image fusion, sevenrepresentative algorithms, namely: PC transformation, IHS transformation, Broveytransformation, HPF, wavelet fusion, SFIM and multiplicative were selected forquantitative analysis. With reference to the ground truth data, calculate the similaritydistance between the parcels where land cover change occurs and parcels where there is noland cover change, as well as the similarity distance between the changed parcels andtypical land cover regions, e.g. farmland, built-up area and water body on the fused imageusing the proposed method. Typical land cover change trajectories in the test areaas shown in Table 1 were analysed in this research. An example is given for analysis onwavelet fusion result (Figure 1 and Tables 2 and 3).

When we look at the fused result in Figure 1(b), we can see that by visualinterpretation, it is hard to separate the changed parcels (highlighted in red) from theunchanged regions. From Table 3, we can see that the similarity distance between thechanged parcel and its unchanged neighbours is quite close, which is 0.354, 0.064 and0.251, respectively, whilst the similarity distance between the changed parcel and therepresentative farmland, built-up area and water body is 1.831, 1.435 and 0.802,respectively, in this example. In comprehensive comparison on the similarity distancesamong the image parcel samples, we found that only when the distance is greater than 1.0,there exist obvious differences between two parcels. It implies that on the fused image,around the changed parcels, there are many unchanged regions with similar imagefeatures. It further indicates that it is hard to extract the changed regions employingother techniques, such as template analysis, etc. The same conclusion can be reached byanalysing other land cover change trajectories.

Experimental results showed that quantitative analysis verified the judgement receivedby visual interpretation; for most land cover change trajectories, it is difficult to locate thechanged parcels on the fused image. It is noted that for these two types of data, imagefusion at this level is not applicable for change detection. Based on the above analysis,a higher image fusion processing level, decision-level, is then put forward for analysis,which comprises the following sections.

4.2 Information extraction from SPOT5 image

In the object-oriented classification, both SPOT5 pan and SPOT5 XS wereused. A Normalized Difference Vegetative Index (NDVI) image was produced as

Table 1. Typical land cover change trajectory.

From To

Water body Bare landGrassland Bare landFarmland Built-up areaGrassland Built-up areaBare land Built-up area

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an additional band. In order to reduce information loss, smoothing, filtering and imagefusion were not conducted. A two-level classification scheme was adopted, which is listedin Table 4. Land use maps, annual land use change investigation data and filed surveyresults were used for accuracy evaluation.

After experiments, a network of three layers is constructed according to features ofground objects. The setting of parameters is given in Table 5. In each image object layer,spectral features are first used for object separation; for objects with close spectral features,shape, texture, and contextual information are further used.

The classified result is illustrated in Figure 2. By accuracy evaluation, for thesecond-level classification, the overall accuracy is 88.53% with Kappa coefficient0.861; for the first-level classification, the overall accuracy is 90.19% with Kappacoefficient 0.872.

Figure 1. Original images and the fused result: (a) SPOT5 pan, 2004; (b) SPOT5 XS (4R/1G/3B),2004; (c) RADARSAT-1, 2005; and (d) wavelet fusion result (Daubechies wavelet, two-leveldecomposition) superimposed by ground truth of changed parcels in red (annual land use changeinvestigation data).


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Table

2.Interiornorm

alisationforeach

feature

componentin

thechangetrajectory

from

waterto

bare

land.

Parcel

type

Image

parcel

Size

(width�

height)

Band

no.

Spectralfeature

Norm

alisedspectralfeature

Mean

Median

SD

Mean

Median

SD

Changed

58�56

15.763

4.000

7.713

0.410

0.406

0.478

226.744

26.000

10.540

0.414

0.414

0.498

361.481

61.000

7.613

0.389

0.384

0.499

Unchanged

123�23

12.837

3.000

2.446

0.400

0.402

0.387

216.970

17.000

2.882

0.387

0.389

0.389

356.140

56.000

2.462

0.347

0.342

0.403

Unchanged

224�23

14.984

4.000

7.350

0.408

0.406

0.472

235.839

35.000

6.117

0.439

0.438

0.435

377.080

77.000

4.651

0.512

0.518

0.444

Unchanged

330�23

13.390

3.000

4.287

0.402

0.402

0.419

226.713

26.000

4.995

0.414

0.414

0.419

356.935

57.000

2.318

0.353

0.351

0.400

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Farm

land

75�75

1106.759

106.000

9.216

0.785

0.791

0.504

2172.590

172.000

11.979

0.811

0.815

0.519

393.557

93.000

7.322

0.642

0.652

0.494

Built-uparea

65�68

182.873

79.000

29.784

0.697

0.689

0.862

2115.881

112.000

35.742

0.656

0.650

0.859

3113.030

109.000

27.101

0.796

0.786

0.865

Waterbody

63�67

12.685

3.000

1.909

0.399

0.402

0.378

214.078

14.000

2.388

0.379

0.381

0.381

370.731

71.000

2.125

0.462

0.468

0.396

(continued

)


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Table

2.Continued.

Texturalfeature

Norm

alisedtexturalfeature

Homogeneity

Contrast

Entropy

Correlation

Homogeneity

Contrast

Entropy

Correlation

Imageparcel

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

Mean

SD

0.324

0.159

22.874

58.214

1.972

0.320�2.945

7.039

0.594

0.765

0.417

0.509

0.369

0.793

0.593

0.421

0.258

0.121

23.094

41.821

2.120

0.117�47.176

145.695

0.537

0.590

0.395

0.464

0.554

0.478

0.241

0.742

0.291

0.132

24.519

46.168

2.138

0.103�95.450

257.232

0.698

0.670

0.311

0.423

0.604

0.440

0.165

0.840

0.282

0.125

24.939

15.403

1.999

0.176�1.682

2.175

0.518

0.484

0.421

0.337

0.422

0.506

0.607

0.399

0.249

0.108

30.517

19.439

2.079

0.142�8.257

9.878

0.517

0.408

0.423

0.362

0.426

0.572

0.625

0.386

0.210

0.119

94.911

63.224

2.067

0.147�4.972

8.977

0.487

0.529

0.653

0.538

0.378

0.630

0.611

0.400

0.414

0.126

10.261

41.346

1.963

0.208�5.972

6.989

0.758

0.496

0.387

0.441

0.353

0.571

0.557

0.421

0.334

0.122

13.685

38.786

2.076

0.165�17.526

25.771

0.705

0.600

0.360

0.450

0.416

0.663

0.534

0.428

0.309

0.117

17.046

39.627

2.082

0.126�19.304

29.616

0.747

0.510

0.274

0.379

0.425

0.536

0.540

0.436

0.327

0.129

10.713

5.919

2.020

0.164�3.252

3.543

0.599

0.518

0.388

0.299

0.463

0.483

0.589

0.405

0.336

0.119

8.502

5.328

2.071

0.139�15.007

17.896

0.709

0.561

0.341

0.297

0.402

0.559

0.559

0.407

0.180

0.121

51.418

25.818

2.070

0.147�4.354

4.167

0.408

0.554

0.442

0.286

0.388

0.627

0.614

0.391

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0.146

0.090

67.184

50.834

2.176

0.059�25.490

55.804

0.271

0.194

0.520

0.479

0.761

0.274

0.330

0.645

0.144

0.092

67.506

51.688

2.179

0.054�16.023

27.304

0.284

0.182

0.561

0.509

0.736

0.233

0.548

0.432

0.131

0.082

86.518

66.086

2.184

0.045�18.815

35.385

0.279

0.141

0.613

0.557

0.753

0.192

0.543

0.447

0.215

0.126

62.836

104.212

2.151

0.096�36.826

93.865

0.396

0.496

0.510

0.693

0.714

0.347

0.198

0.819

0.173

0.113

85.417

114.296

2.164

0.078�41.092

145.248

0.348

0.474

0.627

0.796

0.690

0.324

0.301

0.741

0.203

0.121

64.583

103.557

2.151

0.097�47.693

121.089

0.468

0.557

0.506

0.809

0.646

0.413

0.401

0.599

0.198

0.132

212.133

116.171

1.998

0.186�0.186

0.183

0.364

0.547

0.857

0.741

0.419

0.527

0.625

0.390

0.197

0.128

129.546

76.231

2.031

0.167�1.431

1.660

0.400

0.685

0.792

0.622

0.277

0.670

0.693

0.364

0.182

0.120

104.687

58.661

2.044

0.155�1.979

2.124

0.413

0.539

0.701

0.507

0.306

0.662

0.626

0.388


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Table

3.Exteriornorm

alisationandsimilarity

calculationforsample

parcelsin

thechangetrajectory

from

waterto

bare

land.

Changed

parcel

Unchanged

parcel

1Unchanged

parcel

2Unchanged

parcel

3Farm

land

Built-up

area

Water

body

Spectraldistance

0.186

0.204

0.149

0.862

1.007

0.222

Norm

alisedspectraldistance

0.043

0.064

0.000

0.831

1.000

0.085

Texturaldistance

1.104

0.894

1.065

1.573

1.189

1.381

Norm

alisedtexturaldistance

0.310

0.000

0.251

1.000

0.435

0.717

Sim

ilarity

distance

0.353

0.064

0.251

1.831

1.435

0.802

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4.3 Information extraction from RADARSAT-1 image

Seven texture features were constructed, which are:

Feature 1: Correlation (processed by window size 11);Feature 2: SD (processed by window size 21);Feature 3: Entropy (processed by window size 13);Feature 4: Fractal dimension;Feature 5: Multi-fractal dimension (q¼ 8);Feature 6: Multi-fractal dimension (q¼�8); andFeature 7: Second-order statistic lacunarity (L¼ 2).

From the correlation matrix (Table 6), we can see that the texture feature has a lowcorrelation coefficient with respect to each other. It indicates that they are independent,and they can function jointly for further analysis.

Set features 1–7 as input of SVM, after taking samples, classify RADARSAT-1 imageinto built-up area, vegetation, water body and bare land. RBF was chosen as the kernelfunction of SVM, with penalty parameter C¼ 100, and �¼ 0.25. The classification resultis shown in Figure 3. Comparison on classification performance of different features wasperformed as presented in Tables 7 and 8.

From Tables 7 and 8, we can see that with overall accuracy of 69.8926% and Kappacoefficient of 0.4916, the integrated seven texture features provide better classificationaccuracy than any other feature combinations, and the improvement is significant,which is 37.76787 and 57.63155 when compared with multi-scale GLCM-based features

Table 4. Land cover classification scheme.

Level 1 Level 2

Built-up area BuildingBuilding shadowRoad

Vegetation ForestGrasslandCropland (including irrigated field and nonirrigated field)Vegetable plot (including vegetable greenhouse)

Water body River and canalLake and pond

Bare land

Table 5. Parameter setting for multi-resolution segmentation.

Level Scale Colour Shape

Composition of shape

Smoothness Compactness

Level 3 110 0.8 0.2 0.5 0.5Level 2 90 0.9 0.1 0.4 0.6Level 1 40 0.8 0.2 0.4 0.6


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and fractal model-based features, respectively. Besides, we can see that the classificationaccuracy of multi-scale GLCM-based features is higher than fractal model-based features;by Z-statistic between them, which is 19.93228, we can further see that the former is moreeffective than the latter in terms of accuracy improvement.

The classification performance between traditional maximum likelihood classifier(MLC) and SVM was also compared using the same integrated features (features 1–7)as input. The overall accuracy of SVM classification is higher than that obtained usingMLC by 10%, Kappa coefficient is improved from 0.3816 to 0.4916.

4.4 Change detection result and the evaluation

By post-classification comparison, the change detection result was obtained as shownin Figure 4. Given the classification code 1 as bare land, 2 as built-up area, 3 as vegetation

Figure 2. Object-oriented classification result of SPOT5 image.

Table 6. Correlation matrix of multi-scale GLCM-based features and fractal model-based features.

Correlation Feature 1 Feature 2 Feature 3 Feature 4 Feature 5 Feature 6 Feature 7

Feature 1 1.0000 0.5349 �0.1568 �0.1820 �0.2498 �0.0740 �0.2217Feature 2 0.5349 1.0000 0.2902 �0.3043 �0.3096 0.0366 �0.0192Feature 3 �0.1568 0.2902 1.0000 �0.0542 0.1876 �0.0638 0.4268Feature 4 �0.1820 �0.3043 �0.0542 1.0000 0.4278 �0.2024 �0.1053Feature 5 �0.2498 �0.3096 0.1876 0.4278 1.0000 �0.2545 0.1756Feature 6 �0.0740 0.0366 �0.0638 �0.2024 �0.2545 1.0000 0.0860Feature 7 �0.2217 �0.0192 0.4268 �0.1053 0.1756 0.0860 1.0000

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Figure 3. Classification result of RADATSAT-1 image.

Table 7. Features used for classification and the accuracy.

Group no. Features for classificationOverall

accuracy (%)Kappa

coefficient

1 Multi-scale GLCM-based features þ fractalmodel-based features

69.8926 0.4916

2 Multi-scale GLCM-based features 67.2711 0.46803 Fractal model-based features 66.8600 0.44524 Multi-scale GLCM þ fractal dimension 68.1001 0.4690

Multi-scale GLCM þ multi-fractal features 68.2594 0.4700Multi-scale GLCM þ lacunarity 68.5601 0.4751

5 Multi-scale GLCM þ fractal dimension þmulti-fractal features

68.5857 0.4789

Multi-scale GLCM þ fractal dimension þlacunarity

69.4600 0.4803

Multi-scale GLCM þ multi-fractalfeatures þ lacunarity

69.4831 0.4889

6 SAR intensity image 54.4739 0.2395SAR backscattering coefficient image 45.9268 0.2139

Table 8. Comparison of Z-statistic.

Z-value Group 1 Group 2 Group 3

Group 1 – – –Group 2 37.76787 – –Group 3 57.63155 19.93228 –


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and 4 as water body, the black in this figure represents no change, the green representspositive difference values and the red represents negative difference values.

In order to build an error matrix, the amount of samples was generated fromthe multinomial distribution (Khorram et al. 1999). For four classes, when the desiredconfidence level was selected to be 85%, and the desired precision was set at 7.5%, a totalof 197 samples would be at least required. Three times the calculated sample size were usedin the accuracy assessment. Special effort sampling approach was employed, with 67%sampling effort dedicated to the changed area, and 33% sampling effort dedicated to theunchanged area. The size of each sample is 3� 3 pixel. The collapsed change detectionerror matrix of post-classification comparison is listed in Table 9. The overall accuracyof the change detection is 62.7%. The omission error is 6.2%, and the commission erroris 31.1%.

The spatial distribution of uncertainty in classification results and the change detectionis illustrated in Figure 5. For classification results, the range of probability entropy is from0 to 2; for change detection, the range of probability entropy is from 0 to 4. When entropyvaries from 0 to its maximum, it indicates uncertainty varies from absolute certain

Figure 4. Change detection result by post-classification comparison.

Table 9. Collapsed change detection error matrix of post-classificationcomparison.

Reference data

Sum in rowUnchanged Changed

Classification data Unchanged 163 37 200Changed 187 213 400

Sum in column 350 250 600

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to absolute uncertain. The frequency distribution of uncertainty was further computedby dividing the range of entropy into 10 intervals and by counting the number of pixelsfalling in between each interval (Tables 10–12).

In Figure 5(a), we can see that the SPOT5 image has a higher classification certainty,especially for built-up areas; some bare land and harvested farmland have lower certaintybecause of their spectral similarity. The statistical results in Table 10 show that 92.96%pixels in SPOT5 classified image are within the interval 0–1.0, which further indicates

Figure 5. Spatial distribution of uncertainty represented by probability entropy: (a) uncertaintyof SPOT5 image classification; (b) uncertainty of RADARSAT-1 image classification; and(c) propagated change detection uncertainty.

Table 10. Frequency distribution of classification uncertainty of 2004 SPOT5 image.

Interval ofentropy (D)

0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0 1.0–1.2 1.2–1.4 1.4–1.6 1.6–1.8 1.8–2.0

Pixel % 46.71 1.03 2.31 27.57 15.34 6.15 0.89 0.0 0.0 0.0Accumulative % 46.71 47.74 50.05 77.62 92.96 99.11 100.0 100.0 100.0 100.0


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a higher classification certainty. For RADARSAT-1 classified image, class centre pixelshave higher certainty compared with edge pixels, and uncertainty gradually decreasesfrom centre to edge; some white clusters were caused by classification error, e.g. a built-uparea on top left image was misclassified as water body. Statistical results in Table 11 showthat 63.63% and 18.38% pixels fall into intervals 0–0.4 and 1.0–1.4, respectively, it furtherindicates higher uncertainty in class edges and higher certainty in class centres inRADARSAT-1 classification; compared with Table 10, 81.62% pixels are within theinterval 0–1.0, which indicates a lower certainty compared with SPOT5 classification.Change detection uncertainty can be visualised in Figure 5(c). From Figure 5(c) and

Figure 6. Soft-decision change detection based on rules.

Table 11. Frequency distribution of classification uncertainty of 2005 RADARSAT-1 image.


0–0.2 0.2–0.4 0.4–0.6 0.6–0.8 0.8–1.0 1.0–1.2 1.2–1.4 1.4–1.6 1.6–1.8 1.8–2.0


Table 12. Frequency distribution of propagated change detection uncertainty.


0–0.4 0.4–0.8 0.8–1.2 1.2–1.6 1.6–2.0 2.0–2.4 2.4–2.8 2.8–3.2 3.2–3.6 3.6–4.0


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Table 12, it can be seen that because of error propagation, change detection uncertaintyis higher than each classification result; the main distribution interval changes from 0–1.0to 0–2.0 (with nearly 95% pixels). Taking the value range [0, 4] into consideration,the majority entropy values are located in left-to-middle part of the range, which indicatesthat the change detection result still has the acceptable certainty level.

Soft-decision change detection is illustrated in Figure 6. By employing the rules inturn on post-classification comparison results, part of false changes caused bymisclassification were eliminated. Statistics shows that after executing rules 1–3, detectedchanges decreased by 22.6%; after executing rules 4–6, detected changes decreased byanother 28.9%.

From Table 13, we can see that soft-decision approach generates improved changedetection result, the omission error is 6.5%, the commission error is 15.3%; compared withpost-classification comparison, the overall accuracy is improved from 62.7% to 78.2%,and the commission error is reduced by half.

The statistics of the area change of land cover types is given in Table 14. It can be seenfrom the table that from October 2004 to October 2005, the main land cover change is thereduction of vegetation, and the increase of built-up area and bare land. By furtheranalysing the SPOT5 classified image acquired in 2004, we found that for the change fromvegetation to built-up area, 20% of vegetation is cropland and the rest is grassland. Thisstudy area is located at the downstream region of the Haihe River. Because of its highannual mean relative humidity, plants grow very well, and bare land here usuallyrepresents the land which is just exploited as built-up area. The reduction of vegetation,and the increase of built-up area and bare land well reflects the fact and trend of urbanexpansion, and occupation of cropland, unused land, etc. which has happened or ishappening at urban fringe areas.

Table 14. Area change of land cover types.

2005

2004

Vegetation Built-up area Bare land Water body

Vegetation – 0.0 0.0 0.39Built-up area 149.0 – 7.5 4.8Bare land 173.6 0.0 – 18.8Water body 7.8 0.0 0.0 –

Note: Unit: hectare.

Table 13. Collapsed change detection error matrix of soft-decision change detection.

Reference data

Sum in rowUnchanged Changed

Classification data Unchanged 161 39 200Changed 92 308 400

Sum in column 253 347 600


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5. Conclusions

In this article, the methods and results of different temporal SAR and optical image fusionfor land cover change detection were presented. Results indicate that for most land coverchange trajectories, it is hard to locate the change by pixel-level image fusion. It alsoconcludes that decision-level image fusion can well satisfy the needs; by object-orientedimage analysis and multi-textural analysis based on SVM, more accurate land coverinformation can be obtained from each sensor for further joint decision; compared withhard-decision change detection, soft-decision approach effectively eliminates the over-estimated changes and improves the change detection accuracy.

Acknowledgements

This research was funded partially by Open Research Fund Program of the Key Laboratoryof Geomatics and Digital Technology, Shandong Province, China (SD040207), National HighTechnology Research and Development Program of China (2009AA122003), National NaturalScience Foundation of China (40801178), the Research Fund for the Doctoral Program of ITC andNSERC scholarship.

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image fusion for land cover change detection

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