Automatic Segmentation of High-resolution Satellite ... - CiteSeerX

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Automatic Segmentation of High-resolution Satellite Imagery by Integrating Texture, Intensity, and Color Features Xiangyun Hu, C. Vincent Tao, and Björn Prenzel

Abstract High spatial resolution satellite imagery has become an important source of information for geospatial applications. Automatic segmentation of high-resolution satellite imagery is useful for obtaining more timely and accurate information. In this paper, we develop a method and algorithmic framework for automatically segmenting imagery into different regions corresponding to various features of texture, intensity, and color. The central rationale of the method is that information from the three feature channels are adaptively estimated and integrated into a split-merge plus pixel-wise refinement framework. In the procedure for split-merge and refinement, segmentation is realized by comparing similarities between different features of sub-regions. The similarity measure is based on feature distributions. Without a priori knowledge of image content, the image can be segmented into different regions that frequently correspond to different land-use or other objects. Experimental results indicate that the method performs much better in terms of correctness and adaptation than using single feature or multiple features, but with constant weight for each feature. The method can potentially be applied within a broad range of image segmentation contexts.

Introduction High spatial resolution satellite imagery is becoming an important source of information for geospatial applications. Currently, panchromatic images with 61cm pixel resolution and multispectral images with 2.44 m pixel resolution are available (www.digitalglobe.com). The benefit of highresolution imagery is that it allows, through the use of a variety of analysis methods, for extraction of more detailed and accurate information than is possible with lower resolution imagery. Automatic segmentation of high-resolution satellite imagery is a new, potentially very useful information extraction method that allows for ready acquisition of ground truth information such as land-use, forest, and hydrologic information. Recently, segmentation of high-resolution satellite imagery has received considerable attention. Pesaresi and Benediktsson (2001) presented morphological transforms based on a geodesic metric for automatic segmentation. This was applied into segmentation of complex image scenes, such as aerial or high-resolution

Geospatial Information and Communication Laboratory, Department of Earth and Space Science and Engineering, York University, 4700 Keele St., Toronto, ON, Canada, M3J 1P3 ([email protected]; [email protected]; [email protected]). PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

satellite images. Another example is the segmentation of Ikonos images (Shackelford and Davis, 2003), which focuses on classification of dense urban objects. The method consists of a two-stage process: fuzzy pixel-based classification and object-based fuzzy logic classification. Roads and buildings were discriminated at a classification accuracy of 99 percent and 76 percent, respectively. A comparison study of segmentation of high-resolution satellite imagery has been carried out by Meinel and Neubert (2004). They evaluated the segmentation results from Ikonos panchromatic and multispectral images by using several programs (eCognition™, InfoPACK® and CAESAR®). Their evaluation indicated that the commercial software demonstrated better performance, although they generally do not take into account texture information. In remote sensing, a major category of the image segmentation method is pixel-based classification, which includes both statistical and clustering-based classification (Lillesand and Kiefer, 2000). One frequently used algorithm for image segmentation is feature space clustering. Two drawbacks of the method are: (a) that it does not make use of spatial information, and (b) the number of clusters cannot usually be obtained directly and automatically. Regionbased methods (e.g., region growing, region splitting, region merging, and combinations of these) utilize region homogeneity statistically and so tend to be less sensitive to noise. These methods are generally better than feature space clustering or thresholding approaches since they take into account both feature space and the spatial relation between pixels simultaneously (Cheng et al., 2001). In this paper, we take advantage of the split-merge approach to segment imagery into different homogeneous regions. By using the split-merge approach, the key problem is how to describe region features and how to measure the similarities (or homogeneities) of the neighbor regions. The aim of image segmentation is domain-independent partitioning of imagery into a set of visually distinct regions based on properties such as intensity (grey-level), texture, or color. Earlier research has focused on using relatively simple properties, for example, pixel intensity or color. For features that cannot be described easily, such as more complicated and natural image scenes, texture information is involved for segmentation. There is a large amount of literature on image

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segmentation based on the properties (Reed and Dubuf, 1993; Cheng et al., 2001; Zhang and Tan, 2002; Munoz et al., 2003). Important to note is that for complex land surfaces, particularly when sensed using multi-spectral highresolution satellite imagery, the image scene is usually not easily partitioned correctly using a single feature channel (property) due to the complicated nature of the image scene. Integrated processing of the multiple features is essential to obtaining more reliable segmentation. In this analysis, we endeavor to combine multiple features, including texture, intensity and color information, to achieve more reliable segmentation as compared to using only a single feature or property. There are some segmentation approaches that combine different features where, in the simplest case, each color band is processed separately to segment the color texture image (Caelli and Reye, 1993). Dubuisson-Jolly and Gupta (2000) used the maximum likelihood method to color and texture feature space separately, and then to combine the likelihoods in the two spaces using a particular function. This method was applied to the segmentation of color aerial imagery for the purpose of GIS updating. This method is supervised in that the number of classes, and the location of training samples need to be known in advance. Chen and Chen (2002) proposed a method for color-textured segmentation using feature distributions. They employed the algorithmic framework of split-merge plus pixel-wise refinement for unsupervised segmentation, similar to the Ojala and Pietikäinen’s method (1999). The algorithm solves the color texture segmentation problem by unifying color and edge features rather than simply extending grey-level texture analysis to color images (Caelli and Reye, 1996; Eom, 1999); or by simply analyzing only spatial interactions of colors in a given neighborhood (Mirmehdi and Petrou, 2000; Panjwani and Healey, 1995). In the Chen and Chen (2002) study, the homogeneity or similarity measure of the color and texture feature is based on an aggregate function of the two features in which the weights of each feature are set as constants. Considering the complexity of high-resolution satellite images, we believe that to adaptively combine multiple features will result in a more reliable segmentation. For instance, in regions with low saturation or intensity, the description of color feature is most likely unstable, so the weight of a color feature should be low in this situation. In this paper, we develop a segmentation method that adaptively integrates texture, intensity, and color features. The algorithmic framework of split-merge plus pixel-wise refinement for segmentation is also adopted.

Automatic Segmentation Using a Region-based Method and Feature Distributions Region-based Segmentation: Split-merge Plus Pixel-wise Refinement Framework This algorithmic framework was used by Ojala and Pietikäinen (1999) and Chen and Chen (2002) for segmentation of grey texture images and color texture images. It consists of three steps: (1) hierarchical splitting, (2) agglomerative merging, and (3) pixel-wise refinement; Figure 1 illustrates the procedure. This is a region-based method. In the hierarchical splitting, the image is first segmented into regions of a uniform feature. This does not require a priori knowledge about the image scene. Different regions are separated according to their features (texture, intensity, etc.). Then, the agglomerative merging merges similar adjacent sub regions until a stopping criterion triggers. Different feature regions are roughly estimated by the split-merge processing. The segmentation is completed using pixel1400 D e c e m b e r 2 0 0 5

Figure 1. Region-based segmentation; split-merge plus pixel-wise refinement: (a) the workflow, (b) region similarity measure is critical.

wise refinement in order to improve localization of the region boundaries. Feature Description and Similarity Measure by Feature Distribution In the three phases of the segmentation procedure, description and comparison of region features is critical. The method is illustrated in Figure 1b. The first problem is how to describe the region features, and we use feature distribution to do this. The n-dimensional discrete histogram is employed as the feature descriptor. To keep computational cost low, n is usually less than 3. For a region i, the feature can be described as Hi(x) F(x)/Ni,

a Hi(x) 1

(1)

x

where, Hi(x) is the histogram function, x is the feature vector, F(x) is the pixel number of the vector x, and Ni is the total pixel number of the region. Considering intensity features only, the descriptor is just the grey-level histogram. For color or texture features, it is the description of more complicated feature distributions. To measure the similarities of different feature distributions, one can use the histogram intersection technique (Swain and Ballard, 1991) and the G statistic (Sokal and Rohlf, 1987). The histogram comparison method is less important than feature selection in segmentation (Ojala, 1996). In this paper, we use the correlation coefficient to measure the similarity of two histograms Hi and Hj: rij

cov(Hi,Hj) si sj

,

rij 1.0

(2)

where cov(Hi,Hj) is the covariance of the two histograms; i and j are the mean square deviations. A high value for the correlation coefficient means high similarity of the two feature distributions. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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The remaining problem is how to integrate multiple features of the different regions together. For instance, for two regions with homogenous intensity but different color, using the grey-level histogram can hardly discriminate them. We need to develop an adaptive method to determine the weights of multi-channel features for the information fusionbased methodology.

Adaptive Integration of Texture, Intensity, and Color Features As the estimation of the joint distribution of multi-channel features is usually very difficult and using high dimensional histograms is very costly in terms of computational resources, a feasible way to combine multiple features is to compute similarity in each feature space separately, and then to integrate the scores into an aggregate similarity score. When using feature distributions, the first problem is to select the proper property of the feature used to form the histogram. With regards to the texture feature, the Local Binary Pattern/Contrast (LBP/C) distribution is chosen and approximated using a discrete two-dimensional histogram of size 256 by b, where b is set to 8. This texture descriptor was proposed by Ojala et al. (1996) and has shown great power in texture discrimination and computational simplicity. LBP/C distribution can be obtained by the computation of 3 3 neighborhood pixel values (assuming g is the center pixel, gn is the neighbor pixel, and n 0,1, . . . ,7): (a) The values of the pixels in the thresholded neighborhood are multiplied by the binomial weights given to the corresponding pixels. gn bn2n (if gn g, bn 0 else bn 1), (b) The obtained values are summed for the LBP number of this texture unit: LBPg gn, n 0,1, . . . 7, and (c) C is the difference between the average grey-level of those pixels of which bn is 1 and those of which bn is 0. An LBP value describes the spatial structure of the local texture while the C indicates the contrast of the texture. The comparison between LBP and other texture descriptors can be found in Ojala et al. (2001). LBP/C only describes texture pattern in one band. For multispectral images, for simplicity, we only use the average value of the intensity of each band for computing the LBP/C. Currently, we do not take into account cross-band relations between pixels. The LBP/C description is intensity invariant, so the intensity feature should be added and can be described simply with the grey-level histogram. Identical to the LBP/C descriptor for the multispectral images, the average value of the intensity is adopted to compute the one dimensional intensity histogram. As for color features, we choose a saturation/hue distribution. This is approximated by a two dimensional histogram of size 8 by 60. The saturation and hue of a pixel can be computed by: Sat max(R,G,B) min(R,G,B) 13(G B) Hue arctana b. (R G) (R B)

(3)

Hue value ranges from 0 to 360 degrees, representing different colors. The saturation/hue feature can effectively separate the different colors. One problem of using hue is that hue values of the pixels with low saturation are numerically unstable (Cheng et al., 2001). Also, if the intensity of the color lies close to white or black, hue and saturation play little role in distinguishing colors. This problem is suppressed by our feature integration method. We set the numbers of bins of the saturation and hue as 8 and 60, respectively, which is a trade-off between discriminative power and computational cost. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

Thus far, we can describe any region using texture, intensity, and color features based on feature distributions. To measure the similarity Simij(ft,fi,fc) of two regions i and j, we use an aggregate function to combine multiple features: Simij(ft,fi,fc) Ct rt Ciri Ccrc

(4)

where (ft,fi,fc) represent the three different types of features, which are described as the discrete histograms; ( t,i,c) are respectively similarity measures of texture, intensity, and color features. Now, the problem is how to adaptively determine the weights of the features rather than simply using the constant weights. We used adaptive determination to derive the texture, intensity, and color weights (Ct,Ci,Cc):

•

•

• •

If the similarity of the two regions measured by a particular feature is high, the weight of the feature should be low, while the others should tend to be higher for discriminating the two regions. If the two regions are most likely texture regions, and the t is small, then Ct should be large, and the other two weights should be smaller. In this situation, using texture features can discriminate the regions easily. If one of the two regions is very smooth in intensity and the intensity contrast between them is high, the intensity feature should be dominant. If the two regions are with high saturation and small Ct and Ci, so the weight of the color feature should be greater than that of other two features. The two regions can be separated mainly by color features.

Before we estimate the weights, we need to evaluate the region attributes, for example, how the region is like a texture region? We define a simple score: pt Nt/N

(5)

in order to evaluate the possibility of a region being a texture region. Nt is the pixel number in the texture area, and N is the total pixel number of the region. Texture pixels are identified by a simple and fast computation: in a small window centered by the pixel (the size can be 3 3 or 5 5), the difference between the maximal and minimal intensity indicates the local smoothness (a large difference indicating that it is most likely a pixel in the texture area). We set the threshold of the difference as 40. To evaluate the color region, we use saturation information. The score is defined as: pc Nc/N

(6)

where Nc is the number of pixels whose saturation is greater than 35, and N is the total number of pixels in the region. We have the adaptive estimation of the weights of the multiple features for similarity measure of two regions i and j: vt max(pti,ptj) # (1 rt)/0.2 vi [1 min(pti,ptj)] # (1 ri) # 0gi gj 0/128 vc max(pci,pcj) # (1 rc) # (1 vt) # (1 vi)

(7)

where (vt,vi,vc) are the weight estimation of the texture, intensity, and color features; (pti,ptj) and (pci,pcj) are the estimation of region i and j being a texture and color region; and gi and gj are the mean values of the intensity of the two regions. To normalize the weights, we have the final result: Ct vt/(vt vi vc) Ci vi/(vt vi vc)

(8)

Cc vc/(vt vi vc). The feature integration-based segmentation is realized by the aggregate Equation 4 which uses the adaptive weights of the feature similarities. It is applied to the three phases of the framework. It is expected that the adaptive integration D e c e m b e r 2 0 0 5 1401

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of multiple features provides more reliable and accurate segmentation results than when using just a single feature.

Experiments Using Panchromatic and Multispectral High-resolution Satellite Imagery We tested our algorithm using various high-resolution images, including panchromatic and multispectral images. There are some parameters for the three-phase segmentation procedure. For hierarchical splitting, the initial divided window size is 64, and the smallest size is 16. The trigger criterion X for window splitting is set to 1.3, and the stopping criterion for agglomerative merging MIR is 1.6. In the pixel-wise refinement step, we set the upper limit of iteration times as 32, and the iteration is stopped when the un-relabeled number the boundary pixels is less than 100. Details about the parameters can be obtained from Ojala and Pietikäinen (1999). Segmentation of Panchromatic Images Figure 2 shows a section of a panchromatic image (Ikonos 1 m by 1 m resolution, 512 by 512 pixel dimensions) and its segmentation results. The image contains open areas, river, and forest. In Figure 2b, only using the intensity feature (Ct 0, Ci 1, Cc 0) can segment the image into regions that have different intensity distributions, but it cannot discrimi-

nate the two forest regions, which have different densities of tree arrangement (probably two tree species). In Figure 2c, the result of only using the texture feature (Ct 1, Ci 0, Cc 0) reflects the reverse problem. The partial problem is that the texture descriptor is invariant to the mean of the region intensity. When the two features are integrated, the result is good. The textured and smooth regions are segmented accurately, as shown in Figure 2d and 2e. Figure 3 shows a second set of segmentation results. The images are also Ikonos panchromatic images, containing water, forest, croplands, and construction areas. Despite the lack of any a priori knowledge about the image scene, our method successfully discriminates the regions that correspond to different land-cover types. Segmentation of Multispectral Images In Plate 1, the segmentation results of a multispectral image are illustrated. The image is a Quickbird multispectral image with 2.44 m resolution. It was converted to 24-bit RGB color image before segmentation. It contains typical land-cover information: residential/constructed areas, wooded areas, water, grass lands, and bare ground. In Plate 1a, we show the segmentation result after split-merge procedure. It is obtained by using equivalent weights of texture, intensity, and color features (set Ct Ci Cc 0.33). Although the segmentation is satisfactory at the residential and water areas, other areas are incorrectly segmented. In contrast, the result is satisfied

Figure 2. Automatic segmentation of a panchromatic image a) the original image, (b) segmentation using only intensity distribution, (c) segmentation using only texture features, (d) segmentation using adaptive integration of texture and intensity distributions, and (e) result of pixel-wise refinement.

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TABLE 1.

ADAPTIVELY CALCULATED FEATURE WEIGHTS BETWEEN NEIGHBOR REGIONS DENOTED IN PLATE 1B

THE

Neighbor Regions 0–1 0–2 1–2 1–3 2–3 2–4 2–5 2–6 2–7 2–8 3–4 3–5 4–5 5–7 7–8

TABLE 2.

when the features are adaptively integrated, as shown in Plate 1b. Using an example, Table 1 shows the estimation of the feature weights (Ct,Ci,Cc) of the neighbor regions. They are used for similarity measures and allow the pixel-wise refinement to be carried out correctly, as shown in Plate 1c. Plate 2 demonstrates six examples of segmentation of multispectral images. The size of the images is 512 pixels by 512 pixels. The images contain various textured color regions which typically correspond to different land uses or objects. They are selected to show the capability of our method for automatic segmentation. Considering the segmentation results are obtained requiring no a priori knowledge and no post processing (smoothing or editing), we could say that the method is capable of handling many kinds of colored high-resolution satellite images. The method is not sensitive to the parameters of the region-based method. The same parameter set is used for all of the experimental images, and most of the segmentation results are visually satisfactory (different texture, intensity, and color regions are correctly discriminated). Table 2 shows the misclassified ratio of each kind of region in the Plate 1 and Plate 2 images. The ratios are computed by comparing the pixel number of the result regions to manually digitized region areas, which are treated as reference values. In most of the images, the water areas are segmented accurately. Note that for the Plate 2b image, the water area is discriminated despite the occurrence of shadows. This is due to the texture feature that dominates the segmentation of this region and uses an intensity invariant descriptor (LBP/C). The dense and regularly arranged built-up areas and other construction areas are also segmented to a low misclassification ratio. On the other hand, due to the variable image conditions and contents, there are still some misclassified regions or inaccurate boundary refinement. As shown in Plate 2d, the small regions denoted by the white arrows are misclassified, and the boundaries are not accurately positioned. Small and single houses are not segmented in Plate 2b, 5c, 5e, and 5f. Some regions with thin ribbon shapes, like the one denoted in Plate 2e, tend to be misclassified. This could result from the region-based method. In the split-merge processing, the smallest region unit is 16 16, so that the misclassified small regions lead to final inaccurate result. In Plate PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

1 2a 2b 2c 2d 2e 2f

Ci

Cc

0.97 0.94 0.19 0.69 0.54 0.08 0.30 0.88 0.21 0.03 0.47 0.60 0.20 0.45 0.22

0.01 0.03 0.43 0.17 0.23 0.74 0.42 0.05 0.19 0.62 0.44 0.11 0.73 0.08 0.48

0.02 0.03 0.37 0.13 0.18 0.17 0.37 0.07 0.49 0.35 0.13 0.19 0.06 0.47 0.30

MISCLASSIFIED PIXEL RATIOS IMAGES

Figure 3. Automatic segmentation of panchromatic images. Plate Plate Plate Plate Plate Plate Plate

Ct

FROM

PLATE 1

AND

PLATE 2

Water

Residential/ Construction Area

Wood/ Forest

Crop/Grass/ Other

0.04 0.02 0.02 0.03 –– –– 0.12

0.07 0.11 –– –– –– 0.04 0.06

0.02 0.13 –– –– –– 0.11 ––

0.08 –– –– 0.01 0.09 0.15 ––

2f, the arrows denoted regions are segmented as two different regions which actually belong to the same water area. This is still a reasonable segmentation because the separated two regions have large differences in intensity. “High level” (i.e., expert) knowledge or previously trained samples could be expected to eliminate this kind of misclassification. Figure 4 plots the convergence performance of each of the methods. The samples used are the images in Plate 2a, 2b, and 2c. It shows that the decreasing of the number of relabeled pixels in the iteration of pixel-wise refinement. The boundaries are gradually refined through the iterative pixel reassignment procedure. The iteration is normally convergent in 32 sweeps (twice that of the smallest window size in the split step). Most of the final region boundaries are relocated in one pixel accuracy.

Discussion, Conclusions, and Future Research Automatic segmentation of high-resolution satellite imagery is useful for a wide range of geospatial applications. The challenge lies in the fact that feature compositions in these image data are often quite complex, making automated feature extraction a difficult, challenging task. In such contexts, integrated, simultaneous analysis of multiple features is a potentially powerful way for improving classification accuracy. This paper presents an adaptive method for integrating texture, intensity, and color features for image segmentation. A region-based algorithmic framework is adopted, with feature distributions (histograms) serving as the feature descriptor. To date, the utility of combing multiple features, feature distributions and the split-merge method for segmentation have been investigated by other research. Our contribution, as part of the research presented here, is the weighting of multiple D e c e m b e r 2 0 0 5 1403

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Plate 1. Automatic segmentation of a multispectral (color) image: (a) using constant feature weights, (b) adaptive integration of texture, intensity, and color features, and (c) pixel-wise refinement result of (b).

Plate 2. Automatic segmentation of multispectral (color) images: (a) Quickbird, 2.44 m resolution, (b) Ikonos, 1 m resolution, (c) Quickbird, 2.44 m resolution, (d) Ikonos, 1 m resolution, (e) Ikonos, 1 m resolution, and (f) Ikonos, 4 m resolution.

features and the adaptive estimation of these feature weights. The adaptive integration method significantly improves segmentation accuracy as compared with using only single feature and with using multiple features with constant weights. We tested our method using various high-resolution satellite 1404 D e c e m b e r 2 0 0 5

images containing typical land-cover and hydrologic information. Most of the segmentation results are visually satisfactory. We demonstrate our method to be capable of segmenting images into regions belonging to appropriate land-use classes and object categories. PHOTOGRAMMETRIC ENGINEERING & REMOTE SENSING

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et al., 2004) is a potentially useful means for taking advantage of high level knowledge for achieving higher segmentation accuracy, and thus more research effort needs to be directed toward this goal.

Acknowledgments The authors would like to thank the support of a GEOIDE project “Automating 3D Feature Extraction and Change Detection from High Resolution Satellite Imagery with Lidar and Maps,” Canada. Also, the anonymous reviewers are appreciated for their suggestions improving the paper.

Figure 4. Changing of the number of the relabeled pixel in pixel-wise refinement.

Although the scheme of adaptive integration of multiple features demonstrates promising results, several priorities for future research remain.

•

•

•

•

The first priority relates to thresholds which have considerable impact on segmentation results. In our experiments, the thresholds used performed very well. For hierarchical splitting, the initial divided window size, the level number of splitting, and the trigger criterion X are essential. Smaller window sizes and X will lead to more split windows and a heaver computational burden, although this may improve the boundaries of small objects. In our experience, the stopping criterion for agglomerative merging MIR sometimes greatly affects the segmentation result. A small MIR (for example 1.2) may result in over segmentation due to unmerged small windows, while a large MIR may lead to higher than expected merging and lead to under segmentation. A possible means for selecting the proper thresholds is to employ dynamic and adaptive selection based on the features and their similarity measures. This can be realized by a full multi-scale segmentation scheme whereby the initial window is the entire image and the level of splitting in the sub-windows is adaptively determined based on feature measures. The stop criterion of merging depends on the dynamic estimation of the segmentation result. It is expected that this relieves the threshold impact while identifying more accurate boundaries for small objects. The second research priority relates to description of multiple features. In our method, the three features of a sub-region are separately described by the histograms. LBP/C and saturation/hue are chosen as descriptors of the texture and color features. Presently, there are alternative methods for describing color feature and color texture (Paschos and Petrou, 2003; Mäenpää and Pietikäinen, 2004; Palm, 2004). Further experiments on using alternative descriptors will help to determine better feature measures for segmentation. In addition, for multispectral images we only use the average value of bands for forming the histograms, and do not consider cross-band relations. Higher dimensional histograms can possibly provide more powerful discrimination ability, but using these will lead to higher computational cost. Further analyses and experiments are necessary for determining an optimal balance. The third priority relates to conducting comparative studies. To date, there exist several programs that can automatically segment high-resolution imagery, for example: eCognition™ (http://www.definiens-imaging.com/ecognition/) and InfoPACK® (http://www.infosar.co.uk). Comparing the performance of our method with that of these programs is important to further improving segmentation results. The fourth priority relates to the use of a priori knowledge or high level/expert knowledge as part of the segmentation process. This is a more challenging issue since it depends on context relevant segmentation rules and information. Using object-based methods (Shackelford and Davis, 2003; Benz


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