15 August 2014 Urban area detection from high-spatial resolution remote sensing imagery using Markov random field-based region growing
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Dynamically changing urban areas require periodic automatic monitoring, but urban areas include various objects and different objects show diverse appearances. This makes it difficult to effectively detect urban areas. A region-growing method using the Markov random field (MRF) model is proposed for urban detection. It consists of three modules. First, it provides an automatic urban seed objects extraction approach by designing three features with respect to urban characteristics. Second, the method uses an object-based MRF to model the spatial relationship between urban seed objects and surrounding objects. Third, a MRF-based region-growing criterion is proposed to detect urban areas based on seed points and spatial constraints. The strength of the proposed method lies in two aspects. One is that automatic selection of seed points is presented instead of manual selection. The other one is that the region-growing technique, instead of probabilistic inference, is used to solve the MRF optimization problem. Experiments on aerial images and SPOT5 images demonstrate that our method provides a better performance compared with the region-growing method, the classical and object-based MRF methods, or some other state-of-art methods.



In recent years, urban detection has become more and more crucial for many applications. It helps government agencies and urban region planners in updating the geographic information system and forming plans. Moreover, due to an enormous number of human activities, the scope of urban areas quickly changes from time to time. Considering the conflict between the need for periodically detecting urban areas and the high-human cost, many approaches had been proposed to automatically detect urban areas from remote sensing images. However, an urban area is an abstract semantic object. It is a comprehensive region including several subobjects such as buildings, roads, trees, water bodies, grass spaces, etc. This means that classical spectral-based recognition methods cannot be simply transferred to extract urban areas. Hence, besides spectral value, features that are more effective are needed for urban detection. Since urban scenes usually have a unique texture with respect to natural scenes, texture analysis becomes one main approach for urban monitoring.910.11 However, the texture pattern of urban scenes is not consistent in all kinds of areas. Methods of texture analysis may suffer from a lack of robustness. In order to answer this problem, several methods have been studied. For instance, Benediktsson et al.1 adopted morphological transformations to extract features of urban areas and classify them using a neural network. Weizman and Goldberger12 built a visual dictionary to learn the urban visual words and then detected the urban regions based on the dictionary. Sirmacek and Ünsalan13 employed the local feature points extracted by the Gabor filter to vote for the candidate urban areas. Furthermore, Kajimoto and Susaki14 and Liu et al.15 extracted the urban areas from polarimetric SAR images using the polarization orientation angle and only positive samples, respectively. However, algorithms may have less transferability with respect to different urban characteristics, as no single-feature descriptor is available for all kinds of the urban objects.

On the contrary, some subobjects that consist of a typical urban pattern can be well detected according to their own characteristics. For instance, man-made objects, such as buildings6,7,16 and roads,1718.19 usually have compact shapes. In contrast, spectral features are important for detecting natural objects, e.g., vegetations20,21 and water bodies.21 Hence, an alternative way of urban detection is to first detect some urban subobjects and then extract the entire urban area based on the extracted subobjects. The region-based classification is a widely used approach to detect certain land cover objects.2223.24.25 However, different urban areas may consist of different subobjects. Meanwhile, some subobjects, such as trees and water bodies, may appear in both urban areas and the nonurban areas. This phenomenon makes the region-based urban detection methods challenging, even though each urban subobject can be accurately classified. As urban objects are spatially adjacent, one possible way to answer this problem is to take the spatial information of objects into account. The Markov random field (MRF)26 model provides a statistical way to model spatial contextual information, and it has been extended to the region level for image classification. 2324.25 For example, Wu et al.23 used some rectangular regions as the initial objects and then classified the polarimetric SAR images using the Wishart MRF. However, the accuracy of classification is still limited when the rectangular region is located on the edge of some objects. Zhang et al.24 improved this method by using a mean shift to obtain the finer initial regions. Wang and Zhang25 used the Gaussian distribution to recognize images instead of the Wishart distribution. Although these MRF-based classification approaches usually obtained remarkable results, they assumed that each land class obeyed a certain probability distribution, e.g., the Wishart or Gaussian distribution. Nevertheless, the assumption about the probability distribution does not hold in the case of detecting urban areas, as urban areas are often represented as complex regions with various subobjects. Using the probabilistic inference of the MRF model in terms of common probability distributions cannot appropriately detect urban areas.

Motivated by this observation, this paper proposes an MRF-based region-growing method to extract urban areas. Our main contributions include two aspects. First, the proposed method introduces a new MRF-based region-growing criterion to overcome the limitation of the traditional probabilistic inference way of the MRF model. The method retains the advantages of the MRF model in the description of the regional spatial constraints. Both the spatial constraints and the characteristic of urban areas are considered to design a region-growing criterion. Second, an automatic seed objects extraction method is proposed for the MRF-based region growing. The method automatically extracts three features to describe the spectral and granularity information and uses these three features to detect buildings and their shadows as seed points. Our method provides an unsupervised way to detect urban areas, which makes it possible to capture the correlations among various urban objects by combining the benefits of region growing and the MRF model.

The rest of this paper is organized as follows. Section 2 introduces the method for initializing seeds, and Sec. 3 presents the details of the MRF-based region-growing method. Section 4 discusses the results obtained by applying our method on remote sensing images. Finally, Sec. 5 draws a conclusion.


Selection of Seed Points

The selection of seed points is a fundamental step for a region-growing algorithm. The main concept of the selection of seed points is grounded in the observation that the buildings are located in every corner of the city and are often adjacent to shadow areas. Hence, we extract them and their shadows as seed points in this section. In order to appropriately detect seed points, we will first explore three features F1, F2 and F3. The details are given in the following sections.


Extract the Pixel-Level Spectral Value F1

Because buildings usually show a bright appearance in an image and their shadows are dark, a spectral value F1 is used to describe this feature. Namely, for a given image Y=(Y1,Y2,,YP), each spectral channel Yt (1tP) is defined on an M×N rectangular lattice S, i.e., S={s|s=(i,j),1iM,1jN} and Yt=(yst)M×N. Then, spectral value F1=(fs1)M×N is defined as fs1=t=1Pyst, which can describe the spectral value of each pixel s on different channels.


Extract the Region-Level Spectral Variance F2

Different urban objects have various appearances, so their spectral variance should be relatively large. Hence, we design a region-level spectral variance F2 to capture this feature. First, the initial objects are obtained using a mean shift method,27 which constructs a probability density to reflect the underlying distribution of points in some feature space and to map each point to the mode of the density which is closest to it. Then, the given image Y is divided into an over-segmented region set R, i.e., R={R1,R2,,Rk}. Each Ri of R denotes an over-segmented region (i=1,2,,k), RiRj= (ij), and k is the number of these regions. With the region set R, we can further define the neighborhood system N={Ni|i=1,2,,k} to describe the spatial context of regions. Here, each Ni denotes the set of regions neighboring Ri. Let M(Ri) be the mean value of pixels in Ri, and the local spectral variance between region Ri and its adjacent regions can be calculated as follows:


where μi=(1+|Ni|)1[M(Ri)+jNiM(Rj)], and |Ni| is the number of regions in Ni.

In Eq. (1), every region has the same impact on V(Ri). Intuitively, it may be preferable to determine the impacts in Eq. (1) using an adaptive way. Hence, the equation for V(Ri) is revised as



In Eq. (2), M*(Rj,Ri) is defined as follows:

where α=|Rj|·|Ri|1 and μi*=(1+|Ni|)1[M(Ri)+jNiM*(Rj,Ri)], region size |Ri| is the number of pixels in region Ri. In this revised equation, the impact of Rj will be reduced when the ratio of |Rj| to |Ri| is less than p. That is to say, the effect of each region is affected by its region size. Here, p is a threshold with which to measure the ratio between the sizes of two regions. Since the relationship of region sizes among different objects is relatively stable, we empirically set p as 0.3 in this paper.

Based on the V(Ri), F2=(fs2)M×N is defined as fs2=V[R(s)]1/2 to reflect the spectral variance among regions. Here, R(s) is the region to which pixel s belongs.


Extract the Granularity Information F3

Urban areas have more different types of objects and more complicated appearances than nonurban areas. Therefore, in the over-segmented region set R={R1,R2,,Rk}, objects of urban areas usually have smaller region sizes than objects of nonurban areas. In other words, the granularity of urban areas is finer than that of nonurban areas. Hence, we employ the region size and the spatial relationship among regions to define F3=(fs3)M×N, i.e.,


where P[R(s)]=|R(s)|·(M·N)1. In the above equation, P[R(s)]P[R(s)]·log{P[R(s)]} is used to reflect the region size of R(s) and jNR(s){P(Rj)P(Rj)·log[P(Rj)]} is used to describe the context information of regions. Note that f(x)=xx·log(x) is a monotonically increasing convex function when x[0,1]. Hence, the monotonicity of f(x) can make P[R(s)]P[R(s)]·log{P[R(s)]} to indicate the region size. What is more, if we assume that |NR(s)| is fixed, the convexity of f(x) can make jNR(s){p(Rj)p(Rj)·log[p(Rj)]} take a small value when the sizes of regions neighboring R(s) are close. It will lead to a consistent result with a smooth region size, which is suitable for capturing the granularity information since the granularities of regions are usually similar for one certain object.

An example to illustrate these features is shown in Fig. 1, where Figs. 1(b), 1(d), and 1(e) are features F1, F2 and F3 extracted from Fig. 1(a). From this example, one can see that the buildings in Fig. 1(b) are bright, which denotes a high F1 value, and their shadows are of the low F1 value. Similarly, the spectral variance F2 of urban areas is larger than that of others areas, and urban areas have a small granularity F3 value. Based on these features, we design E1=(es1)M×N, E2=(es2)M×N, E3=(es3)M×N, and E4=(es4)M×N to describe the buildings’ spectral values, dark shadows’ spectral values, regional spectral variance, and granularity information, respectively. They are

where Fγ1, F1γ1, Fλ2, and Fπ3 denote the γ, 1γ, λ, and π fractile of F1, F2 and F3, respectively.

Fig. 1

(a) Original aerial image. (b) Pixel-level spectral value. (c) Initial over-segmented region set R. (d) Region-level spectral variance. (e) Granularity information. (f) Histogram of F1 and γ (g) Histogram of F2 and λ. (h) Histogram of F3 and π. (i) Seed points extracted based on (b), (d), and (e).


γ,λ, and π are the key parameters for the selection of seed points. The parameter γ is used to make E1 capture the spectral feature of buildings. Since buildings usually take a high spectral value, they are often expressed as the tail of the histogram of F1. Hence, γ is set to a high value to get the tail of the histogram of F1, such as Fig. 1(f). Correspondingly, E2 uses 1γ to obtain the first peak of the histogram of F1, which describes the dark shadows with a low F1 value. For the same reason, E3 and E4 are set with a high λ value and low π value to catch the tail of the histogram of F2 and the first peak of the histogram of F3, respectively. These can extract buildings’ spectral variance and granularity features. An illustration of setting γ, λ, and π is shown in Figs. 1(f)1(h).

Then, by sequentially combining E1, E2, E3, and E4, seed points can be obtained. Namely, we first use D1=(ds1)M×N to get pixels belonging to buildings and adjoining the shadows, or pixels belonging to shadows and adjoining the buildings. This is defined as

where the local square window w(s,r) is centered at site s and its radius is r. Then, we further consider the information of E3 and E4 by defining D2=(ds2)M×N and D3=(ds3)M×N as

At last, seed points will be selected as the set D={s|ds3=1,sS}.

For r and l, these seed points are used to determine whether a local window w(s,r) simultaneously contains pixels from E1, E2, E3, and E4 and whether pixels of each kind are not less than l. Because a building is spatially adjacent to its shadow, they can be effectively detected together using a relative small patch of the given image. Hence, by setting r to 2 for D1, D2, and D3, we use the local window w(s,r=2) as the small patch to select seed points in the following. At the same time, if there are buildings and their shadows in the small patch, there will be at least one pixel labeled 1 in the patch for each esi, i=1, 2, 3, 4. Therefore, l is set to 1. It means that only a pixel which simultaneously possesses or neighbors E1, E2, E3, and E4 within the small local window w(s,2) can be chosen as the seed point. An example is shown in Fig. 1(i). Note that one pixel would show different sizes of the Earth’s surface in remote sensing images with various spatial resolutions, which may affect the setting of parameter r. Namely, r can be set to 1 for the low-spatial resolution remote sensing images and be set larger than 2 for extreme high-spatial resolution remote sensing images.


MRF-Based Region Growing

Based on extracted seed points, a MRF-based region-growing criterion is proposed in this section. First, the MRF model is briefly reviewed. Then, the proposed criterion for urban detection is introduced.


MRF Model

Let X={XRi|RiR} be the label random field defined on the over-segmented region set R. We use 1 to flag urban areas and 0 to flag nonurban areas, and each random variable XRi takes a value of 1 or 0 to represent the label of region Ri it belongs to. If x={xRi|RiR} denotes the realization of X, the optimal realization x^ can be obtained by maximizing the posterior probability, i.e.,



The energy form of Eq. (4) is



In Eq. (5), the likelihood function P(Y|X) is used to describe image features. In this paper, we assume that all YRi of Y are independent given labels. That is


The distribution of random field P(X) is assumed to be of the Markovianity property, i.e.,


Therefore, Eq. (5) can be rewritten as



Due to the complexity caused by interactions among labels, it is difficult to find the solution of the MRF model. Hence, the local optimal solution x^=(x^Ri) can be obtained as follows:


where the likelihood energy Ef(Ri) is the cost of the observation of Ri, and the label energy El(Ri) is the cost of the label of Ri.


MRF-Based Region Growing

In this section, an MRF-based region-growing criterion is introduced to find the optimal realization x^. To minimize the total energy of the MRF model, the proposed method will iteratively merge adjacent regions that could decrease the total energy. Namely, for neighboring regions Ri and Rt, the total changed energy E(Ri,Rt) is first calculated these two regions are merged. Based on Eq. (7), E(Ri,Rt) equals the sum of the changed likelihood energy Ef(Ri,Rt) and the changed label energy El(Ri,Rt), i.e.,





where |Ri|·[M(Ri)M(RiRt)]2 and |Rt|·[M(Rt)M(RiRt)]2 can reflect the change of the observations in region Ri and Rt, respectively. The changed label energy of Ri is defined as


where the pair-clique potential
El(Ri,Rt) uses |Ri| to consider all changed label energies for each pixel in Ri and its neighbors when xRi is relabeled as xRt. Then, by merging region Ri and its neighboring region that can minimize the total changed energy, a MRF-based region-growing approach can realize urban detection step by step. The details of the rule of region growing are given in Algorithm 1.

Algorithm 1


Input: the observed image.
Output: urban detection result.
1) Set a threshold T.
2) If there exists a region Ri satisfying |Ri|<T and xRi=0, select Ri and go to step 3; else, stop.
3) For Ri and its neighbor region Rt, based on Eqs. (810), calculate the total changed energy E(Ri,Rt).
4) Find the region Ri* that has the minimum energy value, i.e., Ri*=argminRt,tNiE(Ri,Rt).
Merge Ri and Ri* as a new region labeled xRi*, then go to step 2.

The proposed criterion is different from traditional region-growing methods, as it does not begin from seed points but from nonseed points. We only consider the nonurban regions labeled 0 and their region sizes are less than the threshold. For each selected region Ri, the energy values are calculated between Ri and its neighbor regions, respectively. Then, Ri is merged with the one neighbor region that has the minimum energy value. Hence, Ri merged with an urban region will lead to a larger urban region; in contrast, Ri merged with a nonurban region will result a new nonurban region. Therefore, the rule of our approach is a competition rule of region growing for both urban and nonurban regions.

Urban areas can be extracted using the region-growing criterion. Namely, urban areas are first initialized using the label field x={xRi|RiR} based on seed points D, i.e., set xRi=1 if RiD; or else, set xRi=0. Then, by increasing the thresholds, the growing criterion gradually updates the urban areas. Note that different sun angles may affect the shadow length and direction, but it does not change the spatial topological relationship between buildings and their shadows. Hence, the proposed method is robust for effective detection of varying urban areas contained in different remote sensing images.


Parameter Setting

There are two parameters in the MRF-based region-growing criterion, i.e., β and T. The potential parameter β is used to balance the influence between Ef(Ri,Rt) and El(Ri,Rt). A high β value emphasizes El(Ri,Rt) and leads to results with large homogeneous objects. On the contrary, a low β value emphasizes Ef(Ri,Rt) and is suitable for getting results with many details. Hence, β should select different values for various applications. However, as the relationship between urban and nonurban areas is quite stable, β is fixed and is empirically set as 0.05 for simplifying the parameter setting.

The threshold T is used to control the process of region growing. By gradually increasing T, small regions labeled nonurban are merged into larger urban regions or nonurban regions, then urban areas are extracted. In practice, we used T=25 as the initial threshold and doubled the threshold each time. The final termination threshold was determined by the change of the spectral variance. The assumptions supporting this threshold selection are that urban areas consist of various subobjects and their spectral variance should be large; if the nonurban areas are wrongly recognized as urban areas, an abrupt change of the spectral variance should be observed. Here, we use CR(i,i+1) to show the change rate of spectral variances, i.e.,


where Std_T(i) denotes the standard deviation of detected urban areas with termination threshold T=T(i). Then, we can take the inflection point of CR(i,i+1) as the final termination threshold, after which CR(i,i+1) will abruptly decrease. An example is shown in Fig. 2, where we use T=[25,50,100,200,400,800,1600] as the candidates of termination thresholds. Some extracted urban areas are illustrated in Figs. 2(a)2(g). Std_T(i) with different Ts is calculated and given in Fig. 2(h), where the corresponding CR(i,i+1)s are also shown in Fig. 2(i). As CR(200,400) is an inflection point, we take T=400 as the final termination threshold for this experiment and Fig. 2(e) shows the corresponding detection result.

Fig. 2

Example of parameter T: (a) urban area with T=25; (b) urban area with T=50; (c) urban area with T=100; (d) urban area with T=200; (e) urban area with T=400; (f) urban area with T=800; (g) urban area with T=1600; (h) Std_T(i); and (i) CR(i,i+1).




The MRF-based region-growing method provides an unsupervised way for the monitoring of urban areas. With the aim of fully evaluating the performance of the proposed method, experiments and comparisons were carried on two groups of images, i.e., aerial images (Sec. 4.1) and SPOT5 images (Sec. 4.2).


Experiments of Aerial Images

In this experiment, three aerial images, as shown in Fig. 3, are used to test our method and other urban extraction methods. These aerial images were acquired in 2009 and are located in Taizhou City, China. The three images have the same size of 500×500, and the spatial resolution is 0.4 m. The test images contain plane agriculture fields and small villages, where urban objects show various spectral appearances and some nonurban objects are similar to seed points in terms of spectral characteristics. This makes urban detection challenging. Moreover, the following competitive methods are also considered for comparison:

  • 1. The traditional region-growing method:28 it detects urban areas without employing the MRF model.

  • 2. The classical MRF model:29 it uses the generated probabilistic model at the pixel level to obtain results.

  • 3. The object-based MRF (OMRF) model:25 it extends the MRF model from the pixel level to the object level for capturing the macrotexture pattern of a given image; this uses initial over-segmented regions to build the region adjacency graph (RAG) and defines the MRF model on the RAG to realize the segmentation.

  • 4. The two-class support vector machine (SVM):30 it is provided by ENVI software, which is a commonly used classification approach with training data.

  • 5. The object-based SVM:22 it extracts the regional features from a hierarchical tree of the scene and obtains a classification using the SVM classifier.

Fig. 3

Experiments of aerial images: (a1)–(c1) aerial images; (a2)–(c2) traditional region growing; (a3)–(c3) Markov random field (MRF); (a4)–(c4) object-based MRF (OMRF); (a5)–(c5) support vector machine (SVM); (a6)–(c6) object-based SVM; and (a7)–(c7) MRF-based region growing.


For the sake of fairness, we chose the same seed points to train the urban areas for the traditional region-growing method and the two SVM methods and deliberately selected samples to train the nonurban areas for these SVM methods as well. We also tuned the parameters of these methods to get their optimal performances. For the traditional region-growing method, we chose the threshold parameter following the instructions in the literature.28 For the two-class SVM, we set the radial basis function as the kernel type, the gamma in kernel function as 0.33, and the penalty function as 100, respectively. For the object-based SVM, we use 0.1% as the ratio of training samples based on the literature.22 Therefore, the comparison can demonstrate the difference between our model and other state-of-the-art methods.

Experimental results of aerial images are shown in Fig. 3. Here, the caption of Fig. 3 consists of two parts, where the first part using the alphabetical order denotes different test images and the second part using the number order denotes different detection methods. Detected urban objects are represented as yellow masks over the test images. From the comparative test, one can see that the proposed method exhibits a remarkable improvement for urban detection. Namely, the traditional region-growing method, as shown in Figs. 3(a2)3(c2), still has huge misclassifications which belong to different object categories and have similarity spectral appearances. The main reason is that the traditional region-growing method only uses the spectral features which do not consider the spatial constraint. By employing the spatial context information, the classical MRF model has less misclassification of nonurban areas. However, this pixel-level generate model can just recognize the parts of the urban areas with similar appearances, since it cannot model the complex and macropatterns by incorporating the long-range interactions. It also wrongly labels some urban objects as nonurban, such as the roofs of buildings and vegetation. The OMRF model utilizes the regions to describe the macrospatial constraints and improves the classical MRF model, e.g., Figs. 3(a4) and 3(c4). However, the OMRF model usually leaves the characteristic of urban areas out of consideration, which may lead to some undesirable results such as Fig. 3(b4). The SVM method trains data to obtain urban areas. Although it can effectively recognize buildings, urban vegetation objects are sometimes classified as nonurban areas because of the lack of spatial information. The object-based SVM improves the pixel-based SVM and gets results that are more consistent by considering the object semantic information with regional features. Nevertheless, it still cannot sufficiently use spatial information whose results have some misclassifications. Compared with these methods, our MRF-based region-growing method first considers the urban characteristics when we select seed points, then employs the MRF defined on the region level to capture regional spatial constraints, and finally proposes a corresponding region-growing criterion that utilizes these features to detect urban areas. Hence, our method demonstrates a better performance than the other methods.

Experimental results are quantitatively evaluated by the overall accuracy (OA) and kappa coefficient κ. OA and κ are the two indicators that measure the degree of similarity between two images.31 If Pij is the proportion of subjects that were assigned to the i’th class by the first image and the j’th class by the second image and denotes Pi=j=1kPij and Pj=i=1kPij, then


The OA and κ of aerial images are given in Table 1.

Table 1

Comparison of results.

Fig. 3(a)Fig. 3(b)Fig. 3(c)
Traditional region growing0.3790.5910.3980.6040.4890.648
Classical MRF0.7780.9130.4600.6840.6150.758
Two-class SVM0.8060.9230.6170.7700.6830.796
Object-based SVM0.9110.9660.7400.8500.8320.905
MRF-based region growing0.9140.9670.9020.9520.8860.938

Note: For each column, the bold value denotes the best index among all the indexes in this column.

From these quantitative indexes, we know that MRF-based region growing can enhance both the OA and kappa for each experimental image. This also shows that our method extracts a better scope of urban areas than do the other methods. In particular, when the topographic features are complex, the enhancement of indices is obvious. For clarity, the quantitative indices of Table 1 are illustrated in Fig. 4.

Fig. 4

Kappa and overall accuracy (OA) of experiments of aerial images: (a) kappa and (b) OA.



Experiments of SPOT5 Images

The effectiveness of the proposed method is further tested in this section. Two SPOT 5 remote sensing images, as shown in Fig. 5, are employed for the next experiment. These test images are located on the Pingshuo area of China. Both sizes are 438×438. These test images mainly consist of three object types, i.e., urban areas, cultivated land, and woodland. Among them, urban green space and woodland and urban building and cultivated land have similar spectral appearances, respectively. This phenomenon increases the difficulty of urban detection.

Fig. 5

Experiments of SPOT5 images: (a and d) Original SPOT5 image; (b and e) ground truth (red); and (c and f) results of MRF-based region growing (yellow).


Experiments of SOPT5 images are illustrated in Fig. 5. Compared with the ground truth, the MRF-based region-growing method performs well and the results are close to the ground truth. This demonstrates that our model can effectively extract urban areas from different datasets.



To summarize, we proposed an unsupervised urban detection method by unifying the region-growing method and the MRF model. It first uses the granularity information and spectral features to automatically extract some typical urban objects as the seed points, which can be treated as the skeleton for the urban areas. Then, the MRF is employed to model the spatial relationships between urban seed points and other urban objects. At last, the region-growing criterion uses these relationships to recognize urban nonseed objects, which will lead to consistent results. The main novelty of the method the automatic extraction of urban seed points and the detection of urban areas using a region-growing criterion under the regional MRF-based spatial constraints. The effectiveness of the proposed method is validated by experimental results obtained from various high-spatial resolution remote sensing images. Compared to a traditional region-growing method, the classical and object-based MRF models, and the common and object-based SVM, our method can provide more precise and more meaningful results, which verifies that our method is suitable to detect urban areas. However, this method is only proper for urban detection. If it is used to extract other terrestrial objects, then one has to design a new seed extraction method and modify the region-growing criterion.

For the method presented, the potential parameter β need to be empirically set. If this parameter can be estimated in an adaptive way, then it will improve the current method.


The authors are very grateful to the editor and the anonymous referees for comments and suggestions, which led to the present improved version of the manuscript. This work is supported jointly by the National Natural Science Foundation of China, under Grants 41301470, 41001286, 41101425, and 41001251, and the basic research funds for the provincial universities. The authors would like to thank Associate Prof. Tiancan Mei, Wuhan University, China, for kindly providing aerial images.


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Chen Zheng is currently an assistant professor at the School of Mathematics and Information Sciences, Henan University. He received his BS degree in mathematics (information sciences) from Henan University in 2007 and his MS and PhD degrees in statistics and image processing of remote sensing from Wuhan University, in 2009 and 2012, respectively. His current research interests include various topics in remote sensing and image processing.

Leiguang Wang received his PhD degree in the State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing (LIESMARS) from Wuhan University in 2009. Since 2012, he has been an associate professor with Southwest Forestry University, Kunming, China. He is the author of more than 10 articles. His research interests include remote sensing image segmentation and pattern recognition.

Hui Zhao received his MS degree in the School of Mathematics and Information Sciences from Henan University in 2004. He is currently an associate professor with Henan University, Kaifeng, China. His current research interests include digital image analysis and recognition.

Xiaohui Chen received her MS degree in the School of Mathematics and Statistics from South-Central University for Nationalities in 2011. She is currently with Henan University, Kaifeng, China. Her current research interests include digital image analysis and remote sensing images segmentation.

© The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
Chen Zheng, Chen Zheng, Leiguang Wang, Leiguang Wang, Hui Zhao, Hui Zhao, Xiaohui Chen, Xiaohui Chen, } "Urban area detection from high-spatial resolution remote sensing imagery using Markov random field-based region growing," Journal of Applied Remote Sensing 8(1), 083566 (15 August 2014). https://doi.org/10.1117/1.JRS.8.083566 . Submission:

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