IEEE IMAGE PROCESSING Shadow Detection of Man-Made Buildings In

5374 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 52, NO. 9, SEPTEMBER 2014

Shadow Detection of Man-Made Buildings inHigh-Resolution Panchromatic Satellite Images

Mohamed I. Elbakary and Khan M. Iftekharuddin, Senior Member, IEEE

AbstractHigh-resolution satellite imagery is considered anexcellent candidate for extracting information about the humanactivities on Earth. The information about residential develop-ment and suburban area mapping is of interest that can beobtained from these images. Shadow of structures such as man-made buildings is one of the main cues for structure detectionin panchromatic high-resolution satellite imagery. However, tocorrectly exploit the information of the shadow in an image, theshadow needs to be detected and isolated first. In this paper, wepropose a new algorithm for shadow detection and isolation ofbuildings in high-resolution panchromatic satellite imagery. Theproposed algorithm is based on tailoring the traditional modelof the geometric active contours such that the new model of thecontours is systematically biased toward segmenting the shadowand the dark regions in the image. The systematic biasing inthe proposed contour model is accomplished by novel encodingof the radiometric characteristics of the shadows regions. Afterdetecting and segmenting the shadow and the dark regions inthe image, further processing steps are introduced. The proposedpostprocessing is based on selection of optimal threshold and aboundary complexity metric to distinguish the true shadows fromthe clutter. Experimental results are presented to validate theperformance of the proposed algorithm on real high-resolutionpanchromatic satellite images.

Index TermsBuilding detection, geometric active contours,image segmentation, panchromatic imagery, shadow detection.

I. INTRODUCTION AND RELATED WORK

SATELLITE and aerial imaging is a common method toobtain information about objects on the Earths surface.Object and target detection is of great interest for many appli-cations, including rescue operations and defense applications.Recently, extension of object detection to man-made structure(e.g., buildings) detection and recognition in aerial images hasattracted attention. The ability to effectively detect structureshelps in understanding the scene contents of the image and maybe used for content-based retrieval in databases and in otherapplications such as residential development planning, damageevaluation, and military target detection [1][5]. The shadow isa crucial cue for detecting the existence of the buildings andother man-made structures in the overhead images. Shadow

Manuscript received June 16, 2013; revised September 16, 2013; acceptedOctober 10, 2013.

M. I. Elbakary is with the Electronic Research Institute, Giza 12622, Egypt.K. M. Iftekharuddin is with the Vision Laboratory, Department of Electrical

and Computer Engineering, Old Dominion University, Norfolk, VA 23529USA.

Color versions of one or more of the figures in this paper are available onlineat http://ieeexplore.ieee.org.

Digital Object Identifier 10.1109/TGRS.2013.2288500

of buildings is isolated and then is employed for detection byintegrating and fusing the geometry of the shadow area withthe potential geometry of the building or other elevated man-made structures. However, there is a general lack of shadowdetection methods for panchromatic imagery and particularlyfor shadow of man-made buildings in these images in literature.The recent research for shadow detection mostly focuses ondifferent image modalities other than panchromatic satelliteimagery.

Shadow detection of moving objects in video streams hasbeen investigated by many researchers [6], [7]. The main pur-pose is object detection and tracking. Others have worked onsimultaneous detection and removal of shadows in the images[8][11]. The authors in these works propose to alleviate theeffect of the shadow in the image since the shadow causesloss of the color and hence loss of feature information forobjects in the shadow areas. Few other works propose detectionof shadows of still objects [12], [13]. Zhu et al. [12] uselearning-based approach for shadow detection of still objectsin grayscale images. The authors train and evaluate their systemon a database of natural images. In [13], the proposed algorithmis a successive thresholding scheme to enhance the ratio mapalgorithm for shadow detection in color aerial images. Tolt et al.[14] combine data from two different modalities for shadowdetection. The study employed lidar with position of the sunto detect shadow and then use that shadow in training ofsupervised classifier to find shadow in hyperspectral data. Otheralgorithms are proposed wherein the authors use multiple bandsfor shadow detection in the aerial images [15][17]. The mainpurpose for shadow detection in remotely sensed images is toenhance the classification.

In the color imagery, the primary cue for shadow detection isthe color in addition to the texture feature of the image contents.In the multispectral imagery, the radiometric characteristics ofthe bands are the main source of the information for shadowdetection. However, detection of shadows in panchromatic im-ages is a challenging problem. In addition to missing the colorinformation, many objects and scene contents tend to be dark oralmost dark, and distinguishing them from the shadow regionsadds to further difficulty. For shadow detection and isolationof buildings in panchromatic images, Irvin and Mckeown [18]use the shadow analysis in images of buildings in a databaseto calculate the mean of the shadow regions. They then employthe mean plus one standard deviation as the shadow intensitythreshold for the input image to detect the shadow regions. Dare[19] uses an optimal method to obtain a threshold for isolatingthe building shadow regions from other contents in the inputimages. However, this method requires that the histogram of

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ELBAKARY AND IFTEKHARUDDIN: SHADOW DETECTION OF MAN-MADE BUILDINGS 5375

the input image to be bimodal to find the appropriate threshold,which is not guaranteed in all the input images. Recently,Luus et al. [20] have employed a threshold, as a ratio ofthe maximum intensity value after investigating the imagehistogram, to detect the shadow in the panchromatic imagery.However, the algorithm in [20] suffers from the clutter, whichprevents its generalization.

In this paper, we propose a new algorithm for shadow de-tection of man-made buildings in high-resolution panchromaticsatellite imagery. Our proposed method is based on modifyingthe geometric active-contour model, which is first introducedby Chan and Vese [21]. The geometric active-contour modelproposed in [21] is based on Mumford and Shahs functionfor image segmentation [22]. The contour model is representedby the zero-level set of the higher dimensional function inthe level-set framework. These functions are able to detect theboundary of regions based on the homogeneity of local featuressuch as the intensity without depending on the edges of theregions as motivation force. Few works employed both thespectral and spatial properties in the images, such as variance,entropy, seed pixels, and color as the forces to control thelevel-set curve propagations [23][27]. The advantages of thismethodology include its low sensitivity to noise and its abilityto snap the regions and objects without clear edges. In ourproposed approach, we modify the active contour technique in[21] such that the new model is systematically biased towarddark regions, including the shadow in input image. We forcedthe algorithm in our model to favor the dark regions in theimage by adding additional term that emphasizes the detectionof shadows in the image. The novelty of the proposed methodinvolves development of fully automated algorithms withoutthe need for manual insertion of initial position of the contoursor a seed in input image. Once the potential regions of shadoware obtained, we remove the clutter, such as the vegetationand water bodies, in the candidate shadow regions by furtherprocessing the results using Otsus thresholding technique [28]integrated with a geometric filter based on the boundary com-plexity metric (BCM) [29]. Otsus threshold removes most ofthe clutter and helps the geometric filter to further remove theremaining clutter regions with high boundary complexity (BC).Clutter is naturally associated with high BCM because of thecomplexity of its irregular boundaries. Consequently, anothernovel contribution of this paper stems from using an optimalthreshold for emphasizing the boundary of the regions to be ap-propriate for distinguishing by the geometric filter. Specifically,the geometric filter uses low BCM values to detect shadowsof man-made objects and to remove the clutter. Employingthe gray-level image only without using the color informationis an additional advantage of the proposed algorithm. Theexperimental results on real satellite images show that theproposed algorithm is robust against the clutter and outperformsthe alternative algorithms.

The remainder of this paper is organized as follows.Section II describes the development of the geometric activecontours and its application to aerial image segmentation. Theproposed algorithm is introduced in Section III. In Section IV,we present the experimental results, followed by conclusion inSection V.

II. GEOMETRIC ACTIVE CONTOURS FORAERIAL IMAGE SEGMENTATION

The snake model or the active contour is introduced byKass et al. [30]. These models are energy-minimizing curves/surfaces that move in the image domain using image featuresto accurately localize object contours. Movement of the snakesare guided and influenced by external and internal forces suchthat the models reach at minimal energy by segmenting theobject. First, active-contour models are characterized by a setof parameters, and the evolution of the contours are performedon a predefined set of control points in the spatial domainof the image. One of the major drawbacks of the parametricactive contours is that the methods are influenced by the initialconditions of the contours in the spatial domain. In other words,the contours must be originated in the spatial domain closeenough to the desired feature. Otherwise, the contours convergeto the undesired objects in the image. An additional drawback isthat, usually, each contour captures only one object in the scene.For capturing more than one object, one must initiate a contourfor each. That means the parametric contours do not changetopologies during the evolution process. To overcome thesedrawbacks of the parametric active contours, geometric activecontours are introduced by Caselles et al. [31] and Malladi et al.[32]. Geometric active contours are represented by the zero-level set of a higher dimensional surface such that the updatingof the surface function is performed in the entire image domain.Geometric active contours may be either edge-based or region-based methods. In edge-based methods, the gradient of theimage is employed as an attraction force to attract the contourto the edges of the objects in the image [31], [32]. On the otherhand, the region-based methods employ the region featuressuch as the gray-level intensity, texture, and other pixels statis-tics that reflect the homogeneity of spatially localized regions asan attraction force to these objects [21]. The advantage of thismodel is low sensitivity to the noise [21]. In addition, geometricactive contours implicitly handle the topological changes andare not characterized by a set of parameters. Subsequent workshave contributed to improved results for region-based models[33], [24].

For a simple case in the region-based approach such asthe geometric active-contour method [21], the image domainconsists of background and foreground regions, which arecharacterized by homogeneity of the gray level. The gray lev-els in these two regions are approximately piecewise-constantintensities of different values uo and ui, and C is the boundarybetween the two regions. This geometric active-contour methodmay obtain good results whether the boundaries between re-gions are well distinguished or not. The energy equation ofthe geometric active-contour model that extracts the objectboundary is defined as follows [21]:

Eseg(C) =

inside(C)

|u 1|2 dx dy

+

outside(C)

|u 2|2 dx dy (1)


where C denotes the curve of the active contour, u is the imageintensity, and constants 1 and 2 are the average intensitiesinside and outside of C, respectively. The curve that minimizes(1) is the contour that fits to the edges of the object of interest[see [21] for more details about the level-set formulation andthe numerical implementation of (1)]. The geometric active-contour model in [21] has been widely applied to segmen-tation and detection objects in aerial and satellite images.Cao et al. [34] detect man-made objects in two stages for level-set algorithm. In the first stage, Cao et al. employ the fractalerror image, and then they employ the discrete cosine transformof the texture in the input image in the second stage. The tran-sition from the first stage to the second stage is achieved duringthe level-set evolution of the geometric active-contour model in[21]. The work in [35] employs spectral information in the inputimages for multiregion segmentation of multispectral satelliteimagery based on [21]. The statistical descriptions of the inputimages regions are integrated with the evolving curve in Chanand Veses algorithm [21], to detect the man-made objects in theaerial and satellite images [36]. In order to obtain the desiredboundary of the buildings in the input image, Ahmadi et al.[37] propose manual intervention in the framework of [21] byinserting initial position of the active contours. In addition,they insert grayscale intensity-based constants into the energyequation to attract the active contours toward the buildings.An approach of integrating the prior shapes of the buildingsinto level-set segmentation of Chan and Veses method [21]is proposed in [38] and [39]. The shape prior refers to theparameterization-free description for the building templates.However, having knowledge of shape priors for buildings in ad-vance may not be realistic since the shape of buildings changesdue to weather or to environment. Our proposed methodsin this paper do not anticipate shape priors.

III. PROPOSED ALGORITHM

The general advantages and wide applications of the geomet-ric active-contour model [21] for building and other detectionof man-made structures motivates us to adopt this techniquefor shadow detection of man-made structures in high-resolutionpanchromatic satellite images. However, statistical informationand prior shape information of the shadows are not available inthe overhead panchromatic images. This is because the shadowareas change during the day based on the position of the sunin the sky and the corresponding exposition of geometry ofthe structures that generate the associated shadow areas. Inaddition, the input panchromatic images are very similar to thegrayscale, and the color information and other bands informa-tion are missing. Therefore, we innovate on the geometric activecontour by plugging additional term in the energy equation tosystematically bias the contours toward the boundary of thedark and shadow regions. The additional term is based onencoding the radiometric characteristics of the dark regionsrelatively to the neighbors, which is considered a cue forexistence of the shadows. We further explain this concept inthe following.

In the geometric active-contour method in [21], (1) min-imizes energy to fit the contours between background and

foreground regions. Since the shadows in the image cannot bedescribed by prior spectral information templates or probabilitydue to missing or changing information, we use the relativeintensity of the dark regions to the neighbor as a cue forshadow detection. We reformulate the energy terms to modifythe active contours during the process of evolution such that theenergy function is selectively biased to enclose the dark regions,including the shadows, from their neighbors. The developmentof the new geometric active-contour model is presented in thefollowing.

We reformulate (1) in Section II to detect the shadows andthe dark regions in the input image. Assume an image withdomain R2 and a level-set representation, i.e., R+.We form an energy functional Etotal by adding additionalenergy Eshadow that favors the contours surrounding shadowsand the dark region as follows:

Etotal() = Eseg() + Eshadow() (2)

where Eseg is the energy equation of (1) and Eshadow is theproposed additional energy term to systematically bias (1)toward dark regions. The proposed energy Eshadow affects theentire domain of the input image and can be formulated asfollows:

Eshadow(C) =

inside(C)

|u k |2 dx dy

+

outside(C)

|u |2 dx dy (3)

where is the global mean of the input image, and k is aconstant. The value of k is systematically chosen to encodethe radiometric characteristics of shadow regions relative to thesurrounding pixels in the input image as follows. Comparisonof (3) and (1) shows that 1 is equivalent to k . Therefore,following notations in (1), k is the average intensity insideC in (3). Note that, for k < 1, the average intensity for anyimage inside C in (3) is always less than the global mean ofthe input image (i.e., the average intensity outside C). In otherwords, the value of k in (3) controls the average intensity insideC and, hence, the selection of regions by (3). Consequently,by choosing the appropriate value of k, one can enforce thecontours in (3) to prefer shadow regions in the panchromaticimage. To enforce (3) to select enclosing shadow regions, thevalue of k is chosen such that the average intensity inside Crelative to that outside it is comparable to the average of shadowintensity relative to the mean intensity of the image. Shadowfundamentals suggest that k = 0.7 is a reasonable choice toencode average shadow intensity values relative to the imagemean. In other words, shadow pixels have a lower intensity thantheir neighboring nonshadow pixels, and that the value of k is areasonable ratio to encode this relation. This choice also ensuresthat shadow and dark regions are bounded by C in (3) in thepanchromatic images considered in this paper.

We experiment with different k values in Section IV tosuccessfully encode the radiometric characteristics of shadowpixels. The experiment also confirms that k = 0.7 enforces


contour in (3) and hence systematically biases the model of (2)to enclose shadow and dark regions for all the input images inthis paper.

After inserting the regularization terms, (2) can be rewrittenas follows:

Etotal() = Eseg() + Eshadow()

+ length(C) + area (inside(C)) (4)

where , 0 are constant parameters [21]. The termslength(C) and area(inside(C)) are the length of contour Cand the area of the region inside contour C, respectively [21].Equation (4) specifically considers radiometric characteristicsof the dark regions when compared with that of the surroundingpixels in the input panchromatic image. Consequently, our pro-posed geometric active-contour model in (4) is systematicallybiased to enclose the regions that exhibit lower average inten-sity values. However, since the vegetation and water bodies inthe panchromatic images also show lower intensity than thesurrounding regions, the proposed active-contour model detectand segment these regions along with the shadow regions inan image. Therefore, further processing is necessary to removeclutter regions such as vegetation and water bodies from thesegmented image. The detail of this clutter removal process ispresented in Section III-B.

A. Level-Set Formulation of the Proposed ModelFollowing the work in [21] and [40], the level-set method

is employed to compute energy function over the input imagedomain . In this method, curve C is represented by the zero-level set of a function: R+, such that

C = {(x, y) : (x, y) = 0}

inside(C) = {(x, y) : (x, y) > 0}outside(C) = {(x, y) : (x, y) < 0} .

(5)

Then, curve C is replaced by function by using the Heavisidefunction, H , and the 1-D Dirac measure as follows:

H() =

{1, if 00, if < 0 (6)

() =d

dH(). (7)

Heaviside function H and Dirac , which is a derivative of H ,are approximated by the following functions:

H(z) =

1, if z >0, if z < 12

[1 + z +

1 sin

(z)]

, if |z| (8)

(z) =

{0, if |z| >12 +

12 cos

(z), if |z| (9)

respectively following [41] and [42]. Note that the use of theseapproximations avoids the boundary leakage and the existenceof the energy term Eshadow avoids local minima. Eshadowfavors certain regions and globally affects the energy function[38] and therefore will drive the contours to the global minima.In addition, these approximations are successfully adopted forsimilar application [36] and other algorithms [43]. By usingHeaviside function from (8) and the Dirac function from (9) andfollowing the method of level-set numerical implementation in[21], [37], and [40], the discretization of (4) is implemented asshown in (10) at the bottom of the page. where h and t arethe space iteration step and the time iteration step, respectively.1, 2, 3, and 4 are constants. In addition, 1 and 2 are theaverages intensities inside and outside C, respectively [21] [see[21], [37], and [40] for more details about discretization processand the constants in (10)]. The major steps for numericalimplementation of the level-set method, as shown in (10), aresummarized in Algorithm 1.

Algorithm 1. Proposed algorithm for segmentation

1. Initialize 0 at n = 0, where 0 defines the initial con-tour.

2. Compute of the input image.3. Compute 1 and 2 by using [21].4. Obtain n+1 by using (10).5. Check whether the solution is stationary and is not vary-

ing. If yes, stop/ If not, n = n+ 1, and go to step 3.

The result of the using the new contour model in (10), incomparison to the result of using the model in (1), is presentedin Fig. 8 in Section IV. The result of the proposed geometricactive-contour model in (10) is the segmentation of the dark

n+1i,j ni,jt

= hni,j

h2

x x+n+1i, j(

x+ni,j

)2/h2 +

(ni,j+1 ni,j1

)2/(2h)2

+

h2y

y+n+1i,j(

y+ni,j

)2/h2 +

(ni+1,j ni1,j

)2/(2h)2

1 (ui,j 1n)2

+ 2 (ui,j 2n)2 3 (ui,j kn)2 + 4 (ui,j n)2]

(10)


Fig. 1. Region combined with a strip from the surrounding pixels.

regions, including shadow, in the input image. Therefore, wefurther process the segmented regions to isolate the shadowregion from the clutter by employing Otsus threshold [28] withthe BCM in the following.

B. Shadow Isolation From Clutter

The dark regions obtained by the proposed model for thegeometric active contours include shadow and clutter such aswater bodies, vegetation, and dark grounds. In this paper, weemploy the following steps to remove clutter.

The result of the given modified model in (10) is a set ofpotential shadow regions R = {R0, R1, . . . , Rn1} that mayinclude clutter. To remove the clutter from the potential shadowregions, we propose further processing of the result as follows.We construct Otsus threshold on the global histogram ofthe detected regions after adding to each region a strip, withthickness of three pixels, from the surrounding, as shown inFig. 1. Otsus threshold is an optimal threshold method toautomatically segment a bimodal histogram. Our experimentshows that three pixels from the surrounding for each region areenough to obtain a global bimodal histogram for the detectedregions. In that bimodal histogram, one peak is expected toinclude the shadow and similar intensity pixels in the seg-mented regions. The other peak will include the surroundingspixels and similar intensity pixels. The global Otsus thresholdin this step removes the clutter which fall within the peak ofthe surrounding pixels while at the same time increases theirregularity and complexity of the boundaries of the remainingclutter that fall between the two peaks of the bimodal histogram.High BC helps in removing the clutter by using the geometricfilter as discussed in the following. The given global processingsteps are summarized in Fig. 2.

Next, we use a geometric filter to remove the clutter in Rsby processing each region Rsi in Rs. The geometric filter isimplemented by comparing the BC measure of each regionwith a threshold to filter out the regions of values higherthan the threshold. The BC measure reflects the regularity andthe complexity of the boundary [29]. Fig. 3 shows examplesof the BC of regular and irregular regions. We notice thatthe regular boundaries have smaller BC values than irregularboundaries. Moreover, the BC value increases as the complexityof the boundary increases. The BC measure is higher for theclutter because the clutter, such as vegetation and the water

Fig. 2. Procedure for the proposed global thresholding.

Fig. 3. Examples of regular and irregular boundaries. First row: regularboundaries with their BC. Second row: irregular boundaries with their BC.

body regions, have boundaries that tend to be irregular andcomplicated. On the other hand, the shadows of the buildingsand other man-made structures have boundaries that tend to beregular, and in general, zero BC is associated to straight lines,as shown in the first row in Fig. 3. The value of the threshold forthe geometric filter is chosen to be 0.15 after extensive testingwith all the regions in all the input images in this paper to keepthe shadows of interest and filter out the maximum amount ofclutter. We did not choose that threshold equals to zero since theshadow boundary of man-made structures such as building isnot ideal in all the places but may be associated with complexitythat increases the BC measure, as shown in the example ofthe first row in Fig. 3. The operation of the geometric filter issummarized in Fig. 4.

Finally, we declare a region is true shadow after processingthat region with another local Otsus threshold and then testingits BC using the above geometric filter. Our reasoning for thisstep is explained as follows. The two steps discussed in Figs. 2and 4 help in removing mainly the bright clutter. Here, brightclutter means that it does not fall within the shadow peak in theglobal bimodal histogram in procedure GlobalOtsuThreshold()in Fig. 2 and the clutter that have a high BC measure usingprocedure GeometricFilter() in Fig. 4. However, the globalprocessing for the region boundary using algorithms in Figs. 2


Fig. 4. Procedure for the proposed geometric filter.

Fig. 5. Procedure for the proposed local processing.

and 4 may not be enough to clearly identify the shadow fromthe clutter. Therefore, we use another local processing step,i.e., LocalOtsuThresold(), to strongly emphasize the regionboundaries such that the geometric filter readily detects thetrue shadow. The second row in Fig. 3 shows an example forthe expected result from applying local processing whereinthe BC increased to 0.1584. This procedure is summarizedin Fig. 5.

In Fig. 5, for each remaining region Rsi , we construct anduse its Otsus threshold after adding a strip from its surroundingpixels. The histogram of a region of the shadow of man-madestructures such as buildings with the surrounding pixels willconsist of two distinctive peaks for Otsus threshold becausethe shadow is well defined compared with the surrounding. Onthe other hand, the two peaks in the histogram of the regionof clutter with its surrounding are not well defined since theirregularity and the complexity of its boundary and using itslocal Otsus threshold will reveal a high BC measure for thegeometric filter. After applying the local threshold, we employthe geometric filter to remove the regions that have BC measuremore than 0.15. Fig. 6 shows the schematic of the wholeproposed algorithm.

In summary, we propose an algorithm for detection andsegmentation of shadow of man-made buildings in panchro-matic satellite images. The algorithm is fully automated andsystematically biased for shadow detection. We remove theclutter associated with shadow by using an optimal segmenta-tion methodology integrated with a geometric filter scheme todistinguish the clutter from the shadow of man-made structures.

Fig. 6. Schematic of the proposed algorithm.

IV. EXPERIMENTAL RESULTS AND DISCUSSIONS

Here, we present the results of our proposed algorithm forshadow detection of man-made objects in real panchromaticsatellite imageries for various challenging scenes. The imagesused in this paper are publicly provided by the U.S. GeologicalSurvey, and each image consists of four bands (RGBIR) witha resolution of one pixel per meter in both directions. Forthese color images, we created the corresponding panchromaticimages for our application. The images contain various objects,including vegetation, buildings, towers, water bodies, roads,and other man-made objects such as cars and trucks. We choosethe scenes such that they contain various scenarios for buildingscontents such as isolated buildings, connected buildings, smallbuilding, and large buildings. In addition, the shadow contentsin the input images vary in the shape and the size such thatthe shadows might be represented by a few pixels to a largeregion. In addition, the shadow region can either be isolated or


Fig. 7. (a) Input satellite image used in the first experiment. (b) Initial contourgenerated in the input image. (c) Ground truth of the shadow for man-madebuildings. (d) Evolution of geometric active contours after 20 iterations.

connected to vegetation and/or water bodies. The performanceof the proposed algorithm is evaluated both by using qualitativeand quantitative metrics. The quantitative evaluation of theresults is based on the ground-truth data of the shadows inthe input images, which are derived manually by labeling theshadow pixels of man-made structures to zero. Following [21],we choose the values of parameters in (10): 1=2=1, t=0.1, =1, =1, = 1, and h=1. Moreover, we set the remain-ing parameter values 3=4=1 similar to 1 and 2 in [21].

In the first experiment, we use the input image shown inFig. 7(a). The initial contour of the geometric active contouris generated automatically in the middle of the input image asa regular circle with a diameter that is proportional with thesize of the input image, as shown in Fig. 7(b). After few itera-tions, the active contours start to surround all the dark regionsincluding the shadow regions. Note that the contours favorand enclose the regions that are darker than the surroundingsuch as the shadows, vegetation, and water bodies, as shown inFig. 7(d). Fig. 7(c) shows the ground truth of the shadow in theinput image in Fig. 7(a). In addition to the shadow, the contourssurround a clutter such as the vegetation and the water bodies(e.g., lakes and canals) since these regions are darker than thesurrounding in the gray-level images.

Fig. 8(a) shows the shadow regions segmented by usingthe proposed contour model in (10), and the shadow regionssegmented by the contour model in (1) is presented in Fig. 8(b).The results of segmentation in Fig. 8(a) and (b) demonstratethe ability of the new contour model to distinguish the isolated

Fig. 8. (a)Shadow regions segmented by using the new contour model in (10).(b) Shadow regions segmented by using the contour model in (1). (c) Shadowregions segmented by using (10) with k = 0.9. (d) Shadow regions segmentedby using (10) with k = 0.5. (e) Shadow regions segmented by using (10) withk = 0.3.

shadow and the dark regions in the input image significantlyefficient than the regular control model in (1). Fig. 8(c)(e)present the segmentation results in (10) with k = 0.9, k = 0.5,and k = 0.3, respectively. Fig. 8(c) shows that k = 0.9 tends tooversegment shadow regions since the corresponding segmentsinclude other regions that are not as dark as the shadow. Forexample, the segmented regions in the top and bottom leftare segmented, although these are not shadow regions in thepanchromatic image. On the other hand, k = 0.7 and k = 0.5;the model tends to be undersegmented and misses some shadowregions, as shown in the buildings in the middle of Fig. 8(d).The same observation is very obvious in Fig. 8(e) for k = 0.3.These shadow segmentation results in Fig. 8(c)(e) confirmthat k = 0.7 is a reasonable choice, as discussed in Section III.


Fig. 9. (a) Result from the proposed algorithm. (b) Result from Daresmethod. (c) Result from the fixed masking method.

To isolate the shadow of man-made buildings from the clutterin the segmented regions, further processing is introduced, asdemonstrated in Fig. 6. Fig. 9(a) shows the final result of theproposed algorithm for the input image in Fig. 7(a). Note thatour algorithm shows good detection of the shadows of the man-made buildings in the scene when compared with the groundtruth in Fig. 7(c). Further note that our algorithm detects theshadow of the isolated buildings; however, the shadow of theconnected buildings is segmented as one region. In addition,the algorithm detects the shadows of large buildings such astowers and that of most of the small buildings. Our techniqueis able to remove all the clutter from the image, except the lakethat is connected to the shadow of the lower tower. Therefore,we can claim that the algorithm is robust for the clutter, whichis common in overhead scenes. It is worth noting that the waterbody is hard to separate from the shadow of the tower sincethe water body is adjacent and connected to the shadow of thetower, and they exhibit similar intensity feature characteristics.Hence, the water body and the shadow of the tower are treatedas one object by the geometric active-contour method.

For performance comparison, we evaluate the results of theproposed algorithm with the results of the method used in [19]for shadow detection of buildings in satellite images. Moreover,we implement the algorithm in [20] for comparison with ourtechnique since this algorithm handles a problem of shadowdetection by using the gray level in high-resolution overheadsatellite imagery. We refer to the method of [19] as Daresmethod and the method in [20] as the fixed masking method.

The results of Dares method and the fixed masking method forthe input image in Fig. 7(a) are presented in Fig. 9(b) and (c),respectively. Comparing our final result in Fig. 9(a) with resultsin Fig. 9(b) and (c), our proposed algorithm obviously per-forms better in overhead building shadow segmentation. Daresmethod detects all the buildings shadow regions in the image;however, the boundaries are not clear, and the result includesnumerous clutter. Similar to our result, Dares method detectsthe water body connected to the shadow of the lower tower. Thefixed masking method detects less clutter than Dares methodbut misses many shadow pixels as well.

To further validate performance, we process the imagesin Figs. 10(a)12(a) using our proposed algorithm. Thesepanchromatic images cover completely different scenes, and theshadow contents with the clutter take various challenging sce-narios. The corresponding results of the manually interpretedshadow maps, which are used as ground-truth data for theevaluation of the algorithms, are presented in Figs. 10(b)12(b),respectively.

For the image in Fig. 10(a), we present the results of the in-termediate processing in the proposed algorithm to show theeffect of each process in removing the clutter and isolating thedesired shadow. Fig. 10(c) shows the result of segmentation byusing (10). In this step, the algorithm clearly segmented thedark regions beside the shadow since the vegetation regionsand the water bodies exhibit gray-level characteristics similarto the shadow in the panchromatic images. In order to removethe clutter, we apply three steps as follows.

First, we construct the Otsus threshold on the histogram ofthe detected and segmented regions by the geometric activecontours after adding for each region a strip from the sur-rounding, as explained in Section III. Fig. 10(d) presents theresult of using Otsus threshold with global processing. Thisstep alleviates the clutter by mainly removing the clutter thatis brighter than the shadow, and this is clear in the roof of thebuilding in the bottom of the image and in some bodies of water.The result of this step is a set of potential shadow regions.

Second, we obtain the BCM for each region and removethe regions which have BC of more than 0.15, as shown inFig. 10(e). As discussed in Section III-B, the geometric filteruses the advantages of irregularity and complexity of the clutterthat results from the global processing. We find 0.15 to be rea-sonable for all our experiments in this paper to keep the shadowregions of man-made structures and to simultaneously filterout the maximum amount of the clutter. Comparing Fig. 10(d)and(e) shows the effect of using the value of 0.15, increasingthat value adds more clutter to Fig. 10(e), and reducing it mayremove the desired shadow regions.

Third, to remove the remaining clutter, we distinguish be-tween them and the shadow by constructing local histogramfor each region with its surrounding strip. Fig. 10(f) showsthe result of using Otsus threshold with local processing. Theapplication of local Otsus threshold of each local histogramclearly emphasizes the boundaries and removes the pixels thatcan be considered nonshadow in the perspective of bimodalhistogram for Otsus threshold, as described in Section III-B.Emphasizing the boundaries help in obtaining higher valuesfor BCM for the clutter since the clutter has irregular and


Fig. 10. (a) Input satellite image. (b) Ground truth of the shadow for man-made buildings. (c) Result of segmentation by (10). (d) Result of using Otsusthreshold with global processing. (e) Result of using the geometric filter.(f) Result of using Otsus threshold with local processing. (g) Result of usingthe geometric filter. (h) Shadow detection result by Dares method. (i) Shadowdetection by the fixed masking method.

Fig. 11. Comparison of shadow detection results. (a) Original image.(b) Shadow ground truth. (c) Shadow detection result by the proposed algo-rithm. (d) Shadow detection result by Dares method. (e) Shadow detection bythe fixed masking method.

complicated boundaries and in obtaining low values for thedesired shadow since it has regular boundaries, as we explainedin Fig. 3. Emphasizing the boundaries is clear in the shadowof the building in the bottom of the image, and removingnonshadow pixels is obvious in the remaining clutter.

Then, we employ the geometric filter again for each region toremove the regions that have a BC value above 0.15 to declarethe detection of the shadow of buildings. Fig. 10(g) shows theresult of the proposed algorithm. The result of Dares methodand the fixed masking method are shown in Fig. 10(h) and (i),respectively.

For the images in Figs. 11(a) and 12(a), the shadow detectionresults of the proposed algorithm are presented in Figs. 11(c)and 12(c), and the results of Dares method are shown inFigs. 11(d) and 12(d), respectively. The corresponding resultsfrom the fixed masking method are shown in Figs. 11(e) and12(e). For all the testing images in Figs. 1012, the proposedalgorithm detects shadow maps that are quite similar or veryclose to the ground-truth map. However, the results also include


Fig. 12. Comparison of shadow detection results. (a) Original image.(b) Shadow ground truth. (c) Shadow detection result by the proposed algo-rithm. (d) Shadow detection result by Dares method, and (e) shadow detectionby the fixed masking method.

the dark cars in the scene since they show well distinguishableboundary in grayscale images. In comparison, Dares methodand the fixed masking method fail to separate the shadowsfrom the surrounding vegetation in the images, and most ofthe vegetation and the water bodies are detected as shadow, asshown in the results. Moreover, Dares method and the fixedmasking method detect more cars than the proposed algorithm.The aforementioned qualitative evaluation experiments demon-strate that the proposed algorithm offers better performance,and it is robust against the clutter.

To further investigate the sensitivity of the segmentationalgorithm in (10) due to initialization, we repeat an examplefrom one of experiments with different level-set initializations,as shown in Fig. 13. The contours can be set at any place in theimage. Although these initializations are different, we obtainthe same final segmentation results, as reported in Figs. 912.Therefore, we do not repeat the segmentation results here.

Fig. 13. Different initializations for the level-set function in the proposedsegmentation algorithm.

These findings confirm our hypothesis that our segmentationmodel in (2) is not sensitive to the initialization since this modelconsists of the model in [21], which is not sensitive to theinitialization [21], [33], [36].

To quantitatively compare the performance, we evaluate theproposed algorithm using well-known quantitative metrics [6],[7], [13]. We use these metrics to evaluate the accuracy ofthe proposed building shadow segmentation to that of Daresmethod and the fixed masking method. Three types of metricsare adopted as follows: The first type of metrics is namedproducers accuracies, which measure the correctness of thealgorithm and indicate how well the true shadow and non-shadow pixels are correctly classified. The second type ofmetrics is the users accuracies that measure the precision ofthe algorithm and indicate the probabilities of correctly detectedand classified pixels (shadow and nonshadow). The third typeof metrics is the overall accuracy that measures if the percent-age correct. The producers accuracy of the shadow s andproducers accuracy of nonshadow ns are defined by

s =TP

TP + FN(11)

ns =TN

TN + FP(12)

where true positive (TP) is the number of shadow pixels, whichare correctly detected and identified when compared with theground truth, and false negative (FN) is the number of thetrue shadow pixels, which are detected and identified by analgorithm as nonshadow pixels. True negative (TN) denotesthe number of true nonshadow pixel that are correctly detected


TABLE IDETECTION ACCURACY MEASUREMENTS FOR FIG. 9

TABLE IIDETECTION ACCURACY MEASUREMENTS FOR FIG. 10

TABLE IIIDETECTION ACCURACY MEASUREMENTS FOR FIG. 11

TABLE IVDETECTION ACCURACY MEASUREMENTS FOR FIG. 12

and identified by an algorithm, and false positive (FP) is thenumber of nonshadow pixels that are detected and identifiedby an algorithm as true shadow pixels. The users accuracyof shadow s and the users accuracy of nonshadow ns aredefined by

s =TP

TP + FP(13)

ns =TN

TN + FN. (14)

The overall accuracy is defined by

=TP + TN

TP + TN + FP + FN(15)

where TP + TN is the number of correctly detected and iden-tified true shadow and nonshadow pixels and TP + TN + FP +FN is the total number of pixels in the input image.

Tables IIV show the values of the quantitative metricsfor the results in the experiments of images in Figs. 912.

The results in these tables suggest that the proposed algo-rithm significantly outperforms Dares method and the fixedmasking method in three categories (ns, s, ) and producescomparable performance in ns. Moreover, our method offerslower performance in s when compared with Dares methodand comparable results, on the average, to the fixed maskingmethod. The lower performance for s is due to changing thelabel of some pixels from shadow to nonshadow during theclutter detection process. The change in pixel labels is causedby higher brightness values of these pixels in the shadow area,and this, in turn, is reflected in the increased FN value andhence reduced s values, respectively. Although the increasedFN value is not desirable, it does not affect the overall accuracy of the proposed algorithm, as shown in the tabulated results.Note that a similar trend is observed in [13].

Our improved geometric active-contour model is derivedfrom the level-set-based model of [21]. In general, the reini-tialization step in the level set is usually time-consuming andalso has expensive computational cost [43]. The reinitialization


TABLE VPROCESSING TIME OF THE PROPOSED SHADOW DETECTION APPROACH

step in [21] is optional; hence, all our results are obtainedwithout reinitialization. In order to study the computational per-formance, Table V presents the elapsed time for processing theimages in this paper. All algorithms are implemented in MatlabR2012a on a laptop computer with an 1.65-GHz AMD E-450APU processor with 4-GB RAM. Note that the processing timeis highly dependent on the way Matlab scripts are written, thedetails in the image contents, and the dimension of the image.In Table V, we observe that, for most images, at least halfof the total processing time is consumed in implementing thelevel-set segmentation step. Furthermore, the processing timefor shadow separation step depends on the amount of clutter,the spatial distribution of clutter, and the steps to remove theclutter. Consequently, the processing time to remove clutter inFig. 11(a) is more than that in Fig. 7(a).

V. CONCLUSION

In this paper, we present a novel algorithm for overheadshadow detection and extraction from high-resolution panchro-matic satellite images. The proposed technique segments theshadow of man-made structures such as buildings by using thegray-level satellite image without using the color information.The algorithm is based on employing an improved geometricactive-contour model to handle a rather difficult problem ofshadow detection in satellite imagery. Our proposed model ofthe geometric active contours systematically favors the shadowand the similar dark regions in the input image. Further pro-cessing steps are introduced to isolate the shadow from cluttersuch as vegetation and water bodies. Both qualitative andquantitative experimental results using real images show thatthe proposed algorithm outperforms a comparable algorithmfor shadow and man-made structure segmentation. The resultof the proposed algorithm may be used for overhead man-madebuilding segmentation in high-resolution satellite images.

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Mohamed I. Elbakary received the B.S. and M.S.degrees in electronic engineering from the Universityof El Mansoura, El Mansoura, Egypt, in 1991 and1996, respectively, and the Ph.D. degree in electricaland computer engineering from the University ofArizona, Tucson, AZ, USA, in 2005.

Until 2000, he worked as a Researcher Assistantwith the Department of Computers and Systems,Electronic Research Institute (ERI), Giza, Egypt.After obtaining the Ph.D. degree, he joined the re-search group for hyperspectral image processing in

the Department of Electrical and Computer Engineering, University of SouthAlabama, Mobile, AL, USA, where he worked until 2008. He worked asa Visiting Assistant Professor and a Research Scholar with the Departmentof Computer Systems, Taif University, Taif, Saudi Arabia, and with the De-partment of Electrical and Computer Engineering, Old Dominion University,Norfolk, VA, USA, respectively. He is currently an Assistant Professor withERI. His research interests include image processing and analysis, patternrecognition, image registration, superresolution imaging (2-D/3-D), intelligentsystems, computer vision, and multispectral/hyperspectral image processingand analysis.

Khan M. Iftekharuddin (SM02) received the B.Sc.degree from the Bangladesh Institute of Technology,Dhaka, Bangladesh, in 1989 and the M.S. and Ph.D.degrees both in electrical engineering from the Uni-versity of Dayton, Dayton, OH, USA, in 1991 and1995, respectively.

He is currently a Professor and Chair with theDepartment of Electrical and Computer Engineering,the Director of the Vision Laboratory, a memberof a biomedical engineering program at Old Do-minion University, Norfolk, VA, USA. He is the

author of a book, several book chapters, and more than 150 refereed journaland conference papers. His research has been funded by different agenciessuch as the National Science Foundation, the National Institute of Health,Air Force Office of Scientific Research, Army Research Office, Air ForceResearch Laboratory, U.S. Department of Transportation, U.S. Departmentof Energy, Whitaker Foundation, Assisi Foundation, and FedEx Institute ofTechnology. His research interests include computational modeling of intel-ligent systems and reinforcement learning, stochastic medical image analysisfor tumor phenotype extraction, intersection of bioinformatics and medicalimage analysis, distortion-invariant automatic target recognition, biologicallyinspired human and machine centric recognition, recurrent networks for visionprocessing, probabilistic vision for robotics, emotion detection from speech anddiscourse, sensor signal acquisition and modeling, and optical computing andinterconnection.

Dr. Iftekharuddin is a Fellow of the International Society for Optics and Pho-tonics (SPIE) and a Senior Member of The Optical Society (OSA). He servesas an Associate Editor for multiple journals, including Optical Engineering andComputer Methods in Biomechanics and Biomedical Engineering: Imaging andVisualization.

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IEEE IMAGE PROCESSING Shadow Detection of Man-Made Buildings In