detecting the buildings from airborne laser scanner data by using fourier transform

T E C H N I C A L A R T I C L E

Detecting the Buildings from Airborne Laser Scanner Databy Using Fourier TransformF. Karsli1 and O. Kahya2

1 Department of Geomatics Engineering, Karadeniz Technical University, Engineering Faculty, 61080 Trabzon, Turkey

2 General Directorate of State Airports Authority, 34149 Istanbul, Turkey

KeywordsFourier Transform, Laser Scanning, Building,

GIS, Building Extraction

CorrespondenceF. Karsli,

Department of Geomatics Engineering,

Karadeniz Technical University,

Engineering Faculty,

61080 Trabzon, Turkey

Email: [email protected], [email protected]

Received: March 3, 2009; accepted:

December 17, 2010

doi:10.1111/j.1747-1567.2011.00703.x

Abstract

The automatic extraction of objects from airborne laser scanner data and imageshas been a topic of research for decades. Laser scanner data have proven tobe a powerful source for a wide range of 2D–3D geographic informationsystem object tasks. This paper presents the Fourier transform as an imageenhancement tool for determination of buildings from the images generatedby laser signal. Spatial and frequency domain filtering techniques have beenutilized for extraction of building from enhanced images. While Gaussianand Wiener filterings were selected in frequency domain, Sobel and Unsharpwere selected for the spatial domain. The boundaries of buildings have beendelineated from the generated images, which were obtained from inverseFourier transform, by using edge detectors, such as Canny, Sobel, and Prewitt.Frequency domain filters using Fourier transformation were compared withspatial domain filters in the way of kernel function and windows. The reasonfor doing the filtering in the frequency domain is that it is computationallyfaster to perform two-dimensional Fourier transforms and a filter is moreapplicable than to perform a convolution in the spatial domain. Results showedthat using Fourier transformation has a great advantage in enhancing imagesand detecting the buildings on images. Filtering in the frequency domain ismore efficient computationally than spatial domain filtering when the filtersize is big. The conclusion proved that Fourier transformation can be used asan image enhancement tool to detect and extract buildings automatically.

Introduction

More than 50% of the world population lives inurban/suburban areas, so detailed and up-to-datebuilding information is of great importance to everyresident, government agencies, and private compa-nies (utilities, real estate, etc.). Moreover, accuratethree-dimensional (3D) surface models in urbanareas are essential for a variety of applications,such as urban planning, environmental monitor-ing, geo-information systems, traffic management,and military operations.1 Remote sensing is one ofthe most efficient ways to acquire and extract therequired information.2 However, the traditional man-ual building extraction from raw imagery is highlylabor-intensive, time-consuming, and expensive.

During the past two decades many researchers in pho-togrammetry, remote sensing, and computer visioncommunities have been studying ways to developautomatic or semiautomatic approaches for buildingextraction and reconstruction.3,4

Building is the key information of 3D city models,so extraction of buildings by remote sensing becomesan important step in order to build a digital city.5 Themain data sources used for building extraction areaerial photos, high-resolution satellite images, andairborne laser scanning rangefinder data. SAR data(jointly used with LIDAR and/or optical data) canalso be used for building detection in urban areas.6,7

Therefore, there are generally three types of meth-ods to extract buildings automatically; that is, image

Experimental Techniques 36 (2012) 5–17 © 2011, Society for Experimental Mechanics 5

Detecting the Buildings from LIDAR by Using FT F. Karsli and O. Kahya

Figure 1 Principle of topographic LIDAR.

domain method,8 the digital surface model (DSM)domain method,9 and the combined image and DSMmethod.10–12 In this study, the image domain methodwas implemented for building extraction. All methodsuse the data that can be obtained from both Lidar andoptical and/or radar images (airborne and satellite).

Airborne laser scanning (LIDAR) is a new technol-ogy in which several sensors are integrated to obtain3D coordinates of points on the earth. It makes useof precise GPS tools to determine the position of thesensor, inertial measurement unit (IMU) to determinethe attitude of the sensor, and narrow laser beams todetermine the range between the sensor and the tar-get points (Fig. 1). LIDAR data are dense, with highaccuracy, but one still needs to extract higher-levelfeatures from it.1 There are a number of very opti-mistic claims regarding the accuracy of LIDAR data. Tofully assess the accuracy, one must consider the errorsinherent in the three components of the system (laserscan, GPS, and IMU). It is conservatively estimatedthat the accuracy of LIDAR, as determined from errorpropagation, is about 15 cm in elevation and horizon-tal position. This can be thought of as typical resultsfrom LIDAR surveys. This assumes that the systemis properly calibrated and functioning correctly andthat the surface terrain conditions are ideal. As a ruleof thumb, horizontal accuracy is often claimed to be1/2000th of the flying height. Vertical accuracies ofbetter than 15 cm are obtainable when the sensoraltitude is below 1200 m and up to 25 cm when theoperating altitude is between 1200 and 2500 m.13

Airborne laser scanner data have proved to be apromising data source for various mapping and 3Dmodeling tasks. One of the most important applica-tions in remote sensing area is extraction and mod-eling of buildings to create 3D city models. Several

studies related to the topic have been published inrecent years.11,14–22 The building extraction and mod-eling process can typically be divided into two steps:building detection and building reconstruction. At thefirst stage, buildings have to be distinguished from theground surface and other objects, such as trees. Then,3D models of buildings can accurately be created.

Methods for building detection are often basedon stepwise classification of the data to eliminateobjects. Some methods use aerial images in addi-tion to the laser scanner data for automatic buildingextraction.11,20 Classification can be pixel-based, butsegmentation is normally applied in some stage ofthe process to obtain regions. Segmentation of laserscanner data has been studied in, for example, Refs.22–24. The building detection stage can be avoidedif an up-to-date map is available as a basis for build-ing reconstruction.11,15,19 To construct 3D city modelsautomatically, two successive steps have to be con-sidered. The first one is the automatic segmentationof the point cloud into three classes which are ter-rain, vegetation, and buildings. Once the point cloudis segmented, the modeling of buildings can start.Two types of approaches called ‘‘model-driven’’ and‘‘data-driven’’ approaches in the literature are pro-posed for the problem. The model-driven approachessearch the most appropriate model among primitivebuilding models contained in a model library.16 Onthe other hand, data-driven approaches try to simu-late each part of the building point cloud for obtainingthe nearest or the more faithful polyhedral model.25

Many methods are proposed in order to carry outthis procedure, such as region growing, 3D Houghtransform, and RANSAC.

The Fourier transform is an important imageprocessing tool used to decompose an image infrequency domain, while the input image is thespatial domain equivalent. In the Fourier domainimage, each point represents a particular frequencycontained in the spatial domain image.26 The Fouriertransform produces a complex number-valued outputimage that can be displayed with two images, eitherwith the ‘‘real’’ and ‘‘imaginary’’ part or with‘‘magnitude’’ and ‘‘phase.’’ In image processing, onlythe magnitude of the Fourier transform is displayed, asit contains most of the information of the geometricstructure of the spatial domain image. However, ifthe Fourier image is re-transformed into the correctspatial domain after some processing, both magnitudeand phase of the Fourier image must be preserved.

The aim of this study is to determine the build-ing details by using Fourier transformation on theenhanced images produced by the LIDAR data in

6 Experimental Techniques 36 (2012) 5–17 © 2011, Society for Experimental Mechanics

F. Karsli and O. Kahya Detecting the Buildings from LIDAR by Using FT

the frequency domain. In the phase of the Fouriertransform, the aim is to improve the image qual-ity in the frequency domain and to reconstruct theimage by taking inverse Fourier transformation. Theadvantage of Fourier transform is that it is very fast,easy, and able to be applied in a broad range ofimage processing applications, including enhance-ment, analysis, restoration, and compression. As asummary, we research on how to utilize the intensityimage generated by the laser signal, employ frequencydomain filtering for image enhancement, and detectand extract the building in the intensity images byusing Fourier Transformation.

Study Area and Data Used

The LIDAR data used in this paper are free sampledata provided by the International Society forPhotogrammetry and Remote Sensing, CommissionIII Working Group 3. The LIDAR data were collectedin the second phase of a EuroSDR (EuropeanSpatial Data Research Organization) project on laserscanning, and their range includes the Vaihingen/Enztest field in southern Germany and the Stuttgartcity center.27 The LIDAR data were generated usingan Optech ALTM1201 laser scanner, FOTONOR AS,recording both first and last return pulse data. Threedifferent regions or areas were chosen because oftheir diverse feature content (open fields, vegetation,buildings, roads, railroads, rivers, bridges, power lines,

Table 1 Features of interest of the sites

Site Features of interest

Site 2 Large buildings, irregularly shaped buildings, road with bridge

and small tunnel, and data gapsSite 3 Densely packed buildings with vegetation between them,

building with eccentric roof (bottom left corner), open space

with mixture of low and high features, and data gapsSite 4 Railway station with trains (low density of terrain points) and

data gaps

water surfaces, etc.) (Table 1). The point density forthe City is roughly 0.67 point/m2 (point spacing:1.0–1.5 m). Digital surface model was generatedusing Surfer software package for the three regionsnamed site 2, site 3, and site 4. An ortho-photo mapof the study areas is shown in Fig. 2.

In the preparation phase of the laser data, first pulselaser echo data were selected, due to the fact that itrepresents the objects (building, bridge, trees, etc.)above ground. The LIDAR data were interpolatedby using a Kriging interpolation method via Surfer8.0 (Golden Software) software. The basic premiseof Kriging interpolation is that every unknown pointcan be estimated by the weighted sum of the knownpoints. This method works best for known valuesthat are not evenly scattered. Just after the interpo-lation, the grayscale images obtained from previousstep were imported to MATLAB (MathWorks) soft-ware (Fig. 3). Filtering operations, enhancement with

Figure 2 (a) Ortho-photo map of study area; (b) DSM of site 2 (left), site 3 (middle), and site 4 (right).



Figure 3 Grayscale images of site 2, site 3, and site 4 generated from Surfer 8.0.

Fourier Transform, and edge detection were imple-mented in the Image Processing Toolbox of MAT-LAB. Additionally, spatial domain filtering and imageenhancement techniques have been applied to theimages for validation.

Methodology

The methodology of this study is as follows. At first,raw Lidar point cloud data were transformed intoa regular grid format (1 × 1 m) via Surfer softwareby using the Kriging interpolation method. Then, forimage enhancement and restoration, a Fourier trans-form was applied to the original image. Gaussianand Wiener filterings were performed separately forthe transformed image in the frequency domain toreduce the noise and blurring effects and to improvethe image quality. In addition, 2D spatial filtering(Sobel and Unsharp) was performed on the originalimages in spatial domain. Filtering results obtainedin both domains (frequency and spatial) were com-pared. A reconstructed image was obtained by takingthe inverse Fourier transform. Finally, the buildingson the reconstructed image were detected via Canny,Sobel, and Prewitt edge detectors and the results fromthe three edge-detection methods were compared.

Fourier Transformation

The Fourier transform is one of the most importanttools that are extensively used not only for under-standing the nature of an image and its formation butalso for processing the image. It is a representation ofan image as a sum of complex exponentials of varyingmagnitudes, frequencies, and phases. An image is atwo-dimensional (2D) signal and can be viewed as asurface in 2D space. Using a Fourier transform, it has

been possible to analyze an image as a set of spatialsinusoids, each of which has a precise frequency, invarious directions. We give a short description for the2D Fourier transformation.26,28,29

Two-dimensional Fourier transform of a continu-ous function f (x, y) is denoted by:

F(ω, ψ) =+∞∫

−∞

+∞∫−∞

f (x, y)e[−j2π(ωx+ψy)]dydx (1)

where F(ω, ψ) is the Fourier transform, with thefrequency components ω and ψ corresponding tox and y, respectively, and f (x, y) is the originalcontinuous function. The corresponding inverse 2DFourier transform is given as:

F(x, y) =+∞∫

−∞

+∞∫−∞

f (ω, ψ)e[j2π(ωx+ψy)]dψdω (2)

When the function or signal is represented in discreteform using a sequence of discrete samples, suchas f (x) = {f (0), f (1), . . . , f (N−1)}, the correspondingFourier transform of the discrete signal is called discreteFourier transform (DFT). In this study, the following2D DFT formulation is used. The 2D discrete Fouriertransform of a 2D signal f (x, y) of dimension M × Nwith integer indices x and y running from 0 to M − 1and 0 to N − 1 is represented by:

F(u, v) = 1

MN

M−1∑x=0

N−1∑y=0

f (x, y)e[−j2π( uxM + vy

N )] (3)

The equivalent 2D inverse DFT is given as:

F(x, y) =M−1∑u=0

N−1∑v=0

F(u, v)e[j2π( uxM + vy

N )] (4)



Frequency Domain Filtering

Filtering in the frequency domain is quite simpleconceptually. The foundation for linear filteringin both the spatial and frequency domains is theconvolution theorem, which may be written as:

f (x, y) × h(x, y) ⇔ H(u, v) F(u, v) (5)

and conversely,

f (x, y) h(x, y) ⇔ H(u, v) × F(u, v) (6)

Here, the symbol × indicates convolution of the twofunctions and the expressions on the sides of thedouble arrow constitute a Fourier transform pair. Fil-tering in the spatial domain consists of convolvingan image f (x, y) with a filter mask, h(x, y). Accord-ing to the convolution theorem, we can obtain thesame result in the frequency domain by multiply-ing F(u, v) by H(u, v), the Fourier transform of thespatial filter. It is customary to refer to H(u, v) asthe filter transfer function.29 The filtering procedureis composed of the following steps: preprocess theinput image (f (x, y)), estimate the Fourier trans-form (F(u, v)), define the filter function (H(u, v)),convolve (H(u, v) × F(u, v)), calculate the inverseFourier transform, and post-process the filtered image(g(x, y)). The preprocessing stage might encom-pass procedures, such as determining image size,obtaining the padding parameters, and generating afilter.

Gaussian filtering

In practice, it is common to define the discretedomain transform directly in the DFT frequencyspace. Several filter functions are widely used as thetransfer functions for an N × N pixel image. Those arezonal low-pass filter, zonal high-pass filter, Gaussianfilter, Butterworth low-pass filter, and Butterworthhigh-pass filter.30 The use of the Gaussian kernel forsmoothing has become extremely popular. This hasto do with certain properties of the Gaussian as wellas several application areas, such as edge finding andscale space analysis. This filter is applied to prepareimages for further processing. The Gaussian filter isseparable:

h(x, y) = g2D(x, y) = g1D(x)g1D(y)

=(

1√2πσ

e−

(x2

2σ2

)) (1√2πσ

e−

(y2

2σ2

))(7)

where x, y: 0,1,2, . . . ,N; h(x, y), g2D (x, y), and g1D

(x,y) are the Gaussian filter functions.

Wiener filtering

Inverse filtering is a restoration technique forde-convolution. However, inverse filtering is verysensitive to additive noise.29 The Wiener filteringprocess executes an optimal trade-off between inversefiltering and noise smoothing. It removes the additivenoise and inverts the blurring simultaneously.The Wiener filtering minimizes the overall meansquare error in the process of inverse filtering andnoise smoothing.22 The Wiener filtering is a linearestimation of the original image. The approach isbased on a stochastic framework. The orthogonalityprinciple implies that the Wiener filter in the Fourierdomain can be expressed as follows:

W(f1, f2) = H∗(f1, f2)Sxx(f1, f2)

|H(f1, f2)|2Sxx(f1, f2) + Sηη(f1, f2)(8)

where Sxx(f1, f2), Sηη(f1, f2) are respectively powerspectra of the original image and the additive noise,additionally H(f1, f2) is the blurring filter (for otherformulas see Refs. 31,32).

Spatial Filtering

In general, filtering in the spatial domain iscomputationally more efficient than filtering infrequency domain when the filters are small. Thedefinition of small is a complex question, the answerof which depends on factors such as the machine andalgorithms used and on issues such as the sizes ofbuffers, how well complex data are handled, and ahost of others beyond the scope of this discussion.29

The term spatial domain refers to the image plane itself,and methods in this category are based on directmanipulation of pixels in an image. In this study,Sobel and Unsharp filters in the image domain wereused to show differences between the results fromspatial domain and frequency domain filtering. Thesefilters are very effective for detecting edges in images.

Sobel filtering

The Sobel filtering technique is used in image process-ing, particularly within edge-detection algorithms.Technically, it is a discrete differentiation operatorthat computes an approximation of the gradient ofthe image intensity function. At each point in theimage, the result of the Sobel filter is either the corre-sponding gradient vector or the norm of this vector.The Sobel filter is based on convolving the image witha small, separable, and integer-valued filter in boththe horizontal and the vertical directions and is there-fore relatively inexpensive in terms of computations.Sobel filtering is a three-step process. Two 3 × 3 filters



(called kernels) are applied separately and indepen-dently. The weights of these kernels (horizontal: [−10 1; −2 0 2; −1 0 1], vertical: [1 2 1; 0 0 0; −1 −2 −1])are applied to pixels in the 3 × 3 region (for detailedinformation see Ref. 28).

Unsharp filtering

The Unsharp filter is a simple sharpening operatorthat enhances edges via a procedure that subtractsan Unsharp or smoothed version of an image fromthe original image. The Unsharp filtering techniqueis commonly used in the photograph and printingindustries for crispening edges. Unsharp maskingproduces an edge image of g(x,y) from an input imageof f (x, y) via the following equation:

g(x, y) = f (x, y) − fsmooth(x, y) (9)

where fsmooth(x, y) is a smoothed version of f (x, y)(Fig. 4).33

Edge Detection

Edge detection is a fundamental tool used in mostimage processing applications to obtain informationfrom the frames as a precursor step to featureextraction and object segmentation. This processdetects outlines of an object and boundaries betweenobjects and the background in the image. The edge-detection operator is calculated by forming a matrixcentered on a pixel that is chosen as the center ofthe matrix area. If the value of this matrix area isabove a given threshold, then the middle pixel isclassified as an edge. All the gradient-based algorithmshave kernel operators that calculate the strength ofthe slope in directions which are orthogonal to eachother, commonly vertical and horizontal.34–36

Prewitt edge detector

The Prewitt operator measures two components. Thevertical edge component is calculated with filter

Kx([−1 0 1; −1 0 1; −1 0 1]) and the horizontaledge component is calculated with filter Ky ([1 1 1;0 0 0; −1 −1 −1]). |Kx| + |Ky| give an indicationof the intensity of the gradient in the current pixel.Only a 3 × 3 filter size can be used with this filter.Depending on the noise characteristics of the imageor streaming video, edge-detection results can vary.Gradient-based algorithms such as the Prewitt filterhave a major drawback of being very sensitive tonoise. The size of the kernel filter and coefficientsare fixed and cannot be adapted to a given image.An adaptive edge-detection algorithm is necessaryto provide a robust solution that is adaptable to thevarying noise levels of these images to help distinguishvalid image content from visual artifacts introducedby noise.35

Roberts edge detector

One way to find edges is to explicitly use a {+1, −1}operator that calculates I(xi) − I(xj) for two pixelsi and j in a neighborhood. The Roberts kernelsare in practice too small to reliably find edges inthe presence of noise.36 These kernels attempt toimplement this using two kernels (horizontal: [1 0;0 −1], vertical: [0 1; −1 0]). Although these are notspecifically derivatives with respect to x and y, theyindicate derivatives with respect to the two diagonaldirections. These can be considered components ofthe gradient in such a coordinate system. So, we cancalculate the gradient magnitude by calculating thelength of the gradient vector:

g =√

(g1 × f )2 + (g2 × f )2 (10)

Canny edge detector

The Canny algorithm uses an optimal edge detectorbased on a set of criteria which include findingas many real edges in the image as possible byminimizing the error rate, marking edges as close aspossible to the actual edges to maximize localization,

Figure 4 Calculating an edge image for Unsharp filtering.



and finally marking edges only once in a given imagefor a minimal response. According to Canny, theoptimal filter that meets all three criteria can beefficiently approximated using the first derivative ofa Gaussian function:

G(x, y) = 1

2πσ 2e

x2+y2

2σ2 (11)

where G(x, y) is the Gaussian function. As can beseen from the formula above, Canny determinededges by an optimization process and proposed anapproximation to the optimal detector as the maximaof the gradient magnitude of a Gaussian-smoothedimage. The popularity of the Canny edge detector canbe attributed to its optimality according to the threecriteria of good detection, good localization, and singleresponse to an edge.37

Results

Performing the Fourier transform

Visual analysis of the spectrum is an importantaspect of working in the frequency domain. Forfrequency domain filtering, two filtering methodshave been selected for comparison, Gaussian andWiener. The Fourier transform was performed foreach reconstructed image of airborne laser pointclouds. The distribution of raw laser points is irregularand the dataset is huge. Therefore, one highly effectivemethod of data organization is required. In this article,point clouds were interpolated by the Kriging methodwith a specified grid interval. The images that wereproduced with the measured data (Z value) arerecomputed using grid data in the Kriging model.These images were then transformed into Fourierspectra for each test site. After the spectrum hasvisually been enhanced by a log transformation,centered images of every site have been attainedin the Fourier domain. The MATLAB software and itsImage Processing Toolbox were used for developingthe system. The results were found as follows.

The first column in Fig. 5 shows the originaltransforms to the center of the frequency rectangle ofimages by Fourier transform. When the first columnof Fig. 5 is examined, the centered images in Fourierspectrum appear bright. These images represent theFourier spectrums for each site. Finally, they wereconverted to real enhanced images by using inverseFourier transform.

Implementing of Gaussian and Wiener filtering

In this study, Gaussian filtering algorithms wereapplied to the transformed image in the frequency

domain. A filter size of 5 × 5 and standard deviationof σ = 0.5 were chosen to avoid truncation effectsin the frequency domain due to the infinite extentof the Gaussian. Also, a 7 × 7 and 9 × 9 filter sizewas studied. After the Gaussian filtering with filtersize of 5 × 5 (with standard deviation σ = 0.5) isperformed, filtered images were analyzed and arepresented in the second column of Fig. 5. From thisfigure, it can be seen that noise effects on the imageswere decreased. As the filter size was increased, imagequality decreased and saturation increased. However,few details of the image were lost. Therefore, it canbe concluded that using 5 × 5 filter size is appropriatefor enhancing the images in the frequency domain(Fig. 5).

As pointed out, the same process and rules havebeen fulfilled for the Wiener filtering. The thirdcolumn of Fig. 5 shows the results of the Wienerfiltering that was performed on the transformedimages in Fourier domain. In Fourier domain,transformed images appeared like the point clouds.In the image from each site, it can be seen that theWiener filtering has the same effect with regard toall filter sizes. In Fig. 5, only 5 × 5 filter results havebeen presented. Therefore, it can be concluded thatusing the 5 × 5 filter size might also be acceptablefor Wiener filtering in frequency domain. As a result,the filter with 5 × 5 size was selected for the inverseFourier transformation for both the selected filteringmethods.

Inverse Fourier Transform

As noted in section ‘‘Fourier Transformation,’’ theinverse of a transformation is similar to the originalimage. In this phase, the inverse Fourier transformis performed on the filtered image that is obtainedfrom the transformed image in frequency domainto reconstruct the images in spatial domain. Resultimages of all sites are shown in Fig. 6. They havebeen produced from the Fourier spectrum images thatare generated by the filtering techniques with 5 × 5filter size applying an inverse Fourier Transformation.In Fig. 6, the result images, obtained by takingan inverse Fourier transform, are presented. Uponinverse Fourier transformation, the reconstructionimage was obtained.

Filtering in the spatial domain (Sobel and Unsharpfilters)

The spatial domain filtering techniques, Sobel andUnsharp, were used for benchmarking in this study.The results obtained in the spatial domain werecompared to those in the frequency domain. Thus,



Figure 5 The images after Fourier transform, Gaussian filtering, and Wiener filtering with 5 × 5 filter size; site 2 (up), site 3 (middle), and site 4 (bottom).

enhancement accuracy of the images with Fouriertransformation has been tested in terms of theirusability. In this phase, spatial filtering results werecompared to the filter results applied in frequencydomain for validation. Figure 7 shows the results of

Sobel and Unsharp filtering that have been performeddirectly on the original images in the spatial domain.For Sobel filtering a 3 × 3 Sobel mask was used. TheUnsharp filtering technique was applied to imagesthat were generated from the laser range data.

Figure 6 The images after inverse Fourier transform is performed (for Gaussian filter with 5 × 5 size).



Figure 7 Filtering result images of all sites in the spatial domain.

Unsharp filtering was provided to sharpen the images.The resulting images belonging to all sites are depictedin Fig. 7. Visually, the images produced by Unsharpyielded a better result when compared to the imagesfiltered by Sobel.

Edge detection

After performing filtering in the frequency domainto de-noise and smooth the images, followed bycomputing the inverse FFT, building details in imagesfrom the three different sites were detected. Themost powerful edge-detection method is the Cannytechnique. The Canny method differs from otheredge-detection methods in that it uses two differentthresholds and includes the weak edges in the outputonly if they are connected to strong edges. Thismethod is therefore less likely than the others to befooled by noise and more likely to detect true weakedges.34 All edge detectors were implemented on theimages enhanced by spatial and frequency domainfiltering techniques. Figure 8 shows very effectiveresults of detection in the spatial domain. These resultsshow the details of the images that were obtained afterCanny edge detector was performed. The details ofthe images were mostly found. The images representextracted building details of the different study sites.

Also, Fig. 8 shows the building details obtained whenother edge detectors (Prewitt and Sobel) were usedon the images. Visually, the building details whichwere obtained from the Canny edge detector appearclearer than other edge detectors.

The details of the images obtained by Canny,Prewitt, and Sobel edge detectors are shown in Fig. 9.The building boundaries depicted in this figure weredetected after spatial filtering techniques were used.Figure 9 shows the building details obtained when theother edge detectors (Prewitt and Sobel) were usedon the images. For the spatial domain, the buildingdetails detected by the Canny edge detector are betterthan the other edge detectors.

Discussion

A Fourier transform was performed on each imageand the results of the transformation were relativelygood. In this study, the Fourier transform was used asan image enhancement tool before having obtainedthe renewed images, reconstructed by taking theinverse Fourier transformation. The reconstructedimages using the inverse Fourier transformation werethen used to determine the building details. Forthe Fourier transformation, two filtering methods,



Figure 8 The building details after detection by the three edge-detection methods for the frequency domain.

Gaussian and Wiener, have been applied to imagesto improve the image quality. Gaussian filters haveassumed a central role in image filtering because ofresearch in models of human vision, methods foredge-detection, results in scale space, and techniquesfor accurate measurement of analogue quantitiesbased on digital data.30 This filter removes thenoises. In this study, the noises in the frequency

domain images were successfully removed by usingGaussian filtering. The Wiener filtering executesan optimal trade-off between inverse filtering andnoise smoothing. It removes the additive noise andinverts the blurring simultaneously. In addition tofrequency domain filters, Sobel and Unsharp filterswere implemented to images for improving theirquality in the image domain and benchmarking the



Figure 9 The building details after detection by the three edge-detection methods for the spatial domain.

results obtained in frequency and spatial domains.Unsharp filtering was utilized to enhance the edgeof the images and Sobel filtering was provided tosmooth the original image before transformation.Gaussian filtering has been executed on the image inthe Fourier domain. For 5 × 5, 7 × 7, and 9 × 9 filtersize with standard deviation of σ = 0.5, this filteringwas separately applied on the transformed images infrequency domain. The 5 × 5 filter was found to besatisfactory to reduce the noise effect. Also, Wienerfiltering with different kernel sizes was performed, butthis filter did not give as good a result as the Gaussian.

However, Wiener filtering with a 5 × 5 kernel sizegave the same results as that of Gaussian. When theresults obtained from both domains were examined,details of the buildings were determined with equalquality. This shows that the Fourier Transformationcan be used as an enhancement tool in images forbuilding detection.

At the edge-detection stage, Canny, Prewitt, andSobel edge-detection operators were used (Fig. 9).Canny edge detectors differ from other edge-detectionoperators so as to use two different thresholds. That is,this method is powerful enough to detect strong and



weak edges of the image. In conclusion, all details ofthe image were detected by the three different edgedetectors. At the same time, the building details in allsites were more finely detected on the transformedand enhanced images. Therefore, the results gener-ated by the Canny method were the most satisfactory.

The Fourier transformation belongs to a classof digital image processing algorithms that can beutilized to transform a digital image into the frequencydomain. After an image is transformed and describedas a series of spatial frequencies, a variety of filteringalgorithms can be easily computed and applied,followed by retransformation of the filtered imageback to the spatial domain. This technique is usefulfor performing a variety of filtering operations thatare otherwise very difficult to perform with a spatialconvolution. So, it can be said that the contributionof this study is to enhance the images, createdfrom laser range data, in the frequency domain andautomatically detect building details on the images.

Conclusions

This article describes an automatic approach forbuilding extraction from LIDAR data. This studytries to detect the building details from airbornelaser scanner data by using grayscale images thatwere reconstructed from laser data. In the study,the images were processed using MATLAB software.In this phase, several image processing techniquesin spatial and frequency domains were applied onimages. One of these is the filtering techniques.Four different filtering techniques were used inthis study. As a good filtering operation, we usedGaussian filtering to avoid truncation effects in thefrequency domain. A filter size of 5 × 5 and standarddeviation of 0.5 were selected. After that, a Fouriertransformation was performed on the filtered image(image without noise) for reconstructing the originalimage prior to detecting the lines of the buildings. Inthe frequency domain, use of Gaussian filtering hasgiven satisfactory results for detecting building details.For detecting the building details of an image, Canny,Prewitt, and Sobel edge detectors were applied on theimage that was obtained by taking the inverse Fouriertransformation. Canny edge operator has given thebest result among all the detectors. As comparedwith the results in frequency and spatial domains,there were no explicit differences between buildingboundaries that were extracted from edge detectorsusing two domains. As result, it can be said thatFourier transformation as a tool image enhancementis as useful as the spatial domain technique. Results

suggest that using the Fourier transformation has agreat advantage in enhancing images and detectingbuildings in the images. Filtering in the frequencydomain is computationally more efficient than in thespatial domain when the filters are large. At the end ofthis study, it was shown that Fourier transformationcan be used as an image enhancement tool toautomatically detect and extract buildings. Therefore,we recommend the use of Fourier transform in otherresearch and in the filtering methods for buildingdetection. In conclusion, it can be said that aftersuitably filtering the images, the laser data can beused for the building detection.

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detecting the buildings from airborne laser scanner data by using fourier transform

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