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Optimisation of building detection in satellite images by combining multispectral classification and texture filtering
ISPRS Journal of Photogrammetry & Remote Sensing 54 Ž1999. 50–60
Optimisation of building detection in satellite images by combining multispectral classification and texture filtering Yun Zhang
)
Institute of Planetary Exploration, German Aerospace Centre (DLR), Rudower Chaussee 5, 12489 Berlin, Germany
Abstract Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. In this study, a technique is described which attempts to detect urban buildings in two stages. The first stage is a conventional multispectral classification. In the second stage, the classification of buildings is improved by means of their spatial information through a modified co-occurrence matrix based filtering. The direction dependence of the co-occurrence matrix is utilised in the filtering process. The method has been tested by using TM and SPOT Pan merged data for the whole area of the city of Shanghai, China. After the co-occurrence matrix based filtering, the average user accuracy increased by about 46% and the average Kappa statistic by about 57%. This result is about 26% better than the accuracy improvement through normal texture filtering. The method presented in this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries. q 1999 Elsevier Science B.V. All rights reserved. Keywords: building detection; satellite images; multispectral classification; co-occurrence matrix based filtering
1. Introduction Buildings are one of the most important classes in urban land-cover and land-use classification. The current building distribution and development in a city are essential information for urban environmental investigation and urban planning. However, the collection of such information is not easy. Conventional interpretation of aerial photos is very timeconsuming and expensive. Satellite images are an important information source for earth observation )
Tel.: q49-30-67055-391; Fax: q49-30-67055-384; E-mail: [email protected]
and provide the current information periodically at low cost. They show a very promising perspective for urban land-cover and land-use classification, particularly with very high resolution Ž1–4 m. satellite images, which will be available in the near future ŽFritz, 1996; Stoney, 1996; Meinel et al., 1998; Schmitt et al., 1998.. Thus, it is meaningful and also necessary to develop methods for fast acquisition of up-to-date urban information, such as building distribution and development, from satellite images. The conventional multispectral classification methods have been successfully used for the detection of areal objects from satellite images. However, they are still problematic for the detection of object
0924-2716r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 2 7 1 6 Ž 9 8 . 0 0 0 2 7 - 6
Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
classes in urban areas. The reasons are: Ž1. The objects in urban areas are very complicated. They are characterised more through their structure than through their spectral reflection properties. Ž2. The conventional multispectral classification methods extract the object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. The shortcomings of the conventional multispectral classification in urban areas was also attested by many studies ŽHsu, 1978; Gong and Howarth, 1990; Gong et al., 1992; Fung and Chan, 1994; Johnsson, 1994; Harris and Ventura, 1995; Barnsley and Barr, 1996.. In order to extract urban object classes accurately, it is necessary to include the spatial Žor structural. information in the classification as well as the spectral information. However, the methods of modelling, enhancing, and extracting spatial information from digital images are still quite limited ŽAlbertz, 1991.. A project was therefore carried out in order to develop new methods for the acquisition of important urban object classes such as buildings, water system and street greenery from satellite images. In this paper, a special texture filtering method is presented, which is suitable for increasing the accuracy of the multispectrally classified regular features such as urban buildings. 2. Test area and data sources The test area was the whole city of Shanghai, China. It covers an area of approx. 30 km = 30 km. The buildings in the city, which are approx. 12 to 20 m in width, are mostly residential houses built since 1949, particularly since 1979. This kind of buildings is distributed relatively regularly and forms regular urban features in satellite images. They cover 50% of the entire residential area and develop most dynamically. Therefore, their detection was the main aim of this study. The other three kinds of buildings in the city are high buildings with more than 10 floors, old small buildings which are small and set closely to each other, and primitive houses which are very small and irregular ŽShanghai’s Remote Sensing Office, 1993.. Because of space limitations, the detection of high buildings is not described here. The relevant details can be found in the paper of Zhang
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Ž1998.. The old small buildings and the primitive houses are too small and too close to each other. They can not be recognised in merged TM–SPOT data, even by visual interpretation, so they were not extracted in this study. For the detection of the buildings which are 12 to 20 m in width, merged TM–SPOT data Ž10 m pixel size. were used. The merging method was the IHS transformation. The data used for the merging were the TM bands 3, 4 and 7 taken by Landsat 5 on 18 May 1987 and the SPOT Pan data taken by the sensor HRV2 of SPOT 1 on 25 October 1989. Because of the limited choice, the used source data have a time difference of two years. This caused an accuracy decrease of the results to some extent. However, it did not influence the demonstration of the performance of the new method presented in this study, because the time difference decreased the accuracy of the multispectral classification, not of the texture filtering—the main part of this study. The unsupervised ISODATA clustering method ŽSwain, 1973; ERDAS, 1997. was used for the multispectral classification of the buildings. The result of the multispectral classification is shown in Fig. 1. 3. Common methods of texture analysis 3.1. Normal texture analysis Methods of texture analysis are frequently used for the recognition and distinction of different spatial characteristics in digital images. In performing texture analysis, the grey value relationships between the current pixel and the pixels next to it are calculated on the basis of a certain texture measure Že.g., mean, standard deviation, contrast, correlation, energy, entropy, etc... The grey values of the output image represent the local texture criterion of the input image. In industrial pattern recognition, the average grey values of the output images are usually measured and used as criteria to distinguish input images with different textures ŽHaberacker, 1985; James, 1987; ¨ Jahne, 1989; Bassmann and Besslich, 1993.. ¨ ¨ In the classification of satellite images, texture analysis methods are often used to introduce the spatial information of different object classes into the classification. Normally, the output image generated
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Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
Fig. 1. Buildings extracted with multispectral classification from the merged TM–SPOT data of the city of Shanghai Ž500 = 360 pixel sections.: Ža. section in the northern part of the city, Žb. section in the southern part of the city.
by the texture analysis is classified directly or used as an additional band together with other multispectral bands in classification ŽHsu, 1978; Gong and Howarth, 1990; Gong et al., 1992.. 3.2. Grey Õalue co-occurrence matrix A more effective texture analysis method is the grey value co-occurrence matrix method ŽHaralick,
1986; Bassmann and Besslich, 1993.. The goal of ¨ the co-occurrence matrix method is also to describe the grey value relationships in the neighbourhood of the current pixel, as in the normal texture analysis. However, the grey value relationships are analysed in the co-occurrence matrix space, and not using the original grey values. In the transformation from the image space into the co-occurrence matrix space,
Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
only the neighbour pixels in one or some of the eight defined directions are normally used. The principle of co-occurrence matrix is as follows. The transformation of the grey value relationships within a window Že.g., 7 = 7 pixels. into the co-occurrence matrix space is shown in Fig. 2. In this example, the interpixel distance is 1. The four matrices on the right are images in the co-occurrence matrix space which have been generated through four different neighbour combinations of the pixels a and b. The number of rows and columns of the matrices corresponds to the possible grey values of the input image. In this example, it is only four. The values in the matrices correspond to the frequency of the combination of the grey values a and b in the window. The computation of a co-occurrence matrix
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is limited to the count of certain grey value combinations in a determined direction. This has a very positive effect on the calculation time. Nevertheless, large memory storage is needed in the case of a normal input image with 256 grey values because each of the co-occurrence matrices has dimensions 256 = 256. However, such fine grey value resolution is not necessary for texture analysis. 16 grey values usually suffice ŽBassmann and Besslich, 1993.. ¨ Texture analysis of images could also be achieved through analysis of Fourier-transformed images. Compared to the Fourier transformation, the important advantages of the co-occurrence matrix method are not only that the co-occurrence matrices can be calculated fast, but also that this method can achieve better results except for some images with regular
Fig. 2. Transformation of the grey value relationships within a window into the co-occurrence matrix space with different combination directions Žinterpixel distances 1. Žmodified after Bassmann and Besslich, 1993.. ¨
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Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
Fig. 3. Co-occurrence matrices calculated in different directions and the result of the Fourier transformation Žafter James, 1987.: Ža. section of a knitted wool as input image, Žb. result of the Fourier transformation, Žc. co-occurrence matrix in horizontal direction, Žd. co-occurrence matrix in the upper leftrlower right direction, Že. co-occurrence matrix in the vertical direction, Žf. co-occurrence matrix in the upper rightrlower left direction.
textures ŽJames, 1987.. A particular characteristic of the co-occurrence matrix method is its direction dependence ŽFig. 3., which is very useful for the application in this study. The co-occurrence matrix method has been also used to introduce spatial information into the spectral classification. Barber and LeDrew Ž1991. used it, for example, for the distinction of sea ice types on Synthetic Aperture Radar ŽSAR. data. In their study, a SAR grey value image was transformed into the co-occurrence matrix space and various output images were calculated with different texture measures. Finally, the ice types were distinguished through the analysis of the output images. Gong et al. Ž1992. used the co-occurrence matrix method to increase the accuracy of land-use classification in an urban–rural
area. The input data were one of the SPOT XS bands. Four co-occurrence matrices in four directions were calculated and averaged. Grey value images were produced by using different texture measures. They were then used as an additional band together with the three SPOT XS bands in the subsequent multispectral classification. 4. The co-occurrence matrix based filtering process in this study The co-occurrence matrix method in this study is different from filtering processes, in which the small areas are filtered out or merged into other areas, and the large areas are made more continuous Že.g., Gurney, 1981; Dutra and Mascarenhas, 1984;
Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
Townsend, 1986; Fung and Chan, 1994; Johnsson, 1994.. It is also different than the methods mentioned in Section 3, since it describes the spatial relationships not of grey value images, but multispectrally classified object classes Ži.e., binary images, see Fig. 1.. In addition, usually only the values in one direction or the average values of some directions are calculated and used to compute the texture measures. As a result, areas with different textures can be detected. In this study, the co-occurrence matrix was used to detect objects with different sizes and directions. The co-occurrence matrix values in four diagonal directions Župper left, upper right, lower left and lower right. were calculated with an interpixel distance of 1 and window sizes of 3 = 3 and 5 = 5 pixels, and then the following four
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texture measures were calculated from each co-occurrence matrix: Ny1 Ny1
Energy: ENE s
Ý Ý f Ž i, j.2, is0 js0 Ny1 Ny1
Contrast: CON s
Ý Ý Ž iyj.2 f Ž i, j. , is0 js0
Ny1 Ny1
Entropy: ENT s
Ý Ý f Ž i , j . log Ž f Ž i , j . . , is0 js0 Ny1 Ny1
Homogeneity: HOM s
Ý Ý is0 js0
f Ž i, j. 1q
with i, j s coordinates of the co-occurrence matrix space; f Ž i, j . s co-occurrence matrix value at the
Fig. 4. Results of the building detection after the co-occurrence matrix based filtering in each of the four diagonal directions using a 3 = 3 pixel window and homogeneity Ž500 = 360 pixel section.: Ža. upper left direction, Žb. upper right direction, Žc. lower right direction, Žd. lower left direction.
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Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
Fig. 5. Comparison of the building detection results by using different filtering methods with a 3 = 3 pixel window Ž500 = 360 pixel section.. Compare Ža., Žc., Že. to Fig. 1b, and Žb., Žd., Žf. to Fig. 1a. Ža and b. Result of the co-occurrence matrix based filtering in four diagonal directions and the texture measure homogeneity. Žc. Result of the normal texture analysis filtering and the texture measure energy. Žd. The noise areas detected through the co-occurrence matrix based filtering in four directions. Že and f. Result of the normal texture analysis filtering and the texture measure homogeneity.
Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
coordinates i, j; N s dimension of the co-occurrence matrix Žs grey value range of the input image; in this case, N s 2.. All above texture measure images for each direction are represented in 8-bit. The grey values of these images also relate to the object size, e.g., for homogeneity, the larger the object size Žwithin the window size of course., the higher the grey value, and thus objects with different sizes can be separated from each other. These grey value images were then subdivided into 50 clusters using the ISODATA clustering method. The 50 clusters were manually interpreted by exactly overlaying them on the merged colour TM–SPOT image and using fade-inrfade-out for each cluster to check if it covers the class to be classified in the original image Že.g., building areas.. In this study, the buildings were consisting of six clusters Žrepresenting buildings of varying size., while other clusters were assigned to either noise areas or background. The noise areas Žsee Fig. 5d. were parts of free areas, squares, streets, construction sites etc. and their structures were different from those of the buildings. The images could also be subdivided into more than 50 clusters, but the interpretation would be more difficult and take more time. According to the experience in this study, 40 to 60 clusters were suitable for achieving a highly accurate result and the time for the interpretation was not too long Žabout one hour on an SGI for one texture measure, one direction and a 3000 = 3000 pixel image.. Since the co-occurrence matrix is direction dependent ŽFig. 3., this special characteristic was utilised to distinguish objects in different directions as clearly as possible. The four diagonal directions were used, because the walls of most buildings in the test area were in diagonal image directions. Using this property, the buildings can be better separated from the noise. The four images in Fig. 4 show results which were achieved through filtering in each of the four diagonal directions by using a 3 = 3 pixel window and the texture measure homogeneity. In Fig. 4, the direction dependence of the co-occurrence matrices can be clearly seen in the filtering effects of the airport on the right and the railway-goods field on the bottom right Žcompare to the input image in Fig. 1b., i.e., in each direction, one side of the object edge Že.g., airport edge. can not be filtered out.
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Thus, edges of large non-building objects are classified as buildings. By merging the building class results in each of the four directions using a logical AND, this problem can be solved Žsee Fig. 5a,b.. Similarly, noise areas in the four directions are merged by a logical OR, while the remaining area is assigned to the background. Thus, objects with different sizes were separated from each other. The best result, among the two different window sizes and the four different texture measures, was obtained by the use of a 3 = 3 pixel window and the texture measure homogeneity.
5. Visual comparison of the results of different texture filtering methods For the purpose of comparison, a normal texture analysis method Žsee Section 3.1. was also used to filter the multispectrally classified buildings using the same texture measures and window sizes. The texture measures were computed using the formulas of Section 4, but using the binary values of the multispectral classification instead of the values of the co-occurrence matrices. The best results were achieved by the use of a 3 = 3 pixel window and the texture measures energy and homogeneity ŽFig. 5c,e.. The visual comparison of the images in Fig. 1b and Fig. 5a, c and e shows clearly that the airport Žright., the railway-goods field Žbottom right., the parts of streets Žbottom left. and the gymnasiums Žtop middle. were clearly filtered out in Fig. 5a. In contrast, they were only partially excluded in Fig. 5c and e. The advantage of the co-occurrence matrix based filtering over the normal texture analysis filtering is clearly shown through this comparison. Fig. 1a and Fig. 5b,f show another section of the extracted buildings before and after texture filtering. Through visual comparison, it can be easily seen how large the part of the noise areas ŽFig. 5d. is in the multispectrally classified result ŽFig. 1a.. The difference between the co-occurrence matrix based filtering and the normal texture analysis filtering is also clearly visible by comparing Fig. 5b and f, respectively. The same texture measure homogeneity was used in both methods.
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Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
6. Accuracy assessment of the results In order to assess the accuracy of the results, the merged near-natural-colour TM–SPOT image was used as reference. The random points method was applied to three 600 = 400 pixel test areas. The first test area was in the southern part of the city, where the newly built residential houses, which are about 12 to 20 m in width, predominate ŽFig. 1b.. The second area was in the northern part of the city, where the majority of buildings are newly built residential houses and only a small part of them are old small buildings ŽFig. 1a.. The third area was in the middle of the city, where old small buildings are predominant with only a few large buildings scattered among them. On the average, buildings covered 10% of the three areas. The buildings were usually 1–2 pixels in width. A total of 400 random points were selected as reference pixels in each of the three test areas and manually classified. The temporal difference between TM and SPOT images did not influence the visual detection of buildings, since it could be made by using the higher spatial resolution from SPOT, independently of the spectral information from the TM images. The mixed pixel percentage for the buildings was very high in the TM–SPOT image. Therefore, it was very difficult to exactly determine whether a random point at a building edge belonged to a building or the background. To get a more accurate assessment, the automatically detected buildings were first overlaid on the reference image to check whether recognisable buildings were not detected Žerrors of omission.. In all three areas there were almost no such errors. On the contrary, there were many background objects classified as buildings. Thus, the accuracy assessment focused on errors of commission. The user accuracy and the Kappa statistic ŽRosenfield and Fitzpatrick-Lins, 1986; Congalton, 1991. of classified buildings before and after the texture filtering are shown in Table 1. User accuracy for a class is defined as the ratio of correctly classified objects over the total number of objects Žpixels. assigned to this class, i.e., it relates to errors of commission. The user accuracy and the Kappa statistic of the extracted buildings after co-occurrence matrix based filtering are significantly higher than before in all test areas. After the co-occurrence matrix based improvement,
Table 1 User accuracy and Kappa statistic of the building detection before and after texture filtering User accuracy
Kappa statistic
Multispectral classification
Area 1 Area 2 Area 3
48.6% 58.9% 68.9%
0.438 0.526 0.663
Post-processing with co-occurrence matrix based filtering
Area 1 Area 2 Area 3
85.7% 81.3% 91.3%
0.848 0.800 0.908
Post-processing with normal texture analysis filtering
Area 1
68.8%
0.669
the average user accuracy of the three areas increased from 59% to 86% and the average Kappa statistic increased from 0.54 to 0.85, i.e., ca. 46% and 57% improvement for the two accuracy measures respectively. Results in area 1 were better than in area 2, since latter had proportionally more nonbuilding objects. The results of area 3 Žcity centre. were the best for the following reason. Large buildings had different roof materials than small ones Ždark roofs. and streets Žasphalt.. Thus, they were very distinct and could be classified more accurately than in other areas where other objects Žconstruction sites, open areas, etc.. had similar spectral characteristics to those of buildings. The accuracy improvement through the normal texture analysis filtering with the texture measure homogeneity was also assessed in the first test area ŽFig. 5e. in order to compare it to the co-occurrence matrix based filtering. The assessment method was the same as above and the accuracy results are shown in Table 1. The user accuracy increased from 49% to 69% with the normal texture analysis filtering and to 86% with the co-occurrence matrix based filtering, i.e., the accuracy improvement of the latter method compared to the first one is ca. 25% Žsimilarly, 27% accuracy improvement was obtained for the Kappa statistic..
7. Conclusions It is obvious that the accuracy of building detection in satellite images in this study is still not high
Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
enough for a detailed investigation of urban building development or large-scale GIS applications. Nevertheless, the result of this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries. In addition to the texture filtering of multispectrally classified buildings, the texture filtering method developed in this study can also be used to improve classification accuracy of other regular object classes. It can be expected that such methods will have an increased applicability when satellite images with up to 1 m spatial resolution will be available. Using the forthcoming very high resolution satellite images, it should be possible that even smaller buildings can be automatically extracted and buildings in different sizes can be separated into different classes, so that the detection accuracy will be considerably increased.
Acknowledgements It is with great appreciation that I thank Prof. Dr. J. Albertz, Department of Photogrammetry and Cartography, Technical University of Berlin, and Prof. Dr. U. Freitag, Department of Cartography, Free University of Berlin, for their support during this research. The TM and SPOT Pan images were supplied by Prof. Anxin Mei, Remote Sensing Institute, East China Normal University, Shanghai.
References Albertz, J., 1991. Grundlagen der Interpretation von Luft- und Satellitenbildern: eine Einfuhrung in die Fernerkundung. Wis¨ senschaftliche Buchgesellschaft, Darmstadt. Barber, D.G., LeDrew, E.F., 1991. SAR sea ice discrimination using texture statistics: a multivariate approach. Photogramm. Eng. Remote Sensing 57 Ž4., 385–395. Barnsley, M.J., Barr, S.L., 1996. Inferring urban land use from satellite sensor images using Kernel-based spatial reclassification. Photogramm. Eng. Remote Sensing 62 Ž8., 949–958. Bassmann, H., Besslich, P.W., 1993. Bildverarbeitung: ad oculus: ¨ 2. Auflage. Springer Verlag, Berlin. Congalton, R., 1991. A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing Environ. 37 Ž1., 35–46.
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Dutra, L.V., Mascarenhas, N.D.A., 1984. Some experiments with spatial feature extraction methods in multispectral classification. Int. J. Remote Sensing 5 Ž2., 303–313. ERDAS, 1997. ERDAS Field Guide, 4th edn. Atlanta, USA. Fritz, L.W., 1996. The era of commercial earth observation satellites. Photogramm. Eng. Remote Sensing 62 Ž1., 39–45. Fung, T., Chan, K.C., 1994. Spatial composition of spectral classes: a structural approach for image analysis of heterogeneous land-use and land-cover types. Photogramm. Eng. Remote Sensing 60 Ž2., 173–180. Gong, P., Howarth, P.J., 1990. The use of structural information for improving land-cover classification accuracies at the rural–urban fringe. Photogramm. Eng. Remote Sensing 56 Ž1., 67–73. Gong, P., Marceau, D.J., Howarth, P.J., 1992. A comparison of spatial feature extraction algorithms for land-use classification with SPOT HRV data. Remote Sensing Environ. 40, 137–151. Gurney, C.M., 1981. The use of contextual information to improve land cover classification of digital remotely sensed data. Int. J. Remote Sensing 2 Ž4., 379–388. Haberacker, P., 1985. Digitale Bildverarbeitung, Grundlagen und ¨ Anwendungen. Hanser Verlag, Munchen. ¨ Haralick, R.M., 1986. Statistical image texture analysis. In: Young, T.Y., Fu, K.-S. ŽEds.., Handbook of Pattern Recognition and Image Processing. Academic Press, Orlando. Harris, P.M., Ventura, S.J., 1995. The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Photogramm. Eng. Remote Sensing 61 Ž8., 993–998. Hsu, S.-Y., 1978. Texture–tone analysis for automated land-use mapping. Photogramm. Eng. Remote Sensing 44 Ž11., 1393– 1404. Jahne, B., 1989. Digitale Bildverarbeitung. Springer Verlag, ¨ Berlin. James, M., 1987. Pattern Recognition. BSP Professional Books, Oxford. Johnsson, K., 1994. Segment-based land-use classification from SPOT satellite data. Photogramm. Eng. Remote Sensing 60 Ž1., 47–53. Meinel, G., Lippold, R., Netzband, M., 1998. The potential use of new high resolution satellite data for urban and regional planning. Int. Arch. Photogramm. Remote Sensing 32 Ž7., 250–257. Rosenfield, G.H., Fitzpatrick-Lins, K., 1986. A coefficient of agreement as a measure of thematic classification accuracy. Photogramm. Eng. Remote Sensing 52 Ž2., 223–227. Schmitt, U., Sulzer, W., Schardt, M., 1998. Analysis of settlement structure by means of high resolution satellite imagery. Int. Arch. Photogramm. Remote Sensing 32 Ž4., 557–561. Shanghai’s Remote Sensing Office, 1993. Comprehensive urban investigation and study using aerial remote sensing in Shanghai Ž1988–1991.. Shanghai’s News and Press Bureau Žin Chinese.. Stoney, W., 1996. Data summary. In: Executive Summary: Land Satellite Information in the Next Decade The World Under a Microscope. Published by the American Society for Photogrammetry and Remote Sensing Ž5410 Grosvenor Lane,
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Suite 210, Bethesda, MD 20814-2160, USA, [email protected]., pp. 19–59. Swain, P.H., 1973. Pattern recognition: a basis for remote sensing data analysis. The Laboratory for Application of Remote Sensing, Purdue University, West Lafayette, Indiana. Townsend, F.E., 1986. The enhancement of computer classifications by logical smoothing. Photogramm. Eng. Remote Sensing 52 Ž2., 213–221.
Zhang, Y., 1998. Aufbau eines auf Satellitenfernerkundung basierten Informationssystems zur stadtischen Umwel¨ tuberwachung: Das Beispiel Shanghai. PhD Dissertation, ¨ Berliner Geowissenschaftliche Abhandlungen, Reihe C, Band 17, Free University of Berlin.
Optimisation of building detection in satellite images by combining multispectral classification and texture filtering Yun Zhang
)
Institute of Planetary Exploration, German Aerospace Centre (DLR), Rudower Chaussee 5, 12489 Berlin, Germany
Abstract Conventional multispectral classification methods show poor performance with respect to detection of urban object classes, such as buildings, in high spatial resolution satellite images. This is because objects in urban areas are very complicated with respect to both their spectral and spatial characteristics. Multispectral classification detects object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. In this study, a technique is described which attempts to detect urban buildings in two stages. The first stage is a conventional multispectral classification. In the second stage, the classification of buildings is improved by means of their spatial information through a modified co-occurrence matrix based filtering. The direction dependence of the co-occurrence matrix is utilised in the filtering process. The method has been tested by using TM and SPOT Pan merged data for the whole area of the city of Shanghai, China. After the co-occurrence matrix based filtering, the average user accuracy increased by about 46% and the average Kappa statistic by about 57%. This result is about 26% better than the accuracy improvement through normal texture filtering. The method presented in this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries. q 1999 Elsevier Science B.V. All rights reserved. Keywords: building detection; satellite images; multispectral classification; co-occurrence matrix based filtering
1. Introduction Buildings are one of the most important classes in urban land-cover and land-use classification. The current building distribution and development in a city are essential information for urban environmental investigation and urban planning. However, the collection of such information is not easy. Conventional interpretation of aerial photos is very timeconsuming and expensive. Satellite images are an important information source for earth observation )
Tel.: q49-30-67055-391; Fax: q49-30-67055-384; E-mail: [email protected]
and provide the current information periodically at low cost. They show a very promising perspective for urban land-cover and land-use classification, particularly with very high resolution Ž1–4 m. satellite images, which will be available in the near future ŽFritz, 1996; Stoney, 1996; Meinel et al., 1998; Schmitt et al., 1998.. Thus, it is meaningful and also necessary to develop methods for fast acquisition of up-to-date urban information, such as building distribution and development, from satellite images. The conventional multispectral classification methods have been successfully used for the detection of areal objects from satellite images. However, they are still problematic for the detection of object
0924-2716r99r$ - see front matter q 1999 Elsevier Science B.V. All rights reserved. PII: S 0 9 2 4 - 2 7 1 6 Ž 9 8 . 0 0 0 2 7 - 6
Y. Zhangr ISPRS Journal of Photogrammetry & Remote Sensing 54 (1999) 50–60
classes in urban areas. The reasons are: Ž1. The objects in urban areas are very complicated. They are characterised more through their structure than through their spectral reflection properties. Ž2. The conventional multispectral classification methods extract the object classes only according to the spectral information of the individual pixels, while a large amount of spatial information is neglected. The shortcomings of the conventional multispectral classification in urban areas was also attested by many studies ŽHsu, 1978; Gong and Howarth, 1990; Gong et al., 1992; Fung and Chan, 1994; Johnsson, 1994; Harris and Ventura, 1995; Barnsley and Barr, 1996.. In order to extract urban object classes accurately, it is necessary to include the spatial Žor structural. information in the classification as well as the spectral information. However, the methods of modelling, enhancing, and extracting spatial information from digital images are still quite limited ŽAlbertz, 1991.. A project was therefore carried out in order to develop new methods for the acquisition of important urban object classes such as buildings, water system and street greenery from satellite images. In this paper, a special texture filtering method is presented, which is suitable for increasing the accuracy of the multispectrally classified regular features such as urban buildings. 2. Test area and data sources The test area was the whole city of Shanghai, China. It covers an area of approx. 30 km = 30 km. The buildings in the city, which are approx. 12 to 20 m in width, are mostly residential houses built since 1949, particularly since 1979. This kind of buildings is distributed relatively regularly and forms regular urban features in satellite images. They cover 50% of the entire residential area and develop most dynamically. Therefore, their detection was the main aim of this study. The other three kinds of buildings in the city are high buildings with more than 10 floors, old small buildings which are small and set closely to each other, and primitive houses which are very small and irregular ŽShanghai’s Remote Sensing Office, 1993.. Because of space limitations, the detection of high buildings is not described here. The relevant details can be found in the paper of Zhang
51
Ž1998.. The old small buildings and the primitive houses are too small and too close to each other. They can not be recognised in merged TM–SPOT data, even by visual interpretation, so they were not extracted in this study. For the detection of the buildings which are 12 to 20 m in width, merged TM–SPOT data Ž10 m pixel size. were used. The merging method was the IHS transformation. The data used for the merging were the TM bands 3, 4 and 7 taken by Landsat 5 on 18 May 1987 and the SPOT Pan data taken by the sensor HRV2 of SPOT 1 on 25 October 1989. Because of the limited choice, the used source data have a time difference of two years. This caused an accuracy decrease of the results to some extent. However, it did not influence the demonstration of the performance of the new method presented in this study, because the time difference decreased the accuracy of the multispectral classification, not of the texture filtering—the main part of this study. The unsupervised ISODATA clustering method ŽSwain, 1973; ERDAS, 1997. was used for the multispectral classification of the buildings. The result of the multispectral classification is shown in Fig. 1. 3. Common methods of texture analysis 3.1. Normal texture analysis Methods of texture analysis are frequently used for the recognition and distinction of different spatial characteristics in digital images. In performing texture analysis, the grey value relationships between the current pixel and the pixels next to it are calculated on the basis of a certain texture measure Že.g., mean, standard deviation, contrast, correlation, energy, entropy, etc... The grey values of the output image represent the local texture criterion of the input image. In industrial pattern recognition, the average grey values of the output images are usually measured and used as criteria to distinguish input images with different textures ŽHaberacker, 1985; James, 1987; ¨ Jahne, 1989; Bassmann and Besslich, 1993.. ¨ ¨ In the classification of satellite images, texture analysis methods are often used to introduce the spatial information of different object classes into the classification. Normally, the output image generated
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Fig. 1. Buildings extracted with multispectral classification from the merged TM–SPOT data of the city of Shanghai Ž500 = 360 pixel sections.: Ža. section in the northern part of the city, Žb. section in the southern part of the city.
by the texture analysis is classified directly or used as an additional band together with other multispectral bands in classification ŽHsu, 1978; Gong and Howarth, 1990; Gong et al., 1992.. 3.2. Grey Õalue co-occurrence matrix A more effective texture analysis method is the grey value co-occurrence matrix method ŽHaralick,
1986; Bassmann and Besslich, 1993.. The goal of ¨ the co-occurrence matrix method is also to describe the grey value relationships in the neighbourhood of the current pixel, as in the normal texture analysis. However, the grey value relationships are analysed in the co-occurrence matrix space, and not using the original grey values. In the transformation from the image space into the co-occurrence matrix space,
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only the neighbour pixels in one or some of the eight defined directions are normally used. The principle of co-occurrence matrix is as follows. The transformation of the grey value relationships within a window Že.g., 7 = 7 pixels. into the co-occurrence matrix space is shown in Fig. 2. In this example, the interpixel distance is 1. The four matrices on the right are images in the co-occurrence matrix space which have been generated through four different neighbour combinations of the pixels a and b. The number of rows and columns of the matrices corresponds to the possible grey values of the input image. In this example, it is only four. The values in the matrices correspond to the frequency of the combination of the grey values a and b in the window. The computation of a co-occurrence matrix
53
is limited to the count of certain grey value combinations in a determined direction. This has a very positive effect on the calculation time. Nevertheless, large memory storage is needed in the case of a normal input image with 256 grey values because each of the co-occurrence matrices has dimensions 256 = 256. However, such fine grey value resolution is not necessary for texture analysis. 16 grey values usually suffice ŽBassmann and Besslich, 1993.. ¨ Texture analysis of images could also be achieved through analysis of Fourier-transformed images. Compared to the Fourier transformation, the important advantages of the co-occurrence matrix method are not only that the co-occurrence matrices can be calculated fast, but also that this method can achieve better results except for some images with regular
Fig. 2. Transformation of the grey value relationships within a window into the co-occurrence matrix space with different combination directions Žinterpixel distances 1. Žmodified after Bassmann and Besslich, 1993.. ¨
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Fig. 3. Co-occurrence matrices calculated in different directions and the result of the Fourier transformation Žafter James, 1987.: Ža. section of a knitted wool as input image, Žb. result of the Fourier transformation, Žc. co-occurrence matrix in horizontal direction, Žd. co-occurrence matrix in the upper leftrlower right direction, Že. co-occurrence matrix in the vertical direction, Žf. co-occurrence matrix in the upper rightrlower left direction.
textures ŽJames, 1987.. A particular characteristic of the co-occurrence matrix method is its direction dependence ŽFig. 3., which is very useful for the application in this study. The co-occurrence matrix method has been also used to introduce spatial information into the spectral classification. Barber and LeDrew Ž1991. used it, for example, for the distinction of sea ice types on Synthetic Aperture Radar ŽSAR. data. In their study, a SAR grey value image was transformed into the co-occurrence matrix space and various output images were calculated with different texture measures. Finally, the ice types were distinguished through the analysis of the output images. Gong et al. Ž1992. used the co-occurrence matrix method to increase the accuracy of land-use classification in an urban–rural
area. The input data were one of the SPOT XS bands. Four co-occurrence matrices in four directions were calculated and averaged. Grey value images were produced by using different texture measures. They were then used as an additional band together with the three SPOT XS bands in the subsequent multispectral classification. 4. The co-occurrence matrix based filtering process in this study The co-occurrence matrix method in this study is different from filtering processes, in which the small areas are filtered out or merged into other areas, and the large areas are made more continuous Že.g., Gurney, 1981; Dutra and Mascarenhas, 1984;
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Townsend, 1986; Fung and Chan, 1994; Johnsson, 1994.. It is also different than the methods mentioned in Section 3, since it describes the spatial relationships not of grey value images, but multispectrally classified object classes Ži.e., binary images, see Fig. 1.. In addition, usually only the values in one direction or the average values of some directions are calculated and used to compute the texture measures. As a result, areas with different textures can be detected. In this study, the co-occurrence matrix was used to detect objects with different sizes and directions. The co-occurrence matrix values in four diagonal directions Župper left, upper right, lower left and lower right. were calculated with an interpixel distance of 1 and window sizes of 3 = 3 and 5 = 5 pixels, and then the following four
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texture measures were calculated from each co-occurrence matrix: Ny1 Ny1
Energy: ENE s
Ý Ý f Ž i, j.2, is0 js0 Ny1 Ny1
Contrast: CON s
Ý Ý Ž iyj.2 f Ž i, j. , is0 js0
Ny1 Ny1
Entropy: ENT s
Ý Ý f Ž i , j . log Ž f Ž i , j . . , is0 js0 Ny1 Ny1
Homogeneity: HOM s
Ý Ý is0 js0
f Ž i, j. 1q
with i, j s coordinates of the co-occurrence matrix space; f Ž i, j . s co-occurrence matrix value at the
Fig. 4. Results of the building detection after the co-occurrence matrix based filtering in each of the four diagonal directions using a 3 = 3 pixel window and homogeneity Ž500 = 360 pixel section.: Ža. upper left direction, Žb. upper right direction, Žc. lower right direction, Žd. lower left direction.
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Fig. 5. Comparison of the building detection results by using different filtering methods with a 3 = 3 pixel window Ž500 = 360 pixel section.. Compare Ža., Žc., Že. to Fig. 1b, and Žb., Žd., Žf. to Fig. 1a. Ža and b. Result of the co-occurrence matrix based filtering in four diagonal directions and the texture measure homogeneity. Žc. Result of the normal texture analysis filtering and the texture measure energy. Žd. The noise areas detected through the co-occurrence matrix based filtering in four directions. Že and f. Result of the normal texture analysis filtering and the texture measure homogeneity.
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coordinates i, j; N s dimension of the co-occurrence matrix Žs grey value range of the input image; in this case, N s 2.. All above texture measure images for each direction are represented in 8-bit. The grey values of these images also relate to the object size, e.g., for homogeneity, the larger the object size Žwithin the window size of course., the higher the grey value, and thus objects with different sizes can be separated from each other. These grey value images were then subdivided into 50 clusters using the ISODATA clustering method. The 50 clusters were manually interpreted by exactly overlaying them on the merged colour TM–SPOT image and using fade-inrfade-out for each cluster to check if it covers the class to be classified in the original image Že.g., building areas.. In this study, the buildings were consisting of six clusters Žrepresenting buildings of varying size., while other clusters were assigned to either noise areas or background. The noise areas Žsee Fig. 5d. were parts of free areas, squares, streets, construction sites etc. and their structures were different from those of the buildings. The images could also be subdivided into more than 50 clusters, but the interpretation would be more difficult and take more time. According to the experience in this study, 40 to 60 clusters were suitable for achieving a highly accurate result and the time for the interpretation was not too long Žabout one hour on an SGI for one texture measure, one direction and a 3000 = 3000 pixel image.. Since the co-occurrence matrix is direction dependent ŽFig. 3., this special characteristic was utilised to distinguish objects in different directions as clearly as possible. The four diagonal directions were used, because the walls of most buildings in the test area were in diagonal image directions. Using this property, the buildings can be better separated from the noise. The four images in Fig. 4 show results which were achieved through filtering in each of the four diagonal directions by using a 3 = 3 pixel window and the texture measure homogeneity. In Fig. 4, the direction dependence of the co-occurrence matrices can be clearly seen in the filtering effects of the airport on the right and the railway-goods field on the bottom right Žcompare to the input image in Fig. 1b., i.e., in each direction, one side of the object edge Že.g., airport edge. can not be filtered out.
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Thus, edges of large non-building objects are classified as buildings. By merging the building class results in each of the four directions using a logical AND, this problem can be solved Žsee Fig. 5a,b.. Similarly, noise areas in the four directions are merged by a logical OR, while the remaining area is assigned to the background. Thus, objects with different sizes were separated from each other. The best result, among the two different window sizes and the four different texture measures, was obtained by the use of a 3 = 3 pixel window and the texture measure homogeneity.
5. Visual comparison of the results of different texture filtering methods For the purpose of comparison, a normal texture analysis method Žsee Section 3.1. was also used to filter the multispectrally classified buildings using the same texture measures and window sizes. The texture measures were computed using the formulas of Section 4, but using the binary values of the multispectral classification instead of the values of the co-occurrence matrices. The best results were achieved by the use of a 3 = 3 pixel window and the texture measures energy and homogeneity ŽFig. 5c,e.. The visual comparison of the images in Fig. 1b and Fig. 5a, c and e shows clearly that the airport Žright., the railway-goods field Žbottom right., the parts of streets Žbottom left. and the gymnasiums Žtop middle. were clearly filtered out in Fig. 5a. In contrast, they were only partially excluded in Fig. 5c and e. The advantage of the co-occurrence matrix based filtering over the normal texture analysis filtering is clearly shown through this comparison. Fig. 1a and Fig. 5b,f show another section of the extracted buildings before and after texture filtering. Through visual comparison, it can be easily seen how large the part of the noise areas ŽFig. 5d. is in the multispectrally classified result ŽFig. 1a.. The difference between the co-occurrence matrix based filtering and the normal texture analysis filtering is also clearly visible by comparing Fig. 5b and f, respectively. The same texture measure homogeneity was used in both methods.
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6. Accuracy assessment of the results In order to assess the accuracy of the results, the merged near-natural-colour TM–SPOT image was used as reference. The random points method was applied to three 600 = 400 pixel test areas. The first test area was in the southern part of the city, where the newly built residential houses, which are about 12 to 20 m in width, predominate ŽFig. 1b.. The second area was in the northern part of the city, where the majority of buildings are newly built residential houses and only a small part of them are old small buildings ŽFig. 1a.. The third area was in the middle of the city, where old small buildings are predominant with only a few large buildings scattered among them. On the average, buildings covered 10% of the three areas. The buildings were usually 1–2 pixels in width. A total of 400 random points were selected as reference pixels in each of the three test areas and manually classified. The temporal difference between TM and SPOT images did not influence the visual detection of buildings, since it could be made by using the higher spatial resolution from SPOT, independently of the spectral information from the TM images. The mixed pixel percentage for the buildings was very high in the TM–SPOT image. Therefore, it was very difficult to exactly determine whether a random point at a building edge belonged to a building or the background. To get a more accurate assessment, the automatically detected buildings were first overlaid on the reference image to check whether recognisable buildings were not detected Žerrors of omission.. In all three areas there were almost no such errors. On the contrary, there were many background objects classified as buildings. Thus, the accuracy assessment focused on errors of commission. The user accuracy and the Kappa statistic ŽRosenfield and Fitzpatrick-Lins, 1986; Congalton, 1991. of classified buildings before and after the texture filtering are shown in Table 1. User accuracy for a class is defined as the ratio of correctly classified objects over the total number of objects Žpixels. assigned to this class, i.e., it relates to errors of commission. The user accuracy and the Kappa statistic of the extracted buildings after co-occurrence matrix based filtering are significantly higher than before in all test areas. After the co-occurrence matrix based improvement,
Table 1 User accuracy and Kappa statistic of the building detection before and after texture filtering User accuracy
Kappa statistic
Multispectral classification
Area 1 Area 2 Area 3
48.6% 58.9% 68.9%
0.438 0.526 0.663
Post-processing with co-occurrence matrix based filtering
Area 1 Area 2 Area 3
85.7% 81.3% 91.3%
0.848 0.800 0.908
Post-processing with normal texture analysis filtering
Area 1
68.8%
0.669
the average user accuracy of the three areas increased from 59% to 86% and the average Kappa statistic increased from 0.54 to 0.85, i.e., ca. 46% and 57% improvement for the two accuracy measures respectively. Results in area 1 were better than in area 2, since latter had proportionally more nonbuilding objects. The results of area 3 Žcity centre. were the best for the following reason. Large buildings had different roof materials than small ones Ždark roofs. and streets Žasphalt.. Thus, they were very distinct and could be classified more accurately than in other areas where other objects Žconstruction sites, open areas, etc.. had similar spectral characteristics to those of buildings. The accuracy improvement through the normal texture analysis filtering with the texture measure homogeneity was also assessed in the first test area ŽFig. 5e. in order to compare it to the co-occurrence matrix based filtering. The assessment method was the same as above and the accuracy results are shown in Table 1. The user accuracy increased from 49% to 69% with the normal texture analysis filtering and to 86% with the co-occurrence matrix based filtering, i.e., the accuracy improvement of the latter method compared to the first one is ca. 25% Žsimilarly, 27% accuracy improvement was obtained for the Kappa statistic..
7. Conclusions It is obvious that the accuracy of building detection in satellite images in this study is still not high
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enough for a detailed investigation of urban building development or large-scale GIS applications. Nevertheless, the result of this study is very useful for a rapid estimation of urban building and city development, especially in metropolitan areas of developing countries. In addition to the texture filtering of multispectrally classified buildings, the texture filtering method developed in this study can also be used to improve classification accuracy of other regular object classes. It can be expected that such methods will have an increased applicability when satellite images with up to 1 m spatial resolution will be available. Using the forthcoming very high resolution satellite images, it should be possible that even smaller buildings can be automatically extracted and buildings in different sizes can be separated into different classes, so that the detection accuracy will be considerably increased.
Acknowledgements It is with great appreciation that I thank Prof. Dr. J. Albertz, Department of Photogrammetry and Cartography, Technical University of Berlin, and Prof. Dr. U. Freitag, Department of Cartography, Free University of Berlin, for their support during this research. The TM and SPOT Pan images were supplied by Prof. Anxin Mei, Remote Sensing Institute, East China Normal University, Shanghai.
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