BibTex RIS Kaynak Göster

Analysis of surface textures of physiographic features extracted from multiscale digital elevation models via grey level co-occurrence matrix

Yıl 2013, Sayı: 107, 59 - 69, 01.05.2013
https://doi.org/10.9733/jgg.120913.2t

Öz

This paper is aimed at employing grey level co-occurrence matrix GLCM to analyse the surface textures of physiographic features extracted from multiscale digital elevation models DEMs . Four GLCM parameters, energy, contrast, autocorrelation and entropy, are computed for horizontal 0° , vertical 90° and diagonal 45 and 135° cell pair orientations. For the respective DEMs and physiographic features, varying patterns are observed in the plots of the GLCM parameters due to varying surface profiles and the changes that occur over the scales. Due to the smoothing of the terrain during multiscaling, the features have increasing values of energy and entropy, and decreasing values of contrast and entropy, indicating decreasing roughness. Mountains have the highest roughness as compared to the other features over the scales, while basins have the lowest roughness. For each parameter, similar trends are observed in the plots for the four different cell pair orientations, indicating similar trends of change of surface texture in the different orientations over the scales. However, varying values are observed for the different orientations, depending on textural uniformity in the corresponding orientations. The results obtained demonstrate that GLCM can be an appropriate tool for classifying landforms from multiscale DEMs based on the different texture characteristics of the landforms.

Kaynakça

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  • Ahmad Fadzil M.H., Dinesh S., Vijanth Sagayan A., (2011), A method for computation of surface roughness of digital elevation model terrains via multiscale analysis, Computers & Geosciences, 37(2), 177-192, doi:10.1016/j.cageo.2010.05.021.
  • Al-Janobi A., (2001), Performance evaluation of cross-diagonal texture matrix method of texture analysis, Pattern Recognition, 34(1), 187-197, doi:10.1016/S0031-3203(99)00206-X.
  • Aria E.H., Saradjian M.R., Amini J., Lucas C., (2004), Generalized cooccurrence matrix to classify IRS-1D images using neural network, XXth ISPRS Congress, 12-23 Temmuz 2004 Istanbul, Türkiye.
  • Baraldi A., Parmiggiani F., (1995), An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters, IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293-302, doi:10.1109/36.377929.
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  • Bernardin T., Cowgill E. S., Gold R. D., Hamann, B., Kreylos O., Schmitt A., (2008), Real-time terrain mapping, Scientific Visualization: Challenges for the Future' un İçinde, (Hagen H., Ed.), IEEE Computer Society Press, Los Alamitos, California, ss. 275-288.
  • Carr J.R., Miranda F.P., (1998), The semivariogram in comparison to the co-occurance matrix for classification of image texture, IEEE Transactions on Geosciences and Remote Sensing, 36(6):1945-1952, doi:10.1109/36.729366.
  • Chang T., Kuo C.C.J., (1993), Texture analysis and classification with tree-structured wavelet transform, IEEE Transactions in Image Processing, 2(4), 429-441, doi:10.1109/83.242353.
  • Chiao L.Y., Hsieh C., Chiu T.S., (2013), Exploring spatiotemporal ecological variations by the multiscale interpolation, Ecological Modelling, 246(10), 26-33.
  • Cross G.R., Jain A.K., (1983), Markov random field texture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(1), 25-39, doi:10.1109/TPAMI.1983.4767341.
  • Claypoole R.L., Baraniuk R.G., (2000), A multiresolution wedgelet transform for image processing, In Wavelet Applications in Signal and Image Processing VIII, Volume 4119 of SPIE Proceedings (Unser M.A., Aldroubi A., Laine A.F., Ed.), SPIE, Bellingham, Washington, ss. 253-262.
  • Dinesh S., Radhakrishnan P., Sagar B.S.D., (2007), Morphological segmentation of physiographic features from DEM, International Journal of Remote Sensing, 28(15), 3379-3394, doi:10.1080/01431160500486708.
  • Dinesh S., Ahmad Fadzil M.H., Vijanth Sagayan A., (2011), Computation of uncertainty of physiographic features extracted from multiscale digital elevation models using fuzzy classification, Image Processing, Image Analysis and Real- Time Imaging (IPIARTI) Symposium 2011, 6 Ekim 2011, Multimedia University (MMU), Cyberjaya.
  • Drăguţ L., Eisank C., (2011), Object representations at multiple scales from digital elevation models, Geomorphology, 129 (3- 4), 183-189, doi:10.1016/j.geomorph.2011.03.003.
  • Drăguţ L., Schauppenlehner T., Muhar A., Strobl J., Blaschke T., (2009), Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling, Computers & Geosciences, 35(9), 1875-1883, doi:10.1016/j. cageo.2008.10.008.
  • Eichkitz C.G., Amtmann J. and Schreilechner M.G (2013), Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions, Computer & Geosciences, 30, 176-183, doi:10.1016/j.cageo.2013.07.006.
  • Fisher P., Wood J., Cheng T., (2004), Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District, Transactions of the Institute of British Geographers, 29, 106- 128, doi:10.1111/j.0020-2754.2004.00117.x.
  • Gadelmawla E.S., (2004), A vision system for surface roughness occurrence matrix, NDT&E International, 37(7), 577-588, doi:10.1016/j.ndteint.2004.03.004. using gray-level co
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  • Goodchild M.F., (2011), Scale in GIS: An overview, Geomorphology, 130(1-2), 5-9, doi:10.1016/j.geomorph.2010.10.004.
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  • Haralick R.M., Shanmugam K., Dinstein I., (1973), Texture features for image classification, IEEE Transactions on Systems, Man and Cybernetics, 3(6), 610-621, doi: 10.1109/TSMC.1973.4309314.
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  • Laws K.I., (1980), Textured Image Segmentation, Doktora Tezi, University of Southern California, Los Angeles, California.
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  • Ohanian P.P., Dubes R.C., (1992), Performance evaluation for four classes of textural features, Pattern Recognition, 25(8), 819- 833, doi: 10.1016/0031-3203(92)90036-I.
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Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi

Yıl 2013, Sayı: 107, 59 - 69, 01.05.2013
https://doi.org/10.9733/jgg.120913.2t

Öz

Bu çalışmanın amacı çoklu ölçekli Sayısal Yükseklik Modellerinden SYMler çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin analizinde Gri Düzey Eş-Oluşum Matrisi GDEM ’nin kullanılmasıdır. Enerji, kontrast, otokorelasyon ve entropi olmak üzere dört GDEM parametresi yatay 0° , düşey, ve köşegen 45 and 135° yönler boyunca hücre çiftleri için hesaplanmıştır. Çeşitli yüzey profilleri ve farklı ölçeklerde oluşan değişimler nedeniyle fizyografik özellikler ve bunlara karşılık gelen SYMler için GDEM parametrelerinin çiziminde çeşitli örüntüler gözlemlenmektedir. Çoklu ölçeklendirme esnasında yeryüzündeki detayların yumuşatılması nedeniyle azalan engebeliliği gösterecek şekilde yüzey özellikleri artan enerji ve entropi değerlerine sahip olurken, azalan kontrast ve entropi değerleri oluşmaktadır. Farklı ölçeklerde farklı yüzey özellikleri ile karşılaştırıldıklarında dağlar en yüksek, havzalar ise en düşük engebelilik değerlerine sahip olmaktadır. Her bir parametre için dört farklı hücre çifti yönüne ait çizimlerde benzer eğilimler gözlenmektedir. Yani, farklı ölçeklerde, farklı yönlerde yüzey doku özelliklerindeki değişimde benzer eğilimler oluşmaktadır. Fakat, her bir yöndeki dokusal tekdüzeliğe bağlı olarak farklı yönler için değişen değerler gözlenmiştir. Elde edilen sonuçlar göstermektedir ki, GDEM, yeryüzü şekillerine ait farklı doku özelliklerine dayanan çoklu ölçekli SYMleri kullanarak sınıflandırma yapmak için uygun bir araçtır.

Kaynakça

  • Abdul-Rahman H.S., Jiang X.J., Scott P.J., (2013), Freeform surface filtering using the lifting wavelet transform, Precision Engineering, 37(1), 187-202, doi: 10.1016/j. precisioneng.2012.08.002.
  • Ahmad Fadzil M.H., Dinesh S., Vijanth Sagayan A., (2011), A method for computation of surface roughness of digital elevation model terrains via multiscale analysis, Computers & Geosciences, 37(2), 177-192, doi:10.1016/j.cageo.2010.05.021.
  • Al-Janobi A., (2001), Performance evaluation of cross-diagonal texture matrix method of texture analysis, Pattern Recognition, 34(1), 187-197, doi:10.1016/S0031-3203(99)00206-X.
  • Aria E.H., Saradjian M.R., Amini J., Lucas C., (2004), Generalized cooccurrence matrix to classify IRS-1D images using neural network, XXth ISPRS Congress, 12-23 Temmuz 2004 Istanbul, Türkiye.
  • Baraldi A., Parmiggiani F., (1995), An investigation of the textural characteristics associated with gray level co-occurrence matrix statistical parameters, IEEE Transactions on Geoscience and Remote Sensing, 33(2), 293-302, doi:10.1109/36.377929.
  • Behrens T., Zhu A.X., Schmidt K., Scholten T., (2010), Multi- scale digital terrain analysis and feature selection for digital soil mapping, Geoderma, 155(3-4), 175-185, doi:10.1016/j.geoderma.2009.07.010.
  • Bernardin T., Cowgill E. S., Gold R. D., Hamann, B., Kreylos O., Schmitt A., (2008), Real-time terrain mapping, Scientific Visualization: Challenges for the Future' un İçinde, (Hagen H., Ed.), IEEE Computer Society Press, Los Alamitos, California, ss. 275-288.
  • Carr J.R., Miranda F.P., (1998), The semivariogram in comparison to the co-occurance matrix for classification of image texture, IEEE Transactions on Geosciences and Remote Sensing, 36(6):1945-1952, doi:10.1109/36.729366.
  • Chang T., Kuo C.C.J., (1993), Texture analysis and classification with tree-structured wavelet transform, IEEE Transactions in Image Processing, 2(4), 429-441, doi:10.1109/83.242353.
  • Chiao L.Y., Hsieh C., Chiu T.S., (2013), Exploring spatiotemporal ecological variations by the multiscale interpolation, Ecological Modelling, 246(10), 26-33.
  • Cross G.R., Jain A.K., (1983), Markov random field texture models, IEEE Transactions on Pattern Analysis and Machine Intelligence, 5(1), 25-39, doi:10.1109/TPAMI.1983.4767341.
  • Claypoole R.L., Baraniuk R.G., (2000), A multiresolution wedgelet transform for image processing, In Wavelet Applications in Signal and Image Processing VIII, Volume 4119 of SPIE Proceedings (Unser M.A., Aldroubi A., Laine A.F., Ed.), SPIE, Bellingham, Washington, ss. 253-262.
  • Dinesh S., Radhakrishnan P., Sagar B.S.D., (2007), Morphological segmentation of physiographic features from DEM, International Journal of Remote Sensing, 28(15), 3379-3394, doi:10.1080/01431160500486708.
  • Dinesh S., Ahmad Fadzil M.H., Vijanth Sagayan A., (2011), Computation of uncertainty of physiographic features extracted from multiscale digital elevation models using fuzzy classification, Image Processing, Image Analysis and Real- Time Imaging (IPIARTI) Symposium 2011, 6 Ekim 2011, Multimedia University (MMU), Cyberjaya.
  • Drăguţ L., Eisank C., (2011), Object representations at multiple scales from digital elevation models, Geomorphology, 129 (3- 4), 183-189, doi:10.1016/j.geomorph.2011.03.003.
  • Drăguţ L., Schauppenlehner T., Muhar A., Strobl J., Blaschke T., (2009), Optimization of scale and parametrization for terrain segmentation: An application to soil-landscape modeling, Computers & Geosciences, 35(9), 1875-1883, doi:10.1016/j. cageo.2008.10.008.
  • Eichkitz C.G., Amtmann J. and Schreilechner M.G (2013), Calculation of grey level co-occurrence matrix-based seismic attributes in three dimensions, Computer & Geosciences, 30, 176-183, doi:10.1016/j.cageo.2013.07.006.
  • Fisher P., Wood J., Cheng T., (2004), Where is Helvellyn? Multiscale morphometry and the mountains of the English Lake District, Transactions of the Institute of British Geographers, 29, 106- 128, doi:10.1111/j.0020-2754.2004.00117.x.
  • Gadelmawla E.S., (2004), A vision system for surface roughness occurrence matrix, NDT&E International, 37(7), 577-588, doi:10.1016/j.ndteint.2004.03.004. using gray-level co
  • Gonzalez R.C., Woods R.E., Eddins S.I., (2009), Digital Image Processing Using MATLAB, Prentice Hall, New Jersey.
  • Goodchild M.F., (2011), Scale in GIS: An overview, Geomorphology, 130(1-2), 5-9, doi:10.1016/j.geomorph.2010.10.004.
  • GTOPO30, (1996), GTOPO30: Global 30 Arc Second Elevation Data Set, Available online at: http://edcwww.cr.usgs.gov/ landdaac/gtopo30/gtopo30.html, [ Erişim 1 June 2009].
  • Guo S.M., Chang W.H., Tsai J.S.H., Zhuang B.L., Chen L.C., (2008), JPEG 2000 wavelet next term filter design framework with chaos evolutionary programming, Signal Processing, 88(10): 2542-2553, doi:10.1016/j.sigpro.2008.05.009.
  • Haralick R.M., Shanmugam K., Dinstein I., (1973), Texture features for image classification, IEEE Transactions on Systems, Man and Cybernetics, 3(6), 610-621, doi: 10.1109/TSMC.1973.4309314.
  • Hayat K, Puech W., Gesquère G., (2008), Scalable 3-D terrain visualization through reversible JPEG2000-based blind data hiding, IEEE Transactions on Multimedia, 10(7), 1261-1276, doi:10.1109/TMM.2008.2004905.
  • Howard A.D., (1994), A detachment-limited model of drainage basin evolution, Water Resources Research, 30(7), 2261-2285, doi:10.1029/94WR00757.
  • Howell D., (1995), Principles of Terrain Analysis: New Applications for Global Tectonics, Chapman and Hall, London.
  • Jain A.K., Farrokhnia F., (1991), Unsupervised texture segmentation using Gabor filters, Pattern Recognition, 24(12), 1167-1186, doi: 10.1016/0031-3203(91)90143-S.
  • Jiang X.Q., Blunt L., Stout K.J., (2001a), Application of the lifting scheme to rough surfaces, Precision Engineering, 25(2), 83-89, doi:10.1016/S0141-6359(00)00054-4.
  • Jiang X.Q., Blunt L., Stout K.J., (2001b), Lifting wavelet for three- dimensional surface analysis, International Journal of Machine Tools and Manufacture, 41(13-14), 2163-2169, doi: 10.1016/ S0890-6955(01)00083-9.
  • Karu K., Jain A.K., Bolle R.M., (1996), Is there any texture in the image?, Pattern Recognition, 29(9), 1437-1446, doi: 10.1016/0031-3203(96)00004-0.
  • Lam N., Catts D., Quattrochi D., Brown D., McMaster R., (2004), Scale, A Research Agenda for Geographic Information Science'ın İçinde, (Rechcígl M., McMaster R.B., Usery E.L., Ed.), CRC Press, New York, ss. 93-128.
  • Laws K.I., (1980), Textured Image Segmentation, Doktora Tezi, University of Southern California, Los Angeles, California.
  • Li Z., (2008), Multi-scale digital terrain modelling and analysis, In Advances in Digital Terrain Analysis (Zhou Q., Lees B., Tang G., Ed.), Springer, Berlin, ss. 59-83.
  • Li W., Haese-Coat V., Ronsin J., (1997), Residues of morphological filtering by reconstruction for texture classification, Pattern Recognition, 30(7), 1081-1093, doi:10.1016/S0031-3203(96)00146-X.
  • Miliaresis G. (2008). Quantification of terrain processes, Lecture Notes in Geoinformation and Cartography, XIV: 13-28.
  • Miliaresis G., Argialas D.P., (1999), Segmentation of physiographic features from Global Digital Elevation Model/GTOPO30, Computers & Geosciences, 25(7), 715-728, doi: 10.1016/ S0098-3004(99)00025-4. G., Miliaresis Argialas D.P., representation of mountain objects extracted from the Global Digital Elevation Model (GTOPO30), International Journal of Remote Sensing, 23(5), 949-964, doi:10.1080/01431160110070690 (2002), Quantitative
  • Nonomura K., Ono M., Zhou L.B., Shimizu J., Ojima H., (2010), Design of digital filters for Si wafer surface profile measurement: Noise reduction by lifting scheme wavelet transform, Advanced Materials Research, 126-128, 732-737., doi: 10.4028/www. scientific.net/AMR.126-128.732.
  • Ohanian P.P., Dubes R.C., (1992), Performance evaluation for four classes of textural features, Pattern Recognition, 25(8), 819- 833, doi: 10.1016/0031-3203(92)90036-I.
  • Pentland A., (1984), Fractal-based description of natural scenes, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(6), 661-674, doi: 10.1109/TPAMI.1984.4767591.
  • Poulos M.J., Pierce J.L., Flores A.N., Benner S.G., (2012), Hillslope asymmetry maps reveal widespread, multi-scale organization, Geophysical Research. Letters, 39(6), L06406, doi: 10.1029/2012GL051283.
  • Randen T., Husİy J.H., (1999a), Texture segmentation using filters with optimized energy separation, IEEE Transactions on Image Processing, 8(4), 571-582, doi:10.1109/83.753744.
  • Randen T., Husİy J.H., (1999b), Filtering for texture classification: A comparative study, IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4), 291-310, doi: 10.1109/34.761261.
  • Rodríguez-Iturbe I., Rinaldo A., (1997), Fractal River Basins: Chance and Self-Organization, Cambridge University Press, New York.
  • Ruiz L.A., Sarría A.F., Recio J., (2002), Evaluation of texture analysis techniques to characterize vegetation, Recent Advances in Quantitative Remote Sensing'ın İçinde, (Sobrino J.A., Ed.), Universitat de València, Valencia, ss. 514-521.
  • Sagar B.S.D., Murthy M.B.R., Rao C., Raj B., (2003), Morphological connectivity networks from digital elevation models, International Journal of Remote Sensing, 24(3), 573-581, doi:10.1080/01431160304983. to extract ridge-valley
  • Schmidt J., Andrew R., (2005), Multi-scale landform characterization, Area, 37(3), 341-350, doi: 10.1111/j.1475- 4762.2005.00638.x.
  • Sharma M., Singh S., (2001), Evaluation of texture methods for image analysis, The Seventh Australian and New Zealand Intelligent Information Systems Conference (ANZIIS 2001), 18-21 November 2001, Perth, Avusturalya.
  • Srinivasan G.N., Shobha G. (2008), Statistical texture analysis, Proceedings of World Academy of Science, Engineering and Technology, 36, 1264-1269.
  • Starck J.L., (2002), Non-linear multiscale transforms, Multiscale and Multiresolution Methods: Theory and Applications'ın İçinde. Springer, (Barth T.J., Chan T., Haimes R., Ed.), Heidelberg, Almanya, ss. 239-266, doi: 10.1007/978-3-642-56205-1_5.
  • Summerfield M., (1996), Global Geomorphology, Longman, Essex.
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  • Summerfield M., (2005), A tale of two scales, or two geomorphologies, Transactions of the Institute of British Geographers, 30(4), 402- 415, doi: 10.1111/j.1475-5661.2005.00182.x.
  • Sweldens W., (1996), The lifting scheme: A custom-design construction of biothorgonal wavelets, Applied and Computational Harmonic Analysis, 3(2), 186-200, doi:10.1006/acha.1996.0015.
  • Sweldens W., (1997), The lifting scheme: A construction of second generation wavelets, Journal on Mathematical Analysis, 29(2), 511-546, doi: 10.1137/S0036141095289051.
  • Tate N., Wood J., (2001), Fractals and scale dependencies in topography, Modelling Scale in Geographical Information Science'ın İçinde, (Tate N., Atkinson P., Ed.), Wiley, Chicester, ss. 35-51.
  • Tay L. T., Sagar B.S.D., Chuah H.T., (2005), Analysis of geophysical networks derived from multiscale digital elevation models: A morphological approach, IEEE Geoscience and Remote Sensing Letters, 2(4), 399-403, doi: 10.1109/ LGRS.2005.856008.
  • van Ginneken B., Haar Romeny B.M., (2003), Multi- scale texture classification from generalised locally orderless images, Pattern Recognition, 36(4), 899-911, doi: 10.1016/S0031-3203(02)00118-8.
  • Wood J., (2009), Geomorphometry in LandSerf, Developments in Soil Science'ın İçinde, vol. 33: Geomorphometry: Concepts, Software, Applications, (Hengl T., Reuter H.I., Ed.), Elsevier, Armsterdam, ss. 333-349.
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  • Yang M.D., Su T.C., Pan N..F, Liu P., (2011), Feature extraction of sewer pipe defects using wavelet transform and co-occurrence matrix, International Journal of Wavelets, Multiresolution and Information Processing, 9, 211-225, doi:10.1142/ S0219691311004055.
  • Yang Z., Liu H., Yang N., Xiao X., (2009), Study on multiscale generalization of DEM based on lifting scheme, Proceedings of the SPIE' ın İçinde, Volume 7492, (Liu, Y., Tang, X., Ed.), SPIE, Bellingham, Washington, ss. 74922E-74922E-6.
  • Zhao Y., Kazuo K., Fujita F., (2012), Multi-scale decomposition of co-seismic deformation from high resolution DEMs: A case study of the 2004 mid-Niigata earthquake, Acta Geoligica Sinica, 86(4), 1013-1021, doi: 10.1111/j.1755-6724.2012.00725.x.
Toplam 64 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Araştırma Makalesi
Yazarlar

Dinesh Sathyamoorthy Bu kişi benim

Yayımlanma Tarihi 1 Mayıs 2013
Yayımlandığı Sayı Yıl 2013 Sayı: 107

Kaynak Göster

APA Sathyamoorthy, D. (2013). Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi. Jeodezi Ve Jeoinformasyon Dergisi(107), 59-69. https://doi.org/10.9733/jgg.120913.2t
AMA Sathyamoorthy D. Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi. hkmojjd. Mayıs 2013;(107):59-69. doi:10.9733/jgg.120913.2t
Chicago Sathyamoorthy, Dinesh. “Çoklu ölçekli sayısal yükseklik Modellerinden çıkarılan Fizyografik Detaylara Ait yüzey Doku özelliklerinin Gri düzey Eş-oluşum Matrisi Ile Analizi”. Jeodezi Ve Jeoinformasyon Dergisi, sy. 107 (Mayıs 2013): 59-69. https://doi.org/10.9733/jgg.120913.2t.
EndNote Sathyamoorthy D (01 Mayıs 2013) Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi. Jeodezi ve Jeoinformasyon Dergisi 107 59–69.
IEEE D. Sathyamoorthy, “Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi”, hkmojjd, sy. 107, ss. 59–69, Mayıs 2013, doi: 10.9733/jgg.120913.2t.
ISNAD Sathyamoorthy, Dinesh. “Çoklu ölçekli sayısal yükseklik Modellerinden çıkarılan Fizyografik Detaylara Ait yüzey Doku özelliklerinin Gri düzey Eş-oluşum Matrisi Ile Analizi”. Jeodezi ve Jeoinformasyon Dergisi 107 (Mayıs 2013), 59-69. https://doi.org/10.9733/jgg.120913.2t.
JAMA Sathyamoorthy D. Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi. hkmojjd. 2013;:59–69.
MLA Sathyamoorthy, Dinesh. “Çoklu ölçekli sayısal yükseklik Modellerinden çıkarılan Fizyografik Detaylara Ait yüzey Doku özelliklerinin Gri düzey Eş-oluşum Matrisi Ile Analizi”. Jeodezi Ve Jeoinformasyon Dergisi, sy. 107, 2013, ss. 59-69, doi:10.9733/jgg.120913.2t.
Vancouver Sathyamoorthy D. Çoklu ölçekli sayısal yükseklik modellerinden çıkarılan fizyografik detaylara ait yüzey doku özelliklerinin gri düzey eş-oluşum matrisi ile analizi. hkmojjd. 2013(107):59-6.