Araştırma Makalesi
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Yıl 2021, Cilt 8, Sayı 3, 256 - 266, 05.09.2021
https://doi.org/10.30897/ijegeo.688826

Öz

Kaynakça

  • References Bulut, S., & Gunlu, A. (2016). Comparison of different supervised classification algorithms for land use classes (Turkish). Journal of Forestry Faculty, 16(2), 528-535.
  • Buyuksalih, I. (2016). Landsat images classification and change analysis of land cover/use. International Journal of Environment and Geoinformatics, 3(2), 56-65.
  • Colkesen, I., & Kavzoglu, T. (2008). Classification of Land Cover Using Support Vector Machines: Gebze sample (Turkish). (s. 35-45). Kayseri: 2nd Remote Sensing and Geographical Information Systems Symposium.
  • Cortes, C., & Vapnik, V. N. (1995). Support-Vector Network. Machine Learning, 20, 273-397.
  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185-201.
  • Heisele, B., Serre, T., Prentice, S., & Poggio, T. (2003). Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition, 36, 2007-2017.
  • Hong, J., Min, J., Cho, U., & Cho, S. (2008). Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve bayes classifiers. Pattern Recognition, 41, 662-671.
  • Hu, X., & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113, 2089-2102.
  • Huang, C., Davis, L. S., & Townshend, J. R. (2002). An assessment of support vector machines for land cover classification. International Journal Remote Sensing, 23(4), 725-749.
  • Jamsran, B. E., Lin, C., Byambakhuu, I., Raash, J., & Akhmadi, K. (2019). Applying a support vector model to assess land cover changes in the Uvs Lake basin ecoregion in mongolia. Information Processing in Agriculture, 6, 158-169.
  • Joachims, T. (1998). Text Categorization with Support Vector Machines: learning with Many Relevant Features. In Proceedings of European Conference on Machine Learning, (s. 137-142).
  • Karimi, F., Sultana, S., Babakan, A. S., & Suthaharan, S. (2019). An enhanced support vector machine model for urban expansion prediction. Computers, Environment and Urban Systems, 75, 61-75.
  • Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352-359.
  • Kavzoglu, T., & Colkesen, I. (2010). Classification of Satellite Images Using Decision Trees: Kocaeli Case. Electronic Journal of Map Technologies, 2(1), 36-45.
  • Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15, 1667-1689.
  • Liu, C., Nakashima, K., Sako, H., & Fujisawa, H. (2003). Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognition, 36, 2271-2285.
  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870.
  • Lu, W., Wang, W., Leung, A. T., Lo, S., Yuen, R. K., Xu, Z., et al. (2002). Air pollutant parameter forecasting using support vector machines. (s. 630-635). Honolulu: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02.
  • Mathur, A., & Foody, G. M. (2008). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29, 2227-2240.
  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE transactions on geoscience and remote sensing, 42(8), 1778-1790.
  • Mohapatra, R. P., & Wu, C. (2010). High resolution impervious surface estimation: an integration of Ikonos and Landsat-7 ETM+ imagery. Photogrammetric Engineering & Remote Sensing, 76(12), 1329-1341.
  • Montero, P., Moser, G., & Serpico, S. B. (2005). Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on geoscience and remote sensing, 43(3), 559-570.
  • Mountrakis, G., Im, J., & Ogelo, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.
  • Nagel, P., & Yuan, F. (2016). High-resolution land cover and impervious surface classifications in the twin cities metropolitan area with NAIP imagery. Photogrammetric Engineering & Remote Sensing, 82(1), 63-71.
  • Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: an application for mapping urban extensions. Photogrammetry & Remote Sensing, 61, 125-133.
  • Osuna, E. E., Freund, R., & Girosi, F. (1997). Support Vector Machines: Training and Applications. Massachusetts: A.I. Memo No. 1602, C.B.C.L. Paper No. 144, Massachusetts Institute of Technology Artificial Intelligence Laboratory and Center for Biological and Computational Learning Department of Brain and Cognitive Sciences.
  • Petropoulos, G. P., Arvanitis, K., & Sigrimis, N. (2012b). Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Expert Systems with Applications, 39, 3800-3809.
  • Petropoulos, G. P., Kalaitzidis, C., & Vadrevu, K. P. (2012a). Support vector machines and object-based classification for obtaining land-use/cover cartography from hyperion hyperspectral imagery. Computers & Geosciences, 41, 99-107.
  • Serifoglu Yilmaz, C., Gungor, O., & Kahraman, H. T. (2018). Land cover mapping with advanced classification algorithms. Nature Sciences, 13(3), 41-50.
  • Song, X., Duan, Z., & Jiang, X. (2012). Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. International Journal of Remote Sensing, 33(10), 3301-3320.
  • Ustuner, M., & Balik Sanli, F. (2020). Crop classification using multi-temporal polarimetric SAR data (Turkish). Journal of Geodesy and Geoinformation, 1-10.
  • Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.
  • Vapnik, V. N. (2000). The Nature of Statistical Learning Theory, Second Edition. New York: Springer-Verlag.
  • Zhang, L., Zhang, M., & Yao, Y. (2018). Mapping seasonal impervious surface dynamics in Wuhan urban agglomeration, China from 2000 to 2016. International Journal Applied Earth Observation Geoinformation, 70, 63-71.

Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul

Yıl 2021, Cilt 8, Sayı 3, 256 - 266, 05.09.2021
https://doi.org/10.30897/ijegeo.688826

Öz

As a result of the development of cities and inclination towards urbanization, natural areas decreased while urban areas increased. In this respect, determination of impermeable surfaces is important for the problems covering; effects of urbanization on natural environment, global environmental variation, urban atmospheric process, human activities and the effects of urbanization on the environment. Remote sensing images are used to examine and classify land cover/uses. Traditional classification methods are mainly divided into supervised classification and unsupervised classification. The aim of this study is to classify land cover/use and to state temporal change using the Support Vector Machines (SVM) approach, which is a supervised classification method. Arnavutkoy district of Istanbul was chosen as the study area for land use and change detection analysis. Landsat 5/7/8 satellite images of Arnavutkoy district were obtained and SVM process was applied to obtain these images. Firstly, four classes were created for each image: urban areas, vegetation, bare soils and wetlands SVM was applied and accuracy analysis was performed to the images classes of which were created before. CAD software and GIS software were used for image processing. The classification accuracy for SVM was found to be 98.66%, 98.31%, 98.95%, 97.99%, 96.37%, 97.90% (from 1995 to 2019). In addition, ROC analysis was used for comparison of accuracy analysis. As a result of this study, land cover/use change of Arnavutkoy district in the last 20 years has been determined. The urban area of the district was 40.99 〖"km" 〗^"2" in 1995 and 93.76 〖"km" 〗^"2" in 2019. In addition, the impact of the Europe's largest airport on land cover / use has been examined. The results showed that the accuracy of using SVM to classify land use/cover is high. Therefore, it has been proposed that this algorithm is used as an optimal classifier for land use/cover maps.

Kaynakça

  • References Bulut, S., & Gunlu, A. (2016). Comparison of different supervised classification algorithms for land use classes (Turkish). Journal of Forestry Faculty, 16(2), 528-535.
  • Buyuksalih, I. (2016). Landsat images classification and change analysis of land cover/use. International Journal of Environment and Geoinformatics, 3(2), 56-65.
  • Colkesen, I., & Kavzoglu, T. (2008). Classification of Land Cover Using Support Vector Machines: Gebze sample (Turkish). (s. 35-45). Kayseri: 2nd Remote Sensing and Geographical Information Systems Symposium.
  • Cortes, C., & Vapnik, V. N. (1995). Support-Vector Network. Machine Learning, 20, 273-397.
  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80, 185-201.
  • Heisele, B., Serre, T., Prentice, S., & Poggio, T. (2003). Hierarchical classification and feature reduction for fast face detection with support vector machines. Pattern Recognition, 36, 2007-2017.
  • Hong, J., Min, J., Cho, U., & Cho, S. (2008). Fingerprint classification using one-vs-all support vector machines dynamically ordered with naïve bayes classifiers. Pattern Recognition, 41, 662-671.
  • Hu, X., & Weng, Q. (2009). Estimating impervious surfaces from medium spatial resolution imagery using the self-organizing map and multi-layer perceptron neural networks. Remote Sensing of Environment, 113, 2089-2102.
  • Huang, C., Davis, L. S., & Townshend, J. R. (2002). An assessment of support vector machines for land cover classification. International Journal Remote Sensing, 23(4), 725-749.
  • Jamsran, B. E., Lin, C., Byambakhuu, I., Raash, J., & Akhmadi, K. (2019). Applying a support vector model to assess land cover changes in the Uvs Lake basin ecoregion in mongolia. Information Processing in Agriculture, 6, 158-169.
  • Joachims, T. (1998). Text Categorization with Support Vector Machines: learning with Many Relevant Features. In Proceedings of European Conference on Machine Learning, (s. 137-142).
  • Karimi, F., Sultana, S., Babakan, A. S., & Suthaharan, S. (2019). An enhanced support vector machine model for urban expansion prediction. Computers, Environment and Urban Systems, 75, 61-75.
  • Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11, 352-359.
  • Kavzoglu, T., & Colkesen, I. (2010). Classification of Satellite Images Using Decision Trees: Kocaeli Case. Electronic Journal of Map Technologies, 2(1), 36-45.
  • Keerthi, S. S., & Lin, C. J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15, 1667-1689.
  • Liu, C., Nakashima, K., Sako, H., & Fujisawa, H. (2003). Handwritten digit recognition: benchmarking of state-of-the-art techniques. Pattern Recognition, 36, 2271-2285.
  • Lu, D., & Weng, Q. (2007). A survey of image classification methods and techniques for improving classification performance. International Journal of Remote Sensing, 28(5), 823-870.
  • Lu, W., Wang, W., Leung, A. T., Lo, S., Yuen, R. K., Xu, Z., et al. (2002). Air pollutant parameter forecasting using support vector machines. (s. 630-635). Honolulu: Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02.
  • Mathur, A., & Foody, G. M. (2008). Crop classification by support vector machine with intelligently selected training data for an operational application. International Journal of Remote Sensing, 29, 2227-2240.
  • Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE transactions on geoscience and remote sensing, 42(8), 1778-1790.
  • Mohapatra, R. P., & Wu, C. (2010). High resolution impervious surface estimation: an integration of Ikonos and Landsat-7 ETM+ imagery. Photogrammetric Engineering & Remote Sensing, 76(12), 1329-1341.
  • Montero, P., Moser, G., & Serpico, S. B. (2005). Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE Transactions on geoscience and remote sensing, 43(3), 559-570.
  • Mountrakis, G., Im, J., & Ogelo, C. (2011). Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing, 66, 247-259.
  • Nagel, P., & Yuan, F. (2016). High-resolution land cover and impervious surface classifications in the twin cities metropolitan area with NAIP imagery. Photogrammetric Engineering & Remote Sensing, 82(1), 63-71.
  • Nemmour, H., & Chibani, Y. (2006). Multiple support vector machines for land cover change detection: an application for mapping urban extensions. Photogrammetry & Remote Sensing, 61, 125-133.
  • Osuna, E. E., Freund, R., & Girosi, F. (1997). Support Vector Machines: Training and Applications. Massachusetts: A.I. Memo No. 1602, C.B.C.L. Paper No. 144, Massachusetts Institute of Technology Artificial Intelligence Laboratory and Center for Biological and Computational Learning Department of Brain and Cognitive Sciences.
  • Petropoulos, G. P., Arvanitis, K., & Sigrimis, N. (2012b). Hyperion hyperspectral imagery analysis combined with machine learning classifiers for land use/cover mapping. Expert Systems with Applications, 39, 3800-3809.
  • Petropoulos, G. P., Kalaitzidis, C., & Vadrevu, K. P. (2012a). Support vector machines and object-based classification for obtaining land-use/cover cartography from hyperion hyperspectral imagery. Computers & Geosciences, 41, 99-107.
  • Serifoglu Yilmaz, C., Gungor, O., & Kahraman, H. T. (2018). Land cover mapping with advanced classification algorithms. Nature Sciences, 13(3), 41-50.
  • Song, X., Duan, Z., & Jiang, X. (2012). Comparison of artificial neural networks and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image. International Journal of Remote Sensing, 33(10), 3301-3320.
  • Ustuner, M., & Balik Sanli, F. (2020). Crop classification using multi-temporal polarimetric SAR data (Turkish). Journal of Geodesy and Geoinformation, 1-10.
  • Vapnik, V. N. (1995). The Nature of Statistical Learning Theory. New York: Springer-Verlag.
  • Vapnik, V. N. (2000). The Nature of Statistical Learning Theory, Second Edition. New York: Springer-Verlag.
  • Zhang, L., Zhang, M., & Yao, Y. (2018). Mapping seasonal impervious surface dynamics in Wuhan urban agglomeration, China from 2000 to 2016. International Journal Applied Earth Observation Geoinformation, 70, 63-71.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Articles
Yazarlar

Hatice ÇATAL REİS (Sorumlu Yazar)
GÜMÜŞHANE ÜNİVERSİTESİ
0000-0003-2696-2446
Türkiye


Gülsena YILANCI
GÜMÜŞHANE ÜNİVERSİTESİ
0000-0001-7895-0506
Türkiye

Yayımlanma Tarihi 5 Eylül 2021
Yayınlandığı Sayı Yıl 2021, Cilt 8, Sayı 3

Kaynak Göster

Bibtex @araştırma makalesi { ijegeo688826, journal = {International Journal of Environment and Geoinformatics}, issn = {}, eissn = {2148-9173}, address = {}, publisher = {Cem GAZİOĞLU}, year = {2021}, volume = {8}, pages = {256 - 266}, doi = {10.30897/ijegeo.688826}, title = {Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul}, key = {cite}, author = {Çatal Reis, Hatice and Yılancı, Gülsena} }
APA Çatal Reis, H. & Yılancı, G. (2021). Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul . International Journal of Environment and Geoinformatics , 8 (3) , 256-266 . DOI: 10.30897/ijegeo.688826
MLA Çatal Reis, H. , Yılancı, G. "Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul" . International Journal of Environment and Geoinformatics 8 (2021 ): 256-266 <https://dergipark.org.tr/tr/pub/ijegeo/issue/61200/688826>
Chicago Çatal Reis, H. , Yılancı, G. "Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul". International Journal of Environment and Geoinformatics 8 (2021 ): 256-266
RIS TY - JOUR T1 - Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul AU - Hatice Çatal Reis , Gülsena Yılancı Y1 - 2021 PY - 2021 N1 - doi: 10.30897/ijegeo.688826 DO - 10.30897/ijegeo.688826 T2 - International Journal of Environment and Geoinformatics JF - Journal JO - JOR SP - 256 EP - 266 VL - 8 IS - 3 SN - -2148-9173 M3 - doi: 10.30897/ijegeo.688826 UR - https://doi.org/10.30897/ijegeo.688826 Y2 - 2021 ER -
EndNote %0 International Journal of Environment and Geoinformatics Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul %A Hatice Çatal Reis , Gülsena Yılancı %T Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul %D 2021 %J International Journal of Environment and Geoinformatics %P -2148-9173 %V 8 %N 3 %R doi: 10.30897/ijegeo.688826 %U 10.30897/ijegeo.688826
ISNAD Çatal Reis, Hatice , Yılancı, Gülsena . "Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul". International Journal of Environment and Geoinformatics 8 / 3 (Eylül 2021): 256-266 . https://doi.org/10.30897/ijegeo.688826
AMA Çatal Reis H. , Yılancı G. Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul. International Journal of Environment and Geoinformatics. 2021; 8(3): 256-266.
Vancouver Çatal Reis H. , Yılancı G. Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul. International Journal of Environment and Geoinformatics. 2021; 8(3): 256-266.
IEEE H. Çatal Reis ve G. Yılancı , "Determination of Land Use Change using Support Vector Machines: A Case Study of Arnavutkoy, Istanbul", International Journal of Environment and Geoinformatics, c. 8, sayı. 3, ss. 256-266, Eyl. 2021, doi:10.30897/ijegeo.688826