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Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran

Year 2016, Volume: 66 Issue: 1, 90 - 103, 01.01.2016

Ahmad Maleknezhad Yazdi , Vahid Eisavi , Ali Shahsavari

https://doi.org/10.17099/jffiu.01280

Abstract

Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran

Abstract: Due to rapid population growth over recent decades, changes of urban areas have significantly impacted the environment. Urban is a heterogeneous and highly fragmented environment which has made them a challenging area for remote sensing imagery. The reliability of the information delivered by remote sensing applications in urban area highly depends on the quality of spatial and spectral data. Accordingly, the objective of this study is to analyze the impact of incorporation of Hyperion imagery and textural characteristics of high resolution panchromatic ALI imagery in classifying of urban region of south west of Tehran. To this end, we extracted textural information from panchromatic ALI imagery using gray-level co-occurrence matrix (GLCM) method. Classification was carried out by SVM method in five scenarios: Classification of spectral band of CNT method, classification of spectral bands plus texture with window size 3, size 5, size 7 and size 9. The classification results show that the urban areas of south west of Tehran are insufficiently characterized by the Hyperion satellite imagery. A quantitative assessment of the results demonstrated that the use of texture information improved urban land covers classification. As a result, combining of texture information with Hyperion imagery decreases class confusion specifically in heterogonous classes. The GLCM features show great potential for land use cover classification in heterogeneous areas with rich textural information.

Keywords: Hyperspectral imagery, image texture, GLCM, remote sensing, SVM classification.

İran Tahran şehri güney bölgesinde kent alanlarının sınıflandırılmasında SVM yöntemi ile hiperspektral görüntü ve tekstür bilgilerinin birlikte kullanılması

Özet: Son yıllarda hızlı nüfus artışı ve kentsel alanlardaki değişimler çevreyi önemli bir şekilde etkilemiştir. Kentsel alanlar heterojenik ve parçalanmış bir yapıya sahiptir, bu durum uzaktan algılama görüntüleri açısından zorlu bir durum yaratmaktadır. Kentsel alanlarda uzaktan algılama uygulamalarından elde edilen bilgilerin güvenilirliği  mekansal ve spektral verilerin kalitesine bağlı olarak değişmektedir. Dolayısıyla, bu çalışmanın amacı Tahran'ın güney batısındaki kentsel bölgede Hyperion görüntüleri ve yüksek çözünürlüklü pankromatik ALI görüntülerinin dokusal özelliklerinin esas etkisini analiz etmektir. Bu amaçla, gri-seviyeli eş-oluşum matrisi (gray-level co-occurrence matrix) (GLCM) yöntemini kullanarak pankromatik ALI görüntülerinden yapısal bilgi ayıklanmıştır. Sınıflandırma beş senaryo halinde SVM yöntemi ile gerçekleştirilmiştir: CNT yöntemiyle spektral bantların sınıflandırılması, spektral bant sınıflandırılması pencere boyutu 3, boyut 5, boyut 7 ve boyut 9. Sınıflandırma sonuçları Tahran güney batı kentsel alanların  Hyperion uydu görüntüleri ile yeterince karakterize edilemediğini göstermektedir. Sonuçlar yapısal bilgilerin kullanımı ile kentsel arazi sınıflandırmalarının daha başarılı bir şekilde yapılabildiğini göstermektedir. Sonuç olarak, Hyperion görüntüleri ile yapısal bilgilerinin birleştirilmesi heterojenik sınıflandırmada karışıklığı azaltmaktadır. GLCM özellikleri içerdikleri zengin yapısal bilgi ile heterojen alanlarda arazi kullanım sınıflandırmaları için büyük bir potansiyel gösterirler.

Anahtar Kelimeler: Hiperspektral görüntü, görüntü tekstürü, GLCM, uzaktan algılama, SVM sınıflandırması

Received (Geliş tarihi): 09 February 2015 - Revised (Düzeltme tarihi): 19 February 2015 -   Accepted (Kabul tarihi): 19 February 2015

To cite this article: Yazdi, A.M.,  Eisavi, V., Shahsavari, A., 2016. Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran. Journal of the Faculty of Forestry Istanbul University 66(1): 90-103. DOI: 10.17099/jffiu.01280

Keywords

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References

  • Bokoye, A.I., Dionne, P., 2004. Urban material characterization from the Hyperion hyperspectral imager: Application to downtown Montreal (Quebec, Canada). Image and Signal Processing for Remote Sensing 5238: 569- 574. Conference No.9, Barcelona, Spain, Doi: http://dx.doi.org/10.1117/12.511150
  • Camps-Valls, G., Bruzzone, L., 2005. Kernel-based methods for Hyperspectral image classification, IEEE Transactions on Geoscience Remote Sensing 43: 1351–1362.
  • Carleer, A. P., Wolff, E, 2006. Urban land covermulti-level region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing 27: 1035−1051.
  • Carlson, T.N., Sanchez-Azofeifa, G.A. 1999. Satellite remote sensing of land use changes in and around San José, Costa Rica. Remote Sensing of Environment 70: 247–256.
  • Cavalli, M.R., Fusilli, L., Pascucci, S., Pignatti, S., Federico Santini, F., 2008. Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy). Sensors 8: 3299-3320. Doi: http://dx.doi.org/10.3390/s8053299
  • Clausi, D. A., Yue, B., 2004. Comparing co-occurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery. IEEE Transaction on Geoscience and Remote Sensing 42: 215-228.
  • Colgan, M. S., Baldeck, C. A., Féret, J. B., Asner, G. P., 2012. Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data". Remote Sensing 4(11): 3462-3480. Doi: http://dx.doi.org/10.3390/rs4113462
  • Féret, J., Asner, G. P., 2013. Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE Transactions on Geoscience Remote Sensing 51: 73-84.
  • Forster, B.C., 1985. An examination of some problems and solutions in monitoring urban areas from satellite platforms. International Journal of Remote Sensing 6: 139-151. Doi: http://dx.doi.org/10.1080/01431168508948430
  • Fauvel, M., 2007. Spectral and spatial methods for the classification of urban remote sensing data, Ph.D. dissertation, Grenoble Inst. Technol., Grenoble, France.
  • Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R., 2008. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles. IEEE Transactions on Geoscience Remote Sensing 46: 3804-3814. Doi: http://dx.doi.org/10.1109/TGRS.2008.922034
  • Goetz, A.F.H., 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment 113: S5–S16. Doi: http://dx.doi.org/10.1016/j.rse.2007.12.014
  • Goumehei, E., 2010. Contextual image classification with support vector machine". M.Sc Thesis, ITC, Enscede.
  • Gualtieri, J.A., 2009. The Support Vector Machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data, In: Kernel Methods for Remote Sensing Data Analysis, Edited by Camps-Valls, G., & Bruzzone, L., Wiley, New York, 51-83.
  • Hall-Beyer, M., 2007. GLCM Texture: A Tutorial, Version 2.10. Viewed 16 http://www.fp.ucalgary.ca/mhallbey/tutorial.htm.
  • Haralick, R.M., 1979. Statistical and structural approaches to texture. In: Proceedings of the IEEE 67: 786–804.
  • Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems Man, and Cybernetics SMC-3 610–621 November (6).
  • Huang, C., Davis, L.S., Townshend, J.R.G., 2002. An assessment of support vector machines for land cover classification". International Journal of Remote Sensing 23: 725-749. Doi: http://dx.doi.org/10.1080/01431160110040323
  • Kawishwar. P., 2007. Atmosphere correction models for retrievals of calibrated spectral profiles from Hyperion EO-1 data, M.Sc. Thesis, ITC, Enscede.
  • Mather, P., Tso, B., 2009. Classification methods for remotely sensed data (Second Edition). CRC press. Boca Raton.
  • Melgan, F., Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with support vector machine,
  • IEEE Transactions on Geoscience and Remote Sensing 42: 1778-1790. Doi: http://dx.doi.org/10.1109/TGRS.2004.831865
  • Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66: 247-259. Doi: http://dx.doi.org/10.1016/j.isprsjprs.2010.11.001
  • Murray, H., Lucieer, A., Williams, R., 2010. Texture-based classification of sub-Antarctic vegetation communities on Heard Island. International Journal of Applied Earth Observation and Geoinformation 12: 138- 149. Doi: http://dx.doi.org/10.1016/j.jag.2010.01.006
  • Oommen, T., Misra, D., Twarakavi, N. K., Prakash, A., Sahoo, B., Bandopadhyay, S., 2008. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences 40: 409-424. Doi: http://dx.doi.org/10.1007/s11004-008-9156-6
  • Pacifici, F., Chini, M., Emery, W.J., 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic. Remote Sensing of Environment 113: 1276–1292.
  • Pacifici, F., Del Frate, F., Solimini, C., Emery, W.J. 2007. An innovative neural-net method to detect temporal changes in high-resolution optical satellite imagery. IEEE Transaction on Geoscience and Remote Sensing 45: 2940−2952.
  • Petropoulos, G.P., Knorr, W., Scholze, M., Boschetti, L., Karantounias, G., 2010. Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007. Natural Hazards and Earth System Science 10: 305-317. Doi: http://dx.doi.org/10.5194/nhess-10-305-2010
  • Puissant, A., Hirsch, J., &Weber, C. (2005). The utility of texture analysis to improve perpixel classification for high to very high spatial resolution imagery. International Journal of Remote Sensing 26: 733−745.
  • Ridd, M.K. 1995. Exploring a V–I–S (vegetation–impervious surface–soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing 16: 2165-2185.
  • Shanmugan, K. S., Narayanan, V., Frost, V. S., Stiles, J. A., Holtzman, J. C., 1981. Textural features for Dadar image analysis. IEEE Transactions on Geoscience and Remote Sensing 19: 153−156.
  • Small, C. Scaling Properties of Urban Reflectance Spectra. In Proceeding of AVIRIS Earth Science and Applications Workshop, Pasadena, CA, 27 Feb -2 Mar, 2001.
  • Small, C., 2005. A global analysis of urban reflectance. International Journal of Remote Sensing 26: 661-681. Doi:
  • http://dx.doi.org/10.1080/01431160310001654950
  • Wang, L., Zhang, S., 2014. Incorporation of texture information in a SVM method for classifying salt cedar in western China. Remote Sensing Letters 5: 501-510. Doi: http://dx.doi.org/10.1080/2150704X.2014.928422
  • Welikanna, D.R., Tolpekin, V., Yogesh K., 2008. Analysis of the Effectiveness of Spectral Mixture Analysis and Markov Random Field Based Super Resolution Mapping Over an Urban Environment. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science Vol XXXVII, Part B7: 641-649
  • Yuntao, Q., Minchao, Y., Jun, Z., 2012. Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features. IEEE Transactions on Geoscience Remote Sensing 51: 2276-2291.
  • Zhang, Q.,Wang, J., Gong, P., Shi, P., 2003. Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis. International Journal of Remote Sensing 24: 4137-4160. Doi: http://dx.doi.org/10.1080/0143116031000070445

İran Tahran şehri güney bölgesinde kent alanlarının sınıflandırılmasında SVM yöntemi ile hiperspektral görüntü ve tekstür bilgilerinin birlikte kullanılması

Year 2016, Volume: 66 Issue: 1, 90 - 103, 01.01.2016

Ahmad Maleknezhad Yazdi , Vahid Eisavi , Ali Shahsavari

https://doi.org/10.17099/jffiu.01280

Abstract

Son yıllarda hızlı nüfus artışı ve kentsel alanlardaki değişimler çevreyi önemli bir şekilde etkilemiştir. Kentsel alanlar heterojenik ve parçalanmış bir yapıya sahiptir, bu durum uzaktan algılama görüntüleri açısından zorlu bir durum yaratmaktadır. Kentsel alanlarda uzaktan algılama uygulamalarından elde edilen bilgilerin güvenilirliği mekansal ve spektral verilerin kalitesine bağlı olarak değişmektedir. Dolayısıyla, bu çalışmanın amacı Tahran'ın güney batısındaki kentsel bölgede Hyperion görüntüleri ve yüksek çözünürlüklü pankromatik ALI görüntülerinin dokusal özelliklerinin esas etkisini analiz etmektir. Bu amaçla, gri-seviyeli eş-oluşum matrisi (gray-level co-occurrence matrix) (GLCM) yöntemini kullanarak pankromatik ALI görüntülerinden yapısal bilgi ayıklanmıştır. Sınıflandırma beş senaryo halinde SVM yöntemi ile gerçekleştirilmiştir. CNT yöntemiyle spektral bantların sınıflandırılması, spektral bant sınıflandırılması pencere boyutu 3, boyut 5, boyut 7 ve boyut 9. sınıflandırma sonuçları Tahran güney batı kentsel alanların Hyperion uydu görüntüleri ile yeterince karakterize edilemediğini göstermektedir. Sonuçlar yapısal bilgilerin kullanımı ile kentsel arazi sınıflandırmalarının daha başarılı bir şekilde yapılabildiğini göstermektedir. Sonuç olarak, Hyperion görüntüleri ile yapısal bilgilerinin birleştirilmesi heterojenik sınıflandırmada karışıklığı azaltmaktadır. GLCM özellikleri içerdikleri zengin yapısal bilgi ile heterojen alanlarda arazi kullanım sınıflandırmaları için büyük bir potansiyel gösterirler.

Keywords

Hiperspektral görüntü görüntü tekstürü GLCM uzaktan algılama SVM sınıflandırması

References

  • Bokoye, A.I., Dionne, P., 2004. Urban material characterization from the Hyperion hyperspectral imager: Application to downtown Montreal (Quebec, Canada). Image and Signal Processing for Remote Sensing 5238: 569- 574. Conference No.9, Barcelona, Spain, Doi: http://dx.doi.org/10.1117/12.511150
  • Camps-Valls, G., Bruzzone, L., 2005. Kernel-based methods for Hyperspectral image classification, IEEE Transactions on Geoscience Remote Sensing 43: 1351–1362.
  • Carleer, A. P., Wolff, E, 2006. Urban land covermulti-level region-based classification of VHR data by selecting relevant features. International Journal of Remote Sensing 27: 1035−1051.
  • Carlson, T.N., Sanchez-Azofeifa, G.A. 1999. Satellite remote sensing of land use changes in and around San José, Costa Rica. Remote Sensing of Environment 70: 247–256.
  • Cavalli, M.R., Fusilli, L., Pascucci, S., Pignatti, S., Federico Santini, F., 2008. Hyperspectral Sensor Data Capability for Retrieving Complex Urban Land Cover in Comparison with Multispectral Data: Venice City Case Study (Italy). Sensors 8: 3299-3320. Doi: http://dx.doi.org/10.3390/s8053299
  • Clausi, D. A., Yue, B., 2004. Comparing co-occurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery. IEEE Transaction on Geoscience and Remote Sensing 42: 215-228.
  • Colgan, M. S., Baldeck, C. A., Féret, J. B., Asner, G. P., 2012. Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data". Remote Sensing 4(11): 3462-3480. Doi: http://dx.doi.org/10.3390/rs4113462
  • Féret, J., Asner, G. P., 2013. Tree species discrimination in tropical forests using airborne imaging spectroscopy. IEEE Transactions on Geoscience Remote Sensing 51: 73-84.
  • Forster, B.C., 1985. An examination of some problems and solutions in monitoring urban areas from satellite platforms. International Journal of Remote Sensing 6: 139-151. Doi: http://dx.doi.org/10.1080/01431168508948430
  • Fauvel, M., 2007. Spectral and spatial methods for the classification of urban remote sensing data, Ph.D. dissertation, Grenoble Inst. Technol., Grenoble, France.
  • Fauvel, M., Benediktsson, J.A., Chanussot, J., Sveinsson, J.R., 2008. Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles. IEEE Transactions on Geoscience Remote Sensing 46: 3804-3814. Doi: http://dx.doi.org/10.1109/TGRS.2008.922034
  • Goetz, A.F.H., 2009. Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment 113: S5–S16. Doi: http://dx.doi.org/10.1016/j.rse.2007.12.014
  • Goumehei, E., 2010. Contextual image classification with support vector machine". M.Sc Thesis, ITC, Enscede.
  • Gualtieri, J.A., 2009. The Support Vector Machine (SVM) algorithm for supervised classification of hyperspectral remote sensing data, In: Kernel Methods for Remote Sensing Data Analysis, Edited by Camps-Valls, G., & Bruzzone, L., Wiley, New York, 51-83.
  • Hall-Beyer, M., 2007. GLCM Texture: A Tutorial, Version 2.10. Viewed 16 http://www.fp.ucalgary.ca/mhallbey/tutorial.htm.
  • Haralick, R.M., 1979. Statistical and structural approaches to texture. In: Proceedings of the IEEE 67: 786–804.
  • Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. Textural features for image classification. IEEE Transactions on Systems Man, and Cybernetics SMC-3 610–621 November (6).
  • Huang, C., Davis, L.S., Townshend, J.R.G., 2002. An assessment of support vector machines for land cover classification". International Journal of Remote Sensing 23: 725-749. Doi: http://dx.doi.org/10.1080/01431160110040323
  • Kawishwar. P., 2007. Atmosphere correction models for retrievals of calibrated spectral profiles from Hyperion EO-1 data, M.Sc. Thesis, ITC, Enscede.
  • Mather, P., Tso, B., 2009. Classification methods for remotely sensed data (Second Edition). CRC press. Boca Raton.
  • Melgan, F., Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with support vector machine,
  • IEEE Transactions on Geoscience and Remote Sensing 42: 1778-1790. Doi: http://dx.doi.org/10.1109/TGRS.2004.831865
  • Mountrakis, G., Im, J., Ogole, C., 2011. Support vector machines in remote sensing: A review. ISPRS Journal of Photogrammetry and Remote Sensing 66: 247-259. Doi: http://dx.doi.org/10.1016/j.isprsjprs.2010.11.001
  • Murray, H., Lucieer, A., Williams, R., 2010. Texture-based classification of sub-Antarctic vegetation communities on Heard Island. International Journal of Applied Earth Observation and Geoinformation 12: 138- 149. Doi: http://dx.doi.org/10.1016/j.jag.2010.01.006
  • Oommen, T., Misra, D., Twarakavi, N. K., Prakash, A., Sahoo, B., Bandopadhyay, S., 2008. An objective analysis of support vector machine based classification for remote sensing. Mathematical Geosciences 40: 409-424. Doi: http://dx.doi.org/10.1007/s11004-008-9156-6
  • Pacifici, F., Chini, M., Emery, W.J., 2009. A neural network approach using multi-scale textural metrics from very high-resolution panchromatic. Remote Sensing of Environment 113: 1276–1292.
  • Pacifici, F., Del Frate, F., Solimini, C., Emery, W.J. 2007. An innovative neural-net method to detect temporal changes in high-resolution optical satellite imagery. IEEE Transaction on Geoscience and Remote Sensing 45: 2940−2952.
  • Petropoulos, G.P., Knorr, W., Scholze, M., Boschetti, L., Karantounias, G., 2010. Combining ASTER multispectral imagery analysis and support vector machines for rapid and cost-effective post-fire assessment: a case study from the Greek wildland fires of 2007. Natural Hazards and Earth System Science 10: 305-317. Doi: http://dx.doi.org/10.5194/nhess-10-305-2010
  • Puissant, A., Hirsch, J., &Weber, C. (2005). The utility of texture analysis to improve perpixel classification for high to very high spatial resolution imagery. International Journal of Remote Sensing 26: 733−745.
  • Ridd, M.K. 1995. Exploring a V–I–S (vegetation–impervious surface–soil) model for urban ecosystem analysis through remote sensing: comparative anatomy for cities. International Journal of Remote Sensing 16: 2165-2185.
  • Shanmugan, K. S., Narayanan, V., Frost, V. S., Stiles, J. A., Holtzman, J. C., 1981. Textural features for Dadar image analysis. IEEE Transactions on Geoscience and Remote Sensing 19: 153−156.
  • Small, C. Scaling Properties of Urban Reflectance Spectra. In Proceeding of AVIRIS Earth Science and Applications Workshop, Pasadena, CA, 27 Feb -2 Mar, 2001.
  • Small, C., 2005. A global analysis of urban reflectance. International Journal of Remote Sensing 26: 661-681. Doi:
  • http://dx.doi.org/10.1080/01431160310001654950
  • Wang, L., Zhang, S., 2014. Incorporation of texture information in a SVM method for classifying salt cedar in western China. Remote Sensing Letters 5: 501-510. Doi: http://dx.doi.org/10.1080/2150704X.2014.928422
  • Welikanna, D.R., Tolpekin, V., Yogesh K., 2008. Analysis of the Effectiveness of Spectral Mixture Analysis and Markov Random Field Based Super Resolution Mapping Over an Urban Environment. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Science Vol XXXVII, Part B7: 641-649
  • Yuntao, Q., Minchao, Y., Jun, Z., 2012. Hyperspectral Image Classification Based on Structured Sparse Logistic Regression and Three-Dimensional Wavelet Texture Features. IEEE Transactions on Geoscience Remote Sensing 51: 2276-2291.
  • Zhang, Q.,Wang, J., Gong, P., Shi, P., 2003. Study of urban spatial patterns from SPOT panchromatic imagery using textural analysis. International Journal of Remote Sensing 24: 4137-4160. Doi: http://dx.doi.org/10.1080/0143116031000070445
There are 38 citations in total.

Details

Primary Language English
Journal Section Research Articles (Araştırma Makalesi)
Authors

Ahmad Maleknezhad Yazdi

Vahid Eisavi

Ali Shahsavari This is me

Publication Date January 1, 2016
Published in Issue Year 2016 Volume: 66 Issue: 1

Cite

APA Maleknezhad Yazdi, A., Eisavi, V., & Shahsavari, A. (2016). Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran. Journal of the Faculty of Forestry Istanbul University, 66(1), 90-103. https://doi.org/10.17099/jffiu.01280
AMA Maleknezhad Yazdi A, Eisavi V, Shahsavari A. Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran. J FAC FOR ISTANBUL U. January 2016;66(1):90-103. doi:10.17099/jffiu.01280
Chicago Maleknezhad Yazdi, Ahmad, Vahid Eisavi, and Ali Shahsavari. “Incorporation of Hyperspectral Imagery and Texture Information in a SVM Method for Classifying Urban Area of Southern Regions of Tehran, Iran”. Journal of the Faculty of Forestry Istanbul University 66, no. 1 (January 2016): 90-103. https://doi.org/10.17099/jffiu.01280.
EndNote Maleknezhad Yazdi A, Eisavi V, Shahsavari A (January 1, 2016) Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran. Journal of the Faculty of Forestry Istanbul University 66 1 90–103.
IEEE A. Maleknezhad Yazdi, V. Eisavi, and A. Shahsavari, “Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran”, J FAC FOR ISTANBUL U, vol. 66, no. 1, pp. 90–103, 2016, doi: 10.17099/jffiu.01280.
ISNAD Maleknezhad Yazdi, Ahmad et al. “Incorporation of Hyperspectral Imagery and Texture Information in a SVM Method for Classifying Urban Area of Southern Regions of Tehran, Iran”. Journal of the Faculty of Forestry Istanbul University 66/1 (January 2016), 90-103. https://doi.org/10.17099/jffiu.01280.
JAMA Maleknezhad Yazdi A, Eisavi V, Shahsavari A. Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran. J FAC FOR ISTANBUL U. 2016;66:90–103.
MLA Maleknezhad Yazdi, Ahmad et al. “Incorporation of Hyperspectral Imagery and Texture Information in a SVM Method for Classifying Urban Area of Southern Regions of Tehran, Iran”. Journal of the Faculty of Forestry Istanbul University, vol. 66, no. 1, 2016, pp. 90-103, doi:10.17099/jffiu.01280.
Vancouver Maleknezhad Yazdi A, Eisavi V, Shahsavari A. Incorporation of hyperspectral imagery and texture information in a SVM method for classifying urban area of southern regions of Tehran, Iran. J FAC FOR ISTANBUL U. 2016;66(1):90-103.
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