Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery

KALKAN, Kaan; HAFIZI, Hamed

Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery

Hamed HAFIZI, (Eskişehir Teknik Üniversitesi)

Kaan KALKAN (Eskişehir Teknik Üniversitesi)

Havacılık ve Uzay Teknolojileri Dergisi

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Yıl: 2020 Cilt: 13 Sayı: 1 Sayfa Aralığı: 87 - 89 Metin Dili: İngilizce İndeks Tarihi: 17-06-2020

Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery

Öz:

Water body extraction plays a key role in water resource management and development which is one of the important issues in research topics related to remote sensing in recent decades. Pixel-based; supervised and unsupervised classification methods and water indices have been developed for extracting water body from satellite imagery while for some cases they are unable to differentiate water body from low albedo features with different spectral characteristics. The objective of this paper is to evaluate the efficiency of object-based, pixel-based and water indices for water body extraction. Landsat-8 imagery used for this study which has 30m resolution in Visible, NIR (Near-Infrared), SWIR (Shortwave Infrared) spectrum and panchromatic band with 15m resolution. The Edge-Based Segmentation (EBS) algorithm and the Support Vector Machine (SVM) classification method has been used for object-based approach, on the other hand, Maximum Likelihood and K-means methods has been selected for pixel-based classification. As a water index, Normalized Difference Water Index (NDWI) has been selected to extract water body from satellite imagery. The study area selected in both urban and mountainous regions with different characteristics both located in Turkey. Results show that object-based water body extraction is more accurate than the other methods tested.

Anahtar Kelime:

Konular: Mühendislik, Hava ve Uzay

Landsat-8 Görüntüleri Kullanılarak Obje Tabanlı Su ile Kaplı Alan Tespiti Yaklaşımlarının Değerlendirilmesi

Öz:

Su ile kaplı alanların çıkarılması, su kaynaklarının yönetiminde ve planlanmasında önemli bir rol oynamaktadır ve bu konu son yıllarda uzaktan algılamanın önemli araştırma konularından biri olmuştur. Piksel tabanlı, kontrollü sınıflandırma, kontrolsüz sınıflandırma yöntemleri ve su indeksleri, sulak alanların uydu görüntülerinden çıkarılması için geliştirilmiş olsada, bu yöntemler düşük yansıtım değerine sahip alanlar ile sulak alanları ayırt etmede zorlanabilmektedir. Bu çalışmada obje tabanlı sınıflandırma, piksel tabanlı sınıflandırma ve su indekslerinin su ile kaplı alanların çıkarımındaki başarısı değerlendirilecektir. Görünür, kızılötesi ve kısa dalga kızılötesi bant çözünürlüğü 30 metre olan ve 15 metre çözünürlükte pankromatik banda sahip olan Landsat-8 görüntüleri test için seçilmiştir. Obje tabanlı sınıflandırma için, “Edge Base” segmentasyon algoritması ve Destek Vektör Makinaları (DVM) sınıflandırma yöntemleri seçilmiş, piksel tabanlı sınıflandırma için ise Ençok Benzerlik ve K-ortalamalar yöntemleri kullanılmıştır. Uydu görüntülerinden su belirleme indeksi olarak Normalize Edilmiş Fark Su İndeksi kullanılmıştır (NDWI). Çalışma alanları Türkiye’de bulunan farklı karakteristiklere sahip şehirleşmiş ve dağlık alanlardan seçilmiştir. Su ile kaplı alanların çıkarımında obje tabanlı sınıflandırmanın daha başarılı olduğu gözlemlenmiştir.

Anahtar Kelime:

Konular: Mühendislik, Hava ve Uzay

Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık

[1] EM. Haas, Bartholomé. E. and B. Combal, "Time Series Analysis of Optical Remote Sensing Data for the Mapping of Temporary Surface Water Bodies in Sub-Saharan Western Africa," Journal of Hydrology, Vol. 370, p. 52-63, 2009.
[2] C. Prigent, F. Papa, F. Aires, C. Jimenez, WB. Rossow and E. Matthews, " Changes in land surface water dynamics since the 1990s and relation to population pressure," geophysical research letters, vol. 39(8), 2012.
[3] J. Gardelle, P. Hiernaux, L. Kergoat and M. Grippa, " Less rain, more water in ponds: a remote sensing study of the dynamics of surface water from 1950 to present in pastoral Sahel (Gourma region, Mali)," Hydrology and Earth System Sciences, Vol. 14, p. 309–324, 2010.
[4] Z. Zhang, Z. Dong, C. Yan and G. Hu, " Change of lake area in the southeastern part of China’s Badain Jaran Sand Sea and its implications for recharge sources.," Journal of Arid Land, Vol. 7, No. 1, p. 1–9, 2015.
[5] Acharya, Tri, Anoj Subedi, and Dong Lee. "Evaluation of Water Indices for Surface Water Extraction in a Landsat 8 Scene of Nepal," Sensors, Vol. 18, No. 8, p. 2580, 2018.
[6] Dominici, Donatella, et al. "High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms." Geosciences, Vol. 9, No. 3, p. 123, 2019.
[7] A. G. Dekker, T. J. Malthus, M. M. Wijnen and E. Seyhan, " Remote sensing as a tool for assessing water quality in Loosdrecht lakes," Hydrobiologia, Vol. 233, p. 137-159, 1992.
[8] Lai, Yuequn, Jing Zhang, and Yongyu Song. "Surface Water Information Extraction Based on High-Resolution Image." IOP Conference Series: Earth and Environmental Science, Vol. 330, No. 3, p. 032013, IOP Publishing, 2019.
[9] B. C. Gao, "NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space.," Remote sensing of environment, Vol. 58, No. 3, p. 257–266, 1996.
[10] Q. Qin, Y. Yuan. and R. Lu., "A new approach to object recognition on high resolution satellite image," International archives of photogrammetry and remote sensing, Vol. 33(B3/2; PART 3), p. 753-760, 2000.
[11] G. Kaplan. and U. Avdan., "Object-based water body extraction model using Sentinel-2 satellite imagery," European Journal of Remote Sensing, Vol. 50, No. 1, p. 137-143, 2017.
[12] T. Lillesand, R.W. Kiefer and J. Chipman, Remote sensing and Image Interpretation., John wiley and Sons, 2015.
[13] D. Flanders, M. Hall-Beyer and J. Pereverzoff, "Preliminary evaluation of eCognition object-based software for cut block delineation and feature extraction.," Canadian Journal of Remote Sensing, Vol. 29, No. 4, p. 441–452, 2003.
[14] Y. He, X. Zhang and L. Hua, "Object-Based Distinction between Building Shadow and Water in High-Resolution Imagery Using Fuzzy-Rule Classification and Artificial Bee Colony Optimization.," Journal of Sensors, 2016.
[15] J. Schiewe, "Segmentation of high-resolution remotely sensed data— concepts, applications and problems.," International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences, Vol. 34, No. 4, p. 380-385, 2002.
[16] Hay, G. J. and G. Castilla, Geographic Object-Based Image Analysis (GEOBIA): A new name for a new discipline, Object-based image analysis. Berlin, Heidelberg: Springer, p. 75-89,2008
[17] S. Lang, Object-based image analysis for remote sensing applications: modeling reality–dealing with complexity. In Object-based image analysis. Blaschke, Heidelberg, Berlin: Springer, 2008, p. 3-27.
[18] A. Polychronaki and I. Z. Gitas, "Burned Area Mapping in Greece Using SPOT-4 HRVIR Images and Object-Based Image Analysis.," Remote Sensing, Vol. 4, No. 2, p. 424–438, 2012.
[19] B. Li, H. Zhang and F. Xu, " Water extraction in high resolution remote sensing image based on hierarchical spectrum and shape features," Earth and Environmental Science, Vol. 17, p. 012123, 2014.
[20] Rishikeshan, C. A., and H. Ramesh. "An automated mathematical morphology driven algorithm for water body extraction from remotely sensed image," ISPRS journal of photogrammetry and remote sensing, Vol. 146, p. 11-21, 2018.
[21] Gisgeography. Image classification techniques in remote sensing. http://gisgeography.com/image-classification techniques-remote-sensing/,2016.
[22] J. A. Barsi, K. Lee, G. Kvaran, B. L. Markham and J. A. Pedelty, "The Spectral Response of the Landsat-8 Operational Land Imager.," Remote Sensing, Vol. 6, p. 10232-10251, 2014.
[23] D. Roy, M.A. Wulder, T.R. Loveland, C.E. Woodcock, R.G. Allen, M.C. Anderson, D. Helder, J.R. Irons, D.M. Johnson, R. Kennedy, T.A. Scambos, C. Schaaf, J. Schott, Y. Sheng, E. Vermote, A. Belward, R. Bindschadler, W. Cohen, F. Gao, J. Hipple, P. Hostert, J. Huntington, C. Justice, A. Kilic, V. Kovalskyy, Z. Lee, L. Lymburner, J. Masek, J. McCorkel, Y. Shuai, R. Trezza, J. Vogelmann, R. Wynne and Z. Zhu, "Landsat-8: Science and product vision for terrestrial global change research.," Remote Sensing of Environment, Vol. 145, p. 154–172, 2014.
[24] J. Rouse, Haas, H. R., Schell, A. J. and Deerin, "Monitoring vegetation systems in the Great Plains with ERTS.," In Proceedings of the Third Earth Resources Technology Satellite Symposium, 1974.
[25] G. Kaplan and U. Avdan, "New mapping technique for water extraction using NDWI and land surface temperature water extraction surface temperature index.," In Manuscript submitted for publication., 2016. [26] Jin and Xiaoying, "Segmentation-based image processing system.," U.S. Patent No. 8,260,048., 2012.
[27] Version 5.3.1 ENVI [Computer software]. (2015).
[28] Y. Zhou, J. Dong, X. Xiao, T. Xiao, Z. Yang, G. Zhao, Z. Zou and Y. Qin, "Open Surface Water Mapping Algorithms: A Comparison of Water-Related Spectral Indices and Sensors.," Water, Vol. 9, No. 4, p. 256, 2017.
[29] S.K. McFeeters, " The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features.," International Journal of Remote Sensing, Vol. 17, No. 7, p. 1425–1432, 1996.
[30] Kalkan, K., and D. Maktav. "Comparison of Pixel based and Object based Classification Methods (IKONOS Example).” 3th remote sensing and geographic information system symposium, 2010.

APA	HAFIZI H, KALKAN K (2020). Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. , 87 - 89.
Chicago	HAFIZI Hamed,KALKAN Kaan Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. (2020): 87 - 89.
MLA	HAFIZI Hamed,KALKAN Kaan Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. , 2020, ss.87 - 89.
AMA	HAFIZI H,KALKAN K Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. . 2020; 87 - 89.
Vancouver	HAFIZI H,KALKAN K Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. . 2020; 87 - 89.
IEEE	HAFIZI H,KALKAN K "Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery." , ss.87 - 89, 2020.
ISNAD	HAFIZI, Hamed - KALKAN, Kaan. "Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery". (2020), 87-89.

APA	HAFIZI H, KALKAN K (2020). Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. Havacılık ve Uzay Teknolojileri Dergisi, 13(1), 87 - 89.
Chicago	HAFIZI Hamed,KALKAN Kaan Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. Havacılık ve Uzay Teknolojileri Dergisi 13, no.1 (2020): 87 - 89.
MLA	HAFIZI Hamed,KALKAN Kaan Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. Havacılık ve Uzay Teknolojileri Dergisi, vol.13, no.1, 2020, ss.87 - 89.
AMA	HAFIZI H,KALKAN K Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. Havacılık ve Uzay Teknolojileri Dergisi. 2020; 13(1): 87 - 89.
Vancouver	HAFIZI H,KALKAN K Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery. Havacılık ve Uzay Teknolojileri Dergisi. 2020; 13(1): 87 - 89.
IEEE	HAFIZI H,KALKAN K "Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery." Havacılık ve Uzay Teknolojileri Dergisi, 13, ss.87 - 89, 2020.
ISNAD	HAFIZI, Hamed - KALKAN, Kaan. "Evaluation of Object-Based Water Body Extraction Approaches Using Landsat-8 Imagery". Havacılık ve Uzay Teknolojileri Dergisi 13/1 (2020), 87-89.