COMPARISON OF PIXEL AND OBJECT BASED CLASSIFICATION METHODS ON RAPIDEYE SATELLITE IMAGE

Yıl 2021, Cilt: 5 Sayı: 1, 1 - 11, 30.04.2021

Ebru Ersoy Tonyaloğlu Nurdan Erdogan Betül Çavdar Kübra Kurtşan Engin Nurlu

https://doi.org/10.32328/turkjforsci.741030

Cited By: 1

Öz

The aim of this study is to evaluate the classification performances of land use/land cover (LULC) classification methods by comparing the results of pixel and object-based classification approaches on RapidEye satellite image. Pixel-based classification was carried out in ERDAS Imagine 10.4 using the Maximum Likelihood-supervised approach, whilst object-based classification was performed in e-Cognition Developer 64 using the nearest neighbour-supervised classification method. A LULC map of eight classes was created in both methods. While the accuracy for thematic LULC classes varied in both methods, the overall accuracy and kappa values of LULC maps for pixel and object-based classification methods were 58.39%-0.45 and 89.58%-0.86, respectively. Accuracy assessments and comparative results showed that object-based classification gives better results for thematic LULC classes as well as the overall accuracy of LULC maps. Even though pixel-based classification method was good at mapping many thematic classes, there were misclassifications between natural/semi-natural LULC classes. These results can be attributed to parameters set by users, such as the number of control points, etc. However, the capacity of object-based classification method to include auxiliary data (e.g. DEM, NDVI) increases the accuracy of LULC maps with high-resolution satellites.

Anahtar Kelimeler

Pixel-based classification, Object-based classification, RapidEye, LULC

Destekleyen Kurum

TÜBİTAK

Proje Numarası

TÜBİTAK-No.116K829

Teşekkür

This research is supported by a collaborative research project under the Scientific and Technological Research Council of Turkey (TÜBİTAK-No.116K829). This research is presented at 2nd International Conference on Life and Engineering Sciences, Istanbul/TURKEY, June 27-29, 2019.

RAPIDEYE UYDU GÖRÜNTÜSÜ İLE PİKSEL TABANLI VE OBJE TABANLI SINIFLANDIRMALARIN KARŞILAŞTIRILMASI

Öz

Çalışmanın amacı, RapidEye uydu görüntüsü üzerinde piksel ve obje-tabanlı sınıflandırma yöntemleri karşılaştırarak, alan kullanım/arazi örtüsü sınıflandırma yöntemlerinin performanslarının peyzaj ve sınıf düzeyinde değerlendirilmesidir. Çalışmada, sınıflandırma yüksek çözünürlüklü RapidEye uydu görüntüsü kullanılarak ERDAS Imagine yazılımı kullanılarak piksel-tabanlı kontrollü sınıflandırma işlemi, e-Cognition yazılımı kullanılarak ise obje-tabanlı en yakın komşuluk kontrollü sınıflandırma işlemi uygulanmıştır. Her iki yöntemde de sınıflama, 8 AKAÖ sınıfına göre yapılmıştır. Tematik AKAÖ haritalarının sınıflandırma doğruluğu, her iki yöntemde farklılık gösterirken, piksel-tabanlı sınıflandırma yönteminin genel sınıflandırma doğruluğu %58.39 ve kappa değeri 0.45, obje-tabanlı sınıflandırma yönteminin genel sınıflandırma doğruluğu 89.58% ve kappa değeri 0.86 olarak hesaplanmıştır. Doğruluk analizleri ve sonuçların karşılaştırmalı değerlendirilmesi, obje-tabanlı sınıflandırma yönteminin AKAÖ haritalarının genel doğruluğunun yanı sıra tematik AKAÖ sınıfları için de daha iyi sonuçlar verdiğini göstermiştir. Piksel-tabanlı yöntem birçok tematik sınıfın eşlenmesinde sorun teşkil etmezken, doğal/yarı doğal AKAÖ sınıfları arasında hatalar ortaya çıkmıştır. Doğruluk oranlarında, kullanıcılar tarafından belirlenen kontrol alanı yer seçimi ve kontrol nokta sayısı gibi parametreler ile ilişkilendirilebilinir. Ancak, obje-tabanlı sınıflandırma yönteminde DEM, NDVI gibi yardımcı verilerin de sınıflandırmaya dahil edilebilmesi, yüksek çözünürlüklü uydu görüntüleri ile AKAÖ sınıflandırmada doğruluk oranını arttırmaktadır.

Anahtar Kelimeler

Piksel-tabanlı sınıflandırma, Obje-tabanlı sınıflandırma, RapidEye, AKAÖ

Proje Numarası

TÜBİTAK-No.116K829

Kaynakça

• Anderson, J.R., Hardy, E.E., Roach, J.T., & Witmer, R.E. (1976), A Land Use and Land Cover Classification System for Use with Remote Sensor Data, Geological Survey Professional Paper (Vol. 964), Washington: US Government Printing Office.
• Atak Kesgin, B. (2020), Kentsel peyzaj yapısındaki değişimlerin peyzaj metrikleri ile analizi, İzmir örneği, Ege Üniversitesi Ziraat Fakültesi Dergisi, 57(1), 119-128.
• Benz, U., Hofmann, P., Gregor Willhauck, G., Lingenfelder, I., & Heynen, M. (2004), Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information, ISPRS Journal of Photogrammetry and Remote Sensing, 58(3-4), 239-258.
• Blaschke, T. (2010), Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16.
• Blundell, J.S., & Opitz, D.W. (2006), Object recognition and feature extraction from imagery: The Feature Analyst® approach, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(4), C42.
• Cohen, J. (1960), A coefficient of agreement for nominal scales, Educational and Psychological Measurement, 20(1), 37-46.
• Congalton, R.G., & Green, K. (2008), Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, CRC Press Taylor & Francis Group.
• Dean, A.M., & Smith, G.M. (2003), An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities, International Journal of Remote Sensing, 24(14), 2905-2920.
• Elachi, C., & van Zyl, J.J. (2006), Introduction to the physics and techniques of remote sensing (Vol. 28), John Wiley & Sons.
• Gao, Y., & Mas, J.F. (2008), A comparison of the performance of pixel-based and object-based classifications over images with various spatial resolutions, Online Journal of Earth Sciences, 2(1), 27-35.
• Hay, G.J., & Castilla, G. (2006), Object-based image analysis: strengths, weaknesses, opportunities and threats (SWOT), In S. Lang, T. Blaschke & E. Schöpfer, Proceeding 1st International Conference on Object-Based Image Analysis (OBIA) (pp.1-3), Salzburg, Austria.
• Langanke, T., Blaschke, T., & Lang, S. (2004), An object-based GIS/remote sensing approach supporting monitoring tasks in European-wide nature conservation, In First Mediterranean Conference on Earth Observation (Remote Sensing) (pp. 245-252), Belgrade, Serbia.
• Lechner, A.M., Fletcher, A., Johansen, K., & Erskine, P. (2012), Characterising upland swamps using object-based classification methods and hyper-spatial resolution imagery derived from an unmanned aerial vehicle, In Proceedings of the XXII ISPRS Congress Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Vol: 4, pp. 101-106), Melbourne, Australia.
• Mathieu, R., Aryal, J., & Chong, A. (2007), Object-based classification of Ikonos imagery for mapping large-scale vegetation communities in urban areas, Sensors, 7(11), 2860-2880.
• McRoberts, R.E., & Tomppo, E.O. (2007), Remote sensing support for national forest inventories, Remote Sensing of Environment, 110(4), 412-419.
• Moosavi, V., Talebi, A., & Shirmohammadi, B. (2014), Producing a landslide inventory map using pixel-based and object-oriented approaches optimized by Taguchi method, Geomorphology, 204, 646-656.
• Myint, S.W., Gober, P., Brazel, A., Grossman-Clarke, S., & Weng, Q.H. (2011), Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery, Remote Sensing of Environment, 115 (5), 1145-1161.
• Oruc, M., Marangoz, A.M., & Buyuksalih, G. (2004), Comparison of pixel-based and object-oriented classification approaches using Landsat-7 ETM spectral bands, In Proceedings of the XX ISPRS Congress Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences (p. 5), Istanbul, Turkey.
• Pillai, R.B., Weisberg, P.J., & Lingua, E. (2005), Object-oriented classification of repeat aerial photography for quantifying woodland expansion in central Nevada, In 20th Biennial Workshop on Aerial Photography, Videography, and High Resolution Digital Imagery for Resource Assessment (pp. 2-6), Waslaco, TX.
• Richards, J.A. (1999), Remote Sensing Digital Image Analysis (Vol: 3, pp. 10-38), Berlin: Springer.
• Sabuncu, A., & Sunar, F. (2017), Ortofotolar ile nesne tabanlı görüntü sınıflandırma uygulaması: Van-Erciş depremi örneği, Doğal Afetler ve Çevre Dergisi, 1, 1-8.
• Sertel, E., & Alganci, U. (2016), Comparison of pixel and object-based classification for burned area mapping using SPOT-6 images, Geomatics, Natural Hazards and Risk, 7(4), 1198-1206.
• Turner, B.L., Clark, W.C., Kates, R.W., Richards, J.F., Mathews, J.T., & Meyer, W.B. (Eds.) (1990), The Earth as Transformed by Human Action: Global and Regional Changes in the Biosphere over the Past 300 Years, Cambridge University Press, with Clark University.
• Villarreal, N.R. (2016), Pixel-based and object-based classification methods for surveying wetland vegetation with an unmanned aerial system, (Master thesis, the Graduate Council of Texas State University, Texas State). Retrieved from https://digital.library.txstate.edu/bitstream/handle/10877/6347/VILLARREAL-THESIS-2016.pdf?sequence=1 on 15 May 2019.
• Weih, R.C., & Riggan, N.D. (2010), Object-based classification vs. pixel-based classification: comparative importance of multi-resolution imagery, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 38(4), C7.
• Xiaoliang, Z.O.U., Guihua, Z.H.A.O., Jonathan, L.I., Yuanxi, Y.A.N.G., & Yong, F.A.N.G. (2016), Object based image analysis combining high spatial resolution imagery and laser point clouds for urban land cover, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 41.
• Yan, G. (2003), Pixel based and object oriented image analysis for coal fire research, (Msc. Thesis, International Institute for Geo-information Science and Earth Observation). Retrieved from https://webapps.itc.utwente.nl/librarywww/papers_2003/msc/ereg/gao_yan.pdf on 18 May 2019.