Arazi Kullanımlarının Sınıflandırılmasında Piksel ve Obje Tabanlı Sınıflandırmanın Karşılaştırılması

Yüksek çözünürlüklü uydu görüntülerinin sınıflandırılmasıyla oluşturulan arazi örtüsü-arazi kullanımı (AÖ/AK) envanterleri, hem kent ölçeğinde hem de peyzaj ölçeğindeki planlama uygulamalarının yürütülmesi açısından önem taşımaktadır. Avrupa'da alan kullanım planlaması kapsamında hazırlanan kent atlasları, peyzaj ölçeğindeki Natura 2000 ve CORINE veri setlerine dayanan haritalar, uydu görüntülerinin yüksek doğrulukla sınıflandırılması ile oluşturulmaktadır. Sınıflandırma tekniklerinden kontrollü ve kontrolsüz sınıflandırmanın yanı sıra, güncel bir yöntem olan obje tabanlı sınıflama da etkin olarak kullanılmaktadır. Bu çalışmanın materyali olarak, 2013 yılına ait Aydın kentsel yerleşim merkezi sınırındaki 0.5 m yersel çözünürlüğe sahip Worldview Ortoready Pansharpened uydu görüntüsü ve görüntünün yorumlanmasında Google Earth Pro yazılımının geçmiş görüntüleri arasında aynı yılı kapsayan hava fotoğrafları kullanılmıştır. Kentsel alan kullanımlarını örnekleyecek düzeyde seçilen çalışma alanı, kontrollü ve obje tabanlı sınıflandırma tekniği ile sınıflandırılarak, sınıflandırma sonuçları karşılaştırmalı olarak değerlendirilmiştir. Doğruluk analizi sonuçlarına göre, kontrollü sınıflandırma için hesaplanan ortalama doğruluk değeri %82.79, obje tabanlı sınıflandırma için hesaplanan ortalama doğruluk değeri %92.52 olarak elde edilmiştir.

Anahtar Kelimeler:

piksel tabanlı sınıflandırma, obje tabanlı sınıflandırma, kentsel alan kullanımları

The Comparison of Pixel and Object-based Classification for Land Use/Land Cover Mapping

The classification of high resolution satellite images enable planners and researchers to classify land use/land cover (LULC), which are important in terms of carrying out the planning applications both at city and landscape scale. For conducting different landscape analysis, LULC plays a significant role for numerous applications, such as urban growth analysis, deforestation, etc. Urban atlases provided by European initiatives, Coordination of Information on the Environment (CORINE) and various thematic maps are based on the classification of high resolution satellite images. As a current method, object-based classification is also used effectively in remote sensing researches. The materials of this study are Worldview ortoready pansharpened satellite image with a local resolution of 0.5 m environs the urban settlement of Aydin dated 2013, and the satellite images belong to the same year acquired from Google Earth Pro software. The research area representing urban green space, barren land, road, building, agricultural field, and shadow was chosen to classify urban LULC by supervised classification and object based classification in a comparative way. The results of the accuracy analysis demonstrate that the object-based classifier achieved a high overall accuracy (92.52%), whereas the most commonly used decision rule, namely maximum likelihood classifier, produced a lower overall accuracy (82.79%). This research shows that the object-based classifier is a significantly better approach than the classical pixel classifiers.

Keywords:

supervised classification, object based classification, land use/land cover,

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Baatz M, Benz U, Dehghani S, Heynen M, Höltje A, Hofmann P, Lingenfelder I, Mimler M, Sohlbach, M, Weber M, Willhauck G (2004) eCognition Professional User Guide, Version 4.0. Definiens Imaging GmbH. München, Germany: Definiens.
Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heyen M (2004) Multi-resolution, Object-oriented Fuzzy Analysis of Remote Sensing Data for GIS-ready Information. ISPRS Journal of Photogrammetry & Remote Sensing 58: 239–58.
Blaschke T (2010) Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing 65(1): 2-16.
Blaschke T, Burnett C, Pekkarinen A (2004) Image segmentation methods for object-based analysis and classification. In Remote sensing image analysis: Including the spatial domain (pp. 211-236). Springer, Dordrecht.
Campbell JB (1996) Introduction to Remote Sensing. Guilford Press, New York.
Camps-Valls G, Tuia D, Bruzzone L, Benediktsson JA (2014) Advances in hyperspectral image classification: Earth monitoring with statistical learning methods. IEEE Signal Processing Magazine 31(1): 45-54.
Chen G, Hay GJ, Carvalho LM, Wulder MA (2012) Object-based change detection. International Journal of Remote Sensing 33(14): 4434-4457.
Cohen J (1960) A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement 20 (1): 37-46.
Congalton RG (1991) A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37(1): 35-46.
Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods. Cambridge University Press, 187s.
Dingle Robertson L, King DJ (2011) Comparison of pixel-and object-based classification in land cover change mapping. International Journal of Remote Sensing 32(6):1505-1529.
Duro DC, Franklin SE, Dubé MG (2012) A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sensing of Environment 118: 259-272.
Hussain M, Chen D, Cheng A, Wei H, Stanley D (2013) Change detection from remotely sensed images: From pixel-based to object-based approaches. ISPRS Journal of Photogrammetry and Remote Sensing 80: 91-106.
Jensen JR (2005) Introductory Digital Image Processing: A Remote Sensing Perspective. Pearson Prentice Hall, Upper Saddle River, NJ.
Kalkan K (2011) Kentsel Gelişim için Potansiyel Açık Alanların Belirlenmesinde Nesne Tabanlı Sınıflandırma Yöntemi ile Transfer Edilebilir Kural Dizisi Oluşturulması. Yüksek Lisans Tezi, İstanbul Teknik Üniversitesi Fen Bilimleri Enstitüsü, İstanbul.
Kalkan K, Maktav D (2010) Nesne Tabanlı ve Piksel Tabanlı Sınıflandırma Yöntemlerinin Karşılaştırılması (IKONOS Örneği). III. Uzaktan Algılama ve Coğrafi Bilgi Sistemleri Sempozyumu, 11 – 13 Ekim 2010, Gebze – Kocaeli.
Li W, Wu G, Zhang F, Du Q (2017) Hyperspectral image classification using deep pixel-pair features. IEEE Transactions on Geoscience and Remote Sensing 55(2): 844-853.
Maggiori E, Tarabalka Y, Charpiat G, Alliez P (2017) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing 55(2): 645-657.
Mather P M (1987) Computer Processing of Remotely Sensed Images: An Introduction, John Wiley & Sons Ltd., 360s.
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.
Platt R V, Rapoza L (2008) An Evaluation of an Object-Oriented Paradigm for Land Use/Land Cover Classification. The Professional Geographer 60 (1): 87-100.
Rahman MR, Saha SK (2008) Multi-Resolution Segmentation for Object-based Classification and Accuracy Assessment of Land Use/Land Cover Classification Using Remotely Sensed Data. Journal of the Indian Society of Remote Sensing 36(2): 189-201.
Ryherd S, Woodcock C (1996) Combining Spectral and Texture Data in the Segmentation of Remotely Sensed Images. Photogrammetric Engineering and Remote Sensing 62: 181-194.
Schowengerdt, RA (2012) Techniques for image processing and classifications in remote sensing. Academic Press, 249s.
Tehrany MS, Pradhan B, Jebuv MN (2014) A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery. Geocarto International 29(4): 351-369.
Tzotsos A, Argialas D (2008) Support vector machine classification for object-based image analysis. In Object-Based Image Analysis (pp. 663-677). Springer, Berlin, Heidelberg.
Ustuner M, Sanli FB, Dixon B (2015) Application of support vector machines for landuse classification using high-resolution RapidEye images: a sensitivity analysis. European Journal of Remote Sensing 48(1): 403-422.
Whiteside TG, Boggs GS, Maier SW (2011) Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation 13(6): 884-893.
Yu W, Zhou W, Qian Y, Yan J (2016) A new approach for land cover classification and change analysis: Integrating backdating and an object-based method. Remote Sensing of Environment 177: 37-47.
Zhou W, Huang, G, Troy A, Cadenasso ML (2009) Object-based land cover classification of shaded areas in high spatial resolution imagery of urban areas: A comparison study. Remote Sensing of Environment 113(8): 1769-1777.
Zhu Z, Woodcock CE (2012) Object-based cloud and cloud shadow detection in Landsat imagery. Remote Sensing of Environment 118: 83-94.