Tarımsal alanlarda yüksek çözünürlüklü IKONOS uydu görüntüsünden nesne-tabanlı ürün deseni tespiti

Öz

Günümüzde uzaktan algılama teknolojisi ve görüntü işleme tekniklerin gelişmesiyle birlikte, uydu görüntüleri tarımsal alanlarda ürün deseninin belirlenmesi çalışmalarında sıklıkla tercih edilir hâle gelmiştir. Bu çalışmada, yüksek konumsal çözünürlüklü IKONOS uydu görüntüsünden tarımsal alanlarda nesne-tabanlı sınıflandırma yöntemi ile ürün desenin belirlenmesi hedeflenmiştir. Çalışma alanı, Marmara Bölgesi’nde bulunan Bursa ili, Karacabey ilçesinin güneybatısında yer almakta ve yaklaşık 9×9 km²’lik bir alanı kapsamaktadır. Domates, mısır, biber, buğday, pirinç ve şeker pancarı bölgede yetiştirilen başlıca tarım ürünleridir. Çalışmada, IKONOS uydu görüntüsü çoklu-çözünürlük segmentasyon tekniği ile segmente edilmiştir. Segmentasyon işleminde gerekli parametrelerden en önemlisi olan ölçek parametresi için en uygun değer ESP-2 (Estimation of Scale Parameter) yazılımı ile belirlenmiştir. Diğer segmentasyon parametreleri olan şekil ve bütünlük parametreleri için en uygun değerler ise, yapılan deneme analizleri neticesinde tespit edilmiştir. Sınıflandırmanın doğruluğunu artırmak için, görüntünün orijinal bantlarına ek olarak, normalize edilmiş bitki indeksi (NDVI) bantı ile homojenlik, zıtlık, farklılık, ortalama, varyans ve entropi doku bantları kullanılmıştır. Sınıflandırma işlemi, toplam 29 bantlı veri seti kullanılarak eCognition yazılımında nesne-tabanlı en yakın komşuluk tekniği ile yapılmıştır. Elde edilen sınıflandırma sonucu, 2212 adet yer gerçeği verisi kullanılarak değerlendirilmiştir. Doğruluk analizleri neticesinde, sınıflandırmanın genel doğruluğu %87.5 olarak hesaplanmıştır. Elde edilen sonuçlar, yüksek çözünürlüklü IKONOS uydu görüntüsünden tarımsal ürün deseni tespitinin nesne-tabanlı sınıflandırma yöntemiyle yüksek doğrulukta belirlenebildiğini göstermektedir.

Anahtar Kelimeler

• Segmentasyon,Nesne-tabanlı sınıflandırma,Doku analizi

Object-based crop pattern detection from IKONOS satellite images in agricultural areas

Abstract

Nowadays, with the development of remote sensing technologies and image processing methods, satellite images have become frequently preferred in studies to determine the crop pattern in agricultural areas. In this study, it is aimed to detection the crop pattern in agricultural areas with high accuracy by using object-based classification technique from high spatial resolution IKONOS satellite images. The study area is located on the South-west of the Karacabey district of the Bursa province in the Marmara Region and covers an area of nearly 9×9 km². Tomato, corn, pepper, wheat, rice and sugar beet are the main products grown in the region. In this study, the IKONOS satellite image is segmented using multi-resolution segmentation technique. The most appropriate value for the scale parameter, which is the most important parameter in the segmentation process, has been determined by ESP-2 (Estimation of Scale Parameter) software. Various combinations have been tried for shape and compactness parameters in order to find the optimal segmentation parameters. In order to increase classification accuracy, normalized difference vegetation index (NDVI) and GLCM texture measurement methods have been used, including homogeneity, contrast, dissimilarity, mean, variance, and entropy. Using the data set from consist 29 bands, the image classification process have been performed using the object-based nearest neighbor classification technique in the eCognition software. The obtained classification results have been tested on parcel basis using 2212 ground truth data. The overall accuracy of the classification has been calculated as 87.5%. The results show that the high spatial resolution IKONOS satellite image can be used to detection high accuracy with object-based classification of agricultural crop pattern.

Keywords

• Segmentation,Object-based classification,Texture analysis

Kaynakça

1. Shanahan J, Schepers J, Francis D, Varvel G, Wilhelm W, Tringe J, Major D. “Use of remote-sensing imagery to estimate corn grain yield”. Agronomy Journal, 93(3), 583-589, 2011.
2. Conrad C, Fritsch S, Zeidler J, Rücker G, Dech S. “Per-field irrigated crop classification in arid Central Asia using SPOT and ASTER data”. Remote Sensing, 2(4), 1035-1056, 2010.
3. Antunes A, Lingnau C, Centeno J. “Object oriented analysis and semantic network for high resolution image classification”. Anais XI SBSR, Belo Horizonte, Brazil, 05-10 April 2003.
4. Blaschke T. “Object based image analysis for remote sensing”. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1), 2-16, 2010.
5. Lu D, Weng Q. “A survey of image classification methods and techniques for improving classification performance”. International Journal of Remote Sensing, 28(5), 823-870, 2007.
6. Mialhea F, Gunnell Y, Ignacio J, Delbart N, Ogania J, Henry S. “Monitoring land-use change by combining participatory land-use maps with standard remote sensing techniques: Showcase from a remote forest catchment on Mindanao, Philippines”. International Journal of Applied Earth Observation and Geoinformation, 36, 69-82, 2015.
7. Kim M, Madden M, Warner T. Estimation of Optimal Image Object Size for the Segmentation of Forest Stands with Multispectral IKONOS Imagery. Editors: Blaschke T, Lang S, Hay G. Object-Based Image Analysis, 291-307, Berlin, Heidelberg, Springer-Verlag, 2008.
8. Bock M, Xofis P, Mitchley J, Rossner G, Wissen M. “Object-oriented methods for habitat mapping at multiple scales– Case studies from Northern Germany and Wye Downs, UK”. Journal for Nature Conservation, 13(2), 75-89, 2005.

9. Yu Q, Gong P, Clinton N, Biging, G, Kelly M. “Object-based detailed vegetation classification with airborne high spatial resolution remote sensing imagery”. Photogrammetric Engineering and Remote Sensing, 72(7), 799-811, 2006.
10. Türker M, Özdarıcı A. “Field-based crop classification using SPOT 4, SPOT 5, IKONOS and QuickBird imagery for agricultural areas: a comparison study”. International Journal of Remote Sensing, 32(24), 9735-9768, 2011.
11. Pena-Barragan JM, Ngugi MK, Plant RE, Six J. “Object-based crop identification using multiple vegetation indices, textural features and crop phenology”. Remote Sensing of Environment, 115(6), 1301-1306, 2011.
12. Koutsias N, Mallinis GG, Pleniou M, Voukelatou I, Paschali T, Dimopoulos P. “Object-based classification using a synergy of high spatial (IKONOS) and high spectral (ASTER) satellite data in a rural NATURA 2000 deltaic area”. Proceedings of the 2nd International Conference on Space Technology, Athens, Greece, 15-17 September 2011.
13. Algancı U, Sertel, E, Özdogan M, Örmeci C. “Parcel-level identification of crop types using different classification algorithms and multi-resolution imagery in Southeastern Turkey”. Photogrammetric Engineering and Remote Sensing, 79(11), 1053-1065, 2013.
14. Li M, Ma L, Blaschke T, Cheng L, Tiede D. “A systematic comparison of different object-based classification techniques using high spatial resolution imagery in agricultural environments”. International Journal of Applied Earth Observation and Geoinformation, 49, 87-98, 2016.
15. Singha M, Wu B, Zhang M. “An object-based paddy rice classification using multi-spectral data and crop phenology in Assam, Northeast India”. Remote Sensing, 8(6), 479, 2016.
16. Zhang H, Li Q, Liu J, Du X, Dong T, McNairn H, Shang J. “Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier”. Geocarto International, 33(10), 1-19, 2017.
17. Haralick RM, Shanmugam K, Dinstein I. “Textural features for image classification”. IEEE Transactions on Systems, 3(6), 610-621, 1973.
18. Smith, A. “Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm”. Journal of Spatial Science, 55(1), 69-79, 2010.
19. Baatz M, Schape A. Multiresolution segmentation: an optimization approach for high quality multi-scale image segmentation. Editors: Strobl J, Blaschke T, Griesebner G. Angewandte Geographische Informations-Verarbeitung XII, 12-23, Karlsruhe, Germany, Wichmann, 2000.
20. Benz UC, Hofmann P, Willhauck G, Lingenfelder I, Heynen M. “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, 2004.
21. Myint SW, Gober P, Brazel A, Grossman-Clarke S, Weng Q. “Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery”. Remote Sensing of Environment, 115(5), 1145-1161, 2011.
22. Kim M, Warner TA, Madden M, Atkinson DS. “Multi-scale GEOBIA with very high spatial resolution digital aerial imagery: scale, texture and image objects”. International Journal of Remote Sensing, 32(10), 2828-2850, 2011.
23. Smith GM, Morton RW. “Real world objects in GEOBIA through the exploitation of existing digital cartography and image segmentation”. Photogrammetric Engineering and Remote Sensing, 76(2), 163-171, 2010.
24. Liu Y, Bian L, Meng Y, Wang H, Zhang S, Yang Y, Shao X, Wang B. “Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis”. ISPRS Journal of Photogrammetry and Remote Sensing, 68, 144-156, 2012.
25. Drăgut L, Csillik O, Eisank C, Tiede D. “Automated parameterisation for multi-scale image segmentation on multiple layers”. ISPRS Journal of Photogrammetry and Remote Sensing, 26(4), 119-127, 2010.
26. D’Oleire-Oltmanns S, Tiede D. “Application of the estimation of scale parameter 2 (ESP-2) tool for the identification of scale levels for distinct target objects”. South‐Eastern European Journal of Earth Observation and Geomatics, 3(2), 597-584, 2014.
27. O’Connell J, Bradter U, Benton T. “Wide-area mapping of small-scale features in agricultural landscapes using airborne remote sensing”. ISPRS Journal of Photogrammetry and Remote Sensing, 109, 165-177, 2015.
28. Drăgut L, Tiede D, Levick SR. “ESP: a tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data”. International Journal of Geographical Information Science, 24(6), 859-871, 2014.
29. Kavzoglu T, Yıldız M. “Parameter-Based performance analysis of object-based image analysis using aerial and QuickBird-2 images”. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 11(7), 31-37, 2014.
30. Mather P. Computer Processing of Remotely-Sensed Images: An Introduction. 3rd ed. New York, USA, Wiley, 2004.
31. Cohen J. “A coefficient of agreement for nominal scales”. Educational and Psychological Measurement, 20(1), 37-46, 1960.