Farklı Piksel Tabanlı Sınıflandırma Yöntemlerinin Arazi Kullanımı ve Arazi Örtüsü Belirlemedeki Performansının İncelenmesi

Yıl 2021, , 1 - 6, 15.06.2021

Nizar Polat , Yunus Kaya

https://doi.org/10.51534/tiha.829656

Cited By: 9

Öz

Fotogrametri ve uzaktan algılama tekniklerinin gelişmesiyle birlikte veri toplama daha kolay hale gelmiştir. Ancak toplanan verilerin büyük olması nedeniyle, veri setinden anlamlı veriler çıkarmak son zamanlarda popüler bir araştırma konusu haline gelmiştir. Günümüzde dijital görüntü işleme tekniklerinin geliştirilmesi, arazi örtüsü arazi kullanımının (LCLU) dijital görüntülerle belirlenmesine katkıda bulunmuştur. Bu çalışmada, bir kampüs alanındaki farklı arazi nesne sınıflarını ayırt etmek için ortofoto görüntü üzerinden denetimli sınıflandırma yapılmıştır. Çalışmanın amacı, en popüler denetimli sınıflandırma yöntemlerinden Maksimum Olabilirlik (Maximum Likelihood), Minimum Mesafe (Minimum Distance) ve Mahalanobis Uzaklık (Mahalanobis Distance) sınıflandırma tekniğinin performansını incelemektir. Çalışmada, bir karışıklık matrisi (confusion matrix) oluşturulmuş ve manuel olarak oluşturulan kesin referans verileri ile genel doğruluk ve genel kappa değerleri hesaplanmıştır. Sonuçlara göre, en yüksek genel doğruluk %84,5 oranı ile Maksimum Olabilirlik sınıflandırmasında elde edilmiştir. Minimum Mesafe yöntemi ise en düşük genel doğruluğa (%43) sahiptir. Araştırma, spektral bilgi eksikliğinden dolayı denetimli sınıflandırma yöntemlerinin atlama ve atama hataları (omission and commission) gösterdiğini göstermektedir. Bu durum, genel doğruluk hesaplaması üzerinde doğrudan bir etkiye sahiptir.

Anahtar Kelimeler

Fotogrametri, Arazi örtüsü arazi kullanımı, Denetimli sınıflandırma, İnsansız hava araçları

Investigation of the Performance of Different Pixel-Based Classification Methods in Land Use/Land Cover (LULC) Determination

Öz

With the development of photogrammetry and remote sensing techniques, data collection has become easier. However, due to the large size of the data collected, extracting meaningful data from the data set has become a popular topic. Nowadays, the development of digital image processing techniques has contributed to the determination of land cover land use (LCLU) through digital images. In this study, a supervised classification was made over the orthophoto view to distinguish different land object classes in a campus area. The purpose of the study is to examine the performance of the three popular supervised classification techniques that are maximum likelihood, minimum distance, and mahalanobis distance methods. In the study, a confusion matrix was produced, and overall accuracy and overall kappa were calculated with manually generated ground truth data. According to results, the highest overall accuracy was calculated for maximum likelihood classification with a rate of 84.5 % and the minimum distance method has the lowest overall accuracy (43%). The research denotes that due to the lack of spectral information the supervised classification methods generate omission and commission errors. This fact has a direct effect on overall accuracy calculation.

Anahtar Kelimeler

Photogrammetry, Land cover land use, Supervised classification, Unmanned Aerial Vehicles

Kaynakça

• Ahmed A, Muaz M, Ali M, Yasir M, Ullah S & Khan S (2015). Mahalanobis distance and maximum likelihood-based classification for identifying tobacco in Pakistan. 7th International Conference on Recent Advances in Space Technologies (RAST), 255-260.
• Al-Ahmadi F S & Hames A S (2009). Comparison of four classification methods to extract land use and land cover from raw satellite images for some remote arid areas, Kingdom of Saudi Arabia. Earth, 20(1), 167-191.
• Asad M H & Bais A (2019). Weed detection in canola fields using maximum likelihood classification and deep convolutional neural network. Information Processing in Agriculture. DOI: https://doi.org/10.1016/j.inpa.2019.12.002.
• Brooke C & Clutterbuck B (2020). Mapping heterogeneous buried archaeological features using multisensor data from unmanned aerial vehicles. Remote Sensing, 12(1), 41. DOI: https://doi.org/10.3390/rs12010041.
• Comert R, Avdan U, Gorum T & Nefeslioglu H A (2019). Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data. Engineering Geology, 260, 105264. DOI: https://doi.org/10.1016/j.enggeo.2019.105264.
• De Maesschalck R, Jouan-Rimbaud D & Massart D L (2000). The mahalanobis distance. Chemometrics and intelligent laboratory systems, 50(1), 1-18.
• De Oliveira Duarte D C, Zanetti J, Junior J G & das Graças Medeiros N (2018). Comparison of supervised classification methods of Maximum Likelihood, Minimum Distance, Parallelepiped and Neural Network in images of Unmanned Air Vehicle (UAV) in Viçosa-MG. Revista Brasileira de Cartografia, 70(2), 437-452.
• Erbek F S, Özkan C & Taberner M (2003). Comparison of maximum likelihood classification method with supervised artificial neural network algorithms for land use activities. International Journal of Remote Sensing, 25, 1733-1748.
• Foody G M, Campbell N A, Trodd N M & Wood T F (1992). Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric engineering and remote sensing, 58(9), 1335-1341.
• Galeano P, Joseph E & Lillo R E (2015). The Mahalanobis distance for functional data with applications to classification. Technometrics, 57(2), 281-291.
• Gemperline P J & Boyer N R (1995). Classification of near-infrared spectra using wavelength distances: comparison to the Mahalanobis distance and residual variance methods. Analytical Chemistry, 67(1), 160-166.
• Gholami A, Esfadiari M & Masihabadi M H (2010). The Survey and the Comparison of Maximum Likelihood, Mahalanobis Distance and Minimum Distance Methods in Preparing Landuse Map in the Western Part of Isfahan Province. International Journal of Geological and Environmental Engineering, 4(4), 118-121.
• Hassan F M, Lim H S & Jafri M M (2011). Cropcam UAV for land use/land cover mapping over Penang island, Malaysia. Pertanika Journal of Science & Technology, 19(S), 69-76. ISSN: 0128-7680.
• Huynh H T, & Nguyen L (2020). Nonparametric maximum likelihood estimation using neural networks. Pattern Recognition Letters, 138, 580-586. https://doi.org/10.1016/j.patrec.2020.09.006
• Kavzoğlu T & Çölkesen İ (2010). Karar Ağaçları ile Uydu Görüntülerinin Sınıflandırılması: Kocaeli Örneği Electronic Journal of Map Technologies, 2(1), 447-454.
• Kaya Y, Şenol H İ, Memduhoğlu A, Akça Ş, Ulukavak M & Polat N (2019). Hacim Hesaplarında İHA Kullanımı: Osmanbey Kampüsü Örneği. Türkiye Fotogrametri Dergisi, 1(1), 7-10.
• Kaya Y & Polat N (2019). Buılding Modeling by UAV Images. 1. International Conference of Virtual Reality, 113-117, Şanlıurfa, Turkey.
• Kim K L & Ryu J H (2020). Generation of Large-scale Map of Surface Sedimentary Facies in Intertidal Zone by Using UAV Data and Object-based Image Analysis (OBIA). Korean Journal of Remote Sensing, 36(2_2), 277-292. DOI: https://doi.org/10.7780/kjrs.2020.36.2.2.5.
• Kranz H G (1993). Diagnosis of partial discharge signals using neural networks and minimum distance classification. IEEE transactions on electrical insulation, 28(6), 1016-1024.
• Liang S, Cheng J & Zhang J (2020). Maximum Likelihood Classification of Soil Remote Sensing Image Based on Deep Learning. Earth Sciences Research Journal, 24(3). https://doi.org/10.15446/esrj.v24n3.89750
• Louargant M, Villette S, Jones G, Vigneau N, Paoli J N & Gée C (2017). Weed detection by UAV: simulation of the impact of spectral mixing in multispectral images. Precision Agriculture, 18(6), 932-951. DOI: https://doi.org/10.1007/s11119-017-9528-3.
• Mark H & Workman J (2010). Chemometrics in spectroscopy. Elsevier. ISBN: 978-0-12-374024-3.
• Mei J, Liu M, Wang Y F & Gao H (2015). Learning a mahalanobis distance-based dynamic time warping measure for multivariate time series classification. IEEE transactions on Cybernetics, 46(6), 1363-1374.
• Milas A S, Arend K, Mayer C, Simonson M A & Mackey S (2017). Different colours of shadows: classification of UAV images. International Journal of Remote Sensing, 38(8-10), 3084-3100. DOI: https://doi.org/10.1080/01431161.2016.1274449.
• Moraes J C T B, Seixas M O, Vilani F N & Costa E V. (2002). A real time QRS complex classification method using Mahalanobis distance. In Computers in Cardiology, 201-204, Memphis, USA.
• Otukei J R & Blaschke T (2010). Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms. International Journal of Applied Earth Observation and Geoinformation, 12, 27-31.
• Paola J D & Schowengerdt R A (1995). A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification. IEEE Transactions on Geoscience and remote sensing, 33(4), 981-996.
• Paola J D (1994). Neural Network Classification of Multispectral Imagery, Master’s Thesis, University of Arizona, USA.
• Richards J A & Richards J A (1999). Remote sensing digital image analysis, 3, 10-38. Springer: Berlin. DOI: https://doi.org/10.1007/978-3-642-30062-2, ISBN: 978-3-642-30062-2.
• Sathya P & Deepa V B (2017). Analysis of supervised image classification method for satellite images. International Journal of Computer Science Research (IJCSR), 5(2), 16-19.
• Şenol H İ, Memduhoğlu A, Ulukavak M, Çetin B & Polat N (2019). Lazer Tarayıcı ve İnsansız Hava Aracı Kullanılarak Kızılkoyun Kral Kaya Mezarlarının 3 Boyutlu Belgelenmesi.
• Srivastava D (2006). Making or breaking the heart: from lineage determination to morphogenesis. Cell, 126(6), 1037-1048.
• Strahler A H (1980). The use of prior probabilities in maximum likelihood classification of remotely sensed data. Remote sensing of Environment, 10(2), 135-163.
• Taddia Y, Russo P, Lovo S & Pellegrinelli A (2020). Multispectral UAV monitoring of submerged seaweed in shallow water. Applied Geomatics, 12(1), 19-34.
• Turner D, Lucieer A & Watson C (2012). An automated technique for generating georectified mosaics from ultra-high resolution unmanned aerial vehicle (UAV) imagery, based on structure from motion (SfM) point clouds. Remote sensing, 4(5), 1392-1410.DOI: https://doi.org/10.3390/rs4051392.
• Ulukavak M, Memduhoğlu A, Şenol H İ & Polat N (2019). The use of UAV and photogrammetry in digital documentation. Mersin Photogrammetry Journal, 1(1), 17-22.
• Ulvi A, Yakar M, Yiğit A Y & Kaya Y (2020). İHA ve Yersel Fotogrametrik Teknikler Kullanarak Aksaray Kızıl Kilise’nin 3 Boyutlu Nokta Bulutu ve Modelinin Üretilmesi. Geomatik Dergisi, 5(1), 22-30.
• Villaseñor C (2019). Hyperellipsoidal Neural Network Trained with Extended Kalman Filter for Forecasting of Time Series. Artificial Neural Networks for Engineering Applications, 9-19, Academic Press. DOI: https://doi.org/10.1016/B978-0-12-818247-5.00011-3.
• Woo H, Acuna M, Cho S, Jung G, Kim B, Ryu J, Woo C & Park J (2020). Application of Spectral Angle Mapping and Maximum Likelihood Classification Techniques to Evaluate Forest Fire Severity from UAV Multispectral Images in South Korea. Preprints.
• Yadav P K, Thomasson J A, Enciso J, Samanta S & Shrestha A (2019). Assessment of different image
• Kabadayı A, Kaya Y & Yiğit A Y (2020). Comparison Of Documentation Cultural Artifacts Using The 3d Model In Different Software. Mersin Photogrammetry Journal, 2(2), 51-58.
• Yiğit A Y, Orhan O & Ulvi̇ A (2020). Investigation of The Rainwater Harvesting Potential at The Mersin University, Turkey. Mersin Photogrammetry Journal, 2(2), 64-75.
• Şasi A & Yakar M (2018). Photogrammetric modelling of hasbey dar'ülhuffaz (masjid) Using an unmanned aerial vehicle, International Journal of Engineering and Geosciences (IJEG), 3(1), DOI: 10.26833/ijeg.328919
• Kaya Y & Yiğit A Y (2020). Dijital El Kameraları Kullanılarak Kültürel Mirasın Belgelenmesi. Türkiye Fotogrametri Dergisi, 2(2), 33-38.
• Yiğit A Y & Uysal M (2020). Automatic Road Detection from Orthophoto Images. Mersin Photogrammetry Journal, 2(1), 10-17.
• Yiğit A Y & Uysal M (2019). Nesne Tabanlı Sınıflandırma Yaklaşımı Kullanılarak Yolların Tespiti. Türkiye Fotogrametri Dergisi, 1(1), 17-24.
• Ulvi A, Yakar M, Yiğit A & Kaya Y (2019). The Use of Photogrammetric Techniques in Documenting Cultural Heritage: The Example of Aksaray Selime Sultan Tomb. Universal Journal of Engineering Science, 7(3), 64-73.
• Sarı B, Hamal S N G & Ulvi A (2020). Documentation of complex structure using Unmanned Aerial Vehicle (UAV) photogrammetry method and Terrestrial Laser Scanner (TLS). Türkiye Lidar Dergisi, 2(2), 48-54.