Farklı sınıflandırma algoritmaları kullanarak sentinel-2 ve landsat 8 verileri sınıflandırması ve tematik doğruluk değerlendirmesi (Silifke ilçesi örneği)
Classification of sentinel-2 and landsat 8 data using different classification algorithms and thematic accuracy assessment (example of Silifke)
Yazar: MELİKE ŞEKER
Danışman: PROF. DR. NEBİYE MUSAOĞLU
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Geomatik Mühendisliği Ana Bilim Dalı / Geomatik Mühendisliği Bilim Dalı
Derece: Yüksek Lisans, 2021
Konu: Jeodezi ve Fotogrametri = Geodesy and Photogrammetry
Dizin: Arazi sınıflandırması = Land classification ; Görüntü sınıflandırma = Image classification

Özet:
Gelişen teknoloji ile birlikte uydu görüntüleri, kullanıcılara geniş spektral aralıkta birçok güncel veri sunarak uzaktan algılama çalışmalarında sıkça kullanılmaya başlanmıştır. Günümüzde yüksek çözünürlüklü uydu görüntülerine internet aracılığı ile ücretsiz olarak erişim imkanı sağlanılmaktadır. Bu sayede kullanıcılar tarafından kullanımı yaygınlaşan yüksek çözünürlüklü görüntülerden harita üretimi, kıyı şeridi gözlemi, afet yönetimi, su havzalarının yönetimi gibi birçok alanda sıkça faydalanılmaktadır. 
Bu tez çalışmasının amacı Landsat 8 OLI ve Sentinel- 2 uydu görüntülerini kullanarak üretilen arazi örtüsü (AÖ) / arazi kullanım (AK) haritalarının sınıflandırma doğruluklarını karşılaştırmaktır. Kullanılan uydu görüntülerinin sonuçlarının karşılaştırılabilmesi için mümkün olan en  yakın tarihli görüntüler seçilmiştir. Çalışma alanı olarak Silifke ilçesinin bir bölümü seçilerek 09.10.2020 tarihli Landsat 8 OLI ve 05.10.2020 tarihli Sentinel-2 verileri 30 m piksel boyutuna yeniden örneklenmiştir. Her iki uydu görüntüsünde de benzer spektral bantlar seçilerek benzer bir veri seti oluşturulması amaçlanmıştır.
Doğruluk analizi sonucu oluşan hata matrisi, tematik doğruluk analizi esnasında kullanılan temel etkendir. Hata matrisi elemanları ise sınıflandırılmış pikseller ve referans piksel değerlerinden oluşur. Sınıflandırılmış pikseller ve referans örneklemleri ne kadar doğru seçilirse o kadar kabul edilebilir bir hata matrisi elde edilir. Bu sebeple sınıflandırma işleminde ve referans veri olarak kullanılacak örneklemleri tespit etmek için uygulama öncesi arazi çalışması yapılarak hata matrisi kalitesi arttırılması hedeflenmiştir.
En Yüksek Olabilirlik (MLC), Destek Vektör Makinesi (SVM) ve Sinir Ağları (NN) kontrollü sınıflandırma yöntemleri her iki uydu görüntüsünde de uygulanarak AÖ/AK sınıfları tanımlanmıştır. Sınıflandırma işleminde 7 adet AÖ/AK sınıfı kullanılmıştır. Bunlar; ırmak sınıfı, kayalık-makilik alan, maden alanı, ormanlık alan, tarım alanı, kentsel yapı ve ulaşım ağı sınıflarıdır. Sınıflandırma sonrası aynı referans veriler kullanılarak hata matrisleri oluşturulmuştur. Hata matrisleri incelenerek 6 adet tematik doğruluk sonucu birbirleriyle kıyaslanmıştır. 
Bu çalışmada iki farklı uydu verisinin genel doğrulukları, AÖ/AK sınıfları doğrulukları ayrı ayrı incelenerek farklı sınıflandırma algoritmalarının avantaj ve dezavantajları ortaya konulmaya çalışılmıştır. Bu amaçla tematik doğruluk değerlendirmesi sonucu oluşan hata matrisi incelenerek birbirleri ile karışan AÖ/AK sınıfları tespit edilmiştir. Karşılaştırma sonucunda Landsat 8 ve Sentinel-2 uydu görüntüsü için  literatür çalışmalarından farklı olarak En Yüksek  Olabilirlik sınıflandırması diğer sınıflandırma algoritmalarına kıyasla yaklaşık %3  fark ile daha yüksek bir genel doğruluk sonucu göstermektedir. 
Landsat 8 ve Sentinel-2 verileri tüm sınıflandırma algoritmaları kendi aralarında kıyaslanacak olunursa %90,2 genel doğruluk değeri ile Sentinel-2 Maksimum Olabilirlik yöntemi tüm yöntemler içinde en başarılı sonucu göstermektedir. Sinir Ağları ve Destek Vektör Makineleri yöntemleri ise her iki uydu verisi için de birbirine benzer sonuçlar göstermektedir. Landsat 8 verisi için Sinir Ağları ve Destek Vektör Makineleri yöntemleri %84,5 genel doğruluk değerleri tüm sınıflandırma yöntemleri içinde en başarısız sonucu göstermektedir. 
En Yüksek Olabilirlik algoritması bazı AÖ/AK sınıflarını başarılı bir şekilde sınflandırırken bazı sınıfların atanmasında ise başarısız sonuçlar göstermektedir. Landsat 8 ve Sentinel-2 uydu görüntüleri tematik doğrulukları kendi aralarında kıyaslanırsa en başarılı sonuç Sentinel-2 verisinden elde edilen sınıflandırma sonuçlarıdır.

Abstract:
Along with the developing technology, satellite images have started to be used frequently in remote sensing studies by providing users with many updated data in a wide spectral range. Apart from satellite images, aerial photographs obtained by photogrammetric methods are frequently used in remote sensing studies. Today, free access to high-resolution satellite images is provided via the internet. In this way, high-resolution satellite images, which are widely used by users, are frequently used in many areas such as map production, coastline observation, disaster management and water basin management. 
The aim of this thesis study is to compare the classification accuracies of land cover (LC) / land use (LU) maps produced using Landsat 8 OLI and Sentinel-2 satellite images. In order to compare the results of the satellite images used, the most recent images were selected as a possible. A part of Silifke district was selected as the study area and Landsat 8 dated 09.10.2020 and Sentinel-2 data dated 05.10.2020 were resampled to 30 m pixel size. It is aimed to create a similar data set by selecting similar spectral bands in both satellite images. There are many resampling methods available. In this study, nearest neighbour resampling method was used in the resampling phase. This is because the method used does not cause any changes in the output image, as they are copied directly from the original image. Thus, a change or error in the classification process due to the resampling stage will not occur. During the data selection, October data were preferred. The reason for this is that the meteorological conditions of the district in October are suitable and the satellite. In this way, there was no need for any atmospheric correction. The reasons for preferring Silifke district as a working area are; It has the desired land classes with LC / LU diversity, meteorological conditions are appropriate in terms of remote sensing data, and the user is familiar with the LC / LU conditions of the district.
The error matrix formed as a result of the accuracy analysis is the main factor used during thematic accuracy analysis. Error matrix elements consist of classified pixels and reference pixel values. The more accurately classified pixels and reference samples are chosen, the more acceptable an error matrix is obtained. For this reason, the training and reference data used in the study directly affect the thematic accuracy values. For this reason, it is aimed to increase the quality of the error matrix by conducting a field study before the application to determine the samples to be used as reference data in the classification process.
There are many integrated AÖ / AK classes within the boundaries of the study area.  For the purpose of the study, some LC / LU classes have been integrated with each other. For example, the greenhouses in the study area have been integrated into the urban structure class. The aim of the field study is to identify the AÖ / AK classes on the field and to minimize user errors during the classification phase. For this purpose, photographs containing coordinate information were taken within the boundaries of the study area and samples were collected. Field study was conducted on 10-11-12-13 November. The field study should be concurrent with the satellite acquisition of Landsat 8 and Sentinel-2 images. Because the change in AÖ / AK classes determined in the study area negatively affects the accuracy of the study.
There are some factors to consider when collecting samples during the field study. It was paid attention that the collected samples were homogeneously distributed in the study area. Samples were collected from each LC / LU classes in proportion to the size they occupy in the study area. In addition, more samples were collected in order to show statistically more successful results from LC / LU classes that might interfere with each other. For example, within the boundaries of the study area, the rocky-maquis area class covers more areas than the mining area class. For example, within the boundaries of the study area, the rocky scrub area class covers more areas than the mining area class. For this reason, while 50 training and test data were used for the rocky-maquis area class, 15 training and test data were sufficient for the mining area class.
LC / LU classes were defined by applying the methods of Maximum Likelihood, Support Vector Machine and Neural Networks supervised classification methods in both satellite images. Seven LC / LU classes were used for the classification process. These are river class, rocky-maquis area, mining area, forest area, agricultural area, urban structure and transportation network classes. Error matrices were created using the same reference data after classification process. Error matrices were examined and six thematic accuracy results were compared with each other.
In this study, the general accuracies of two different satellite data, the accuracies of LC / LU classes were examined separately and the advantages and disadvantages of different classsification algorithms were compared. For this purpose, the error matrix as a result of thematic accuracy assessment was examined and LC / LU classes that were mixed with each other were determined. Unlike the literature studies for Landsat 8 and Sentinel-2 satellite imagery, the Maximum Likelihood classification shows a higher overall accuracy result with a difference of approximately %3 compared to other classification algorithms. There may be many reasons for this difference. Many examples can be given to these, such as the physical characteristics of the study area, the user experience, the variety of LC / LU classes in the study area, and the integration of some of these classes into a single class in accordance with the study purpose (for example, the greenhouses in the study area are integrated into the urban building class).
If we compare all the classification algorithms of Landsat 8 and Sentinel-2 among themselves, Sentinel-2 Maximum Likelihood method shows the most successful result among all methods with an overall accuracy of  90.2% . Neural Networks and Support Vector Machines methods show similar results for both satellite data. Neural Networks and Support Vector Machines methods for Landsat 8 data  show 84.5 % general accuracy values, showing the most unsuccessful result amon all classification methods.
However, it is not correct to comment on the classification algorithm, considering only the general accuracy values. Producer and user accuracies of all LC / LU classes should also be examined, and the success of the classification algorithm should also be examined in this respect. In this study, the Maximum Likelihood classification shows the highest general accuracy value, but while this algorithm successfully classifies some LC / LU classes, it shows unsuccessful results in assigning some classes. If the thematic accuracies of Landsat 8 and Sentinel-2 satellite images are compared among themselves, the most successful results are the thematic accuracy results obtained from Sentinel-2 data. 
When classifying satellite images, besides spectral features, texture components are also used as data sets. Thus, it is aimed to obtain higher thematic accuracy values. Average, variance, entropy, homogeneity, contrast, difference and correlation values can be given as examples to the tissue components used. In addition to the study, texture components were formed by using variance and entropy values. The generated texture components were combined with satellite images bands and they were reclassified using Maximum Likelihood, Neural Networks and Support Vector Machines classification methods in both satellite images. The effects of texture components on classification accuracy were examined for all three classification methods. For thematic images containing texture components, Support Vector Machines classification shows a higher overall accuracy result compared to other classification algorithms, as in the literature studies in both satellite images. 
For thematic images containing texture components, Maximum Likelihood classification shows a lower overall accuracy result compared to other two classification algorithms in Landsat 8 satellite images. For Sentinel-2 satellite images, Neurol Network classification shows a lower overall accuracy result compared to other two classification algorithms. 
In thematic images containing texture components, if Landsat 8 and Sentinel-2 data are compared among all classification algorithms, the Sentinel-2 Support Vector Machines method shows the most successful result among all methods with an overall accuracy of 92.83%. In thematic images containing texture components, the Maximum Likelihood method 81.89% general accuracy values for Landsat 8 data show the most unsuccessful result among all classification methods.
In this way, the effect of texture components on thematic accuracy values was examined. Texture components created by using variance and entropy values, Maximum Likelihood classification decreases the general accuracy values, while Support Vector Machines classification increases the wide accuracy values. In the general accuracy results of Neural Networks classification, it was determined that texture components did not have much effect on thematic accuracy values.