Google Earth Engine Üzerinde Sentinel-2 Görüntülerinin Arazi Örtüsü Haritalama Doğruluğunun İyileştirilmesi

Year 2022, , 150 - 159, 18.09.2022

Çiğdem Şerifoğlu Yılmaz

https://doi.org/10.48123/rsgis.1119572

Cited By: 1

Abstract

Yeryüzü nesnelerinin başarılı bir şekilde izlenmesi, planlanması ve yönetimi için yüksek doğruluklu arazi örtüsü bilgisi gereklidir. Ücretsiz erişilebilen uydu görüntüleri sayesinde, birçok uygulamada temel altlık verisi olarak kullanılabilen yüksek doğruluklu tematik haritaların oluşturulmasına yönelik çalışmalar yoğunlaşmıştır. Bulut tabanlı Google Earth Engine (GEE) platformu, bu tür uydu görüntülerine erişmeyi, depolamayı ve işlemeyi kolaylaştırmaktadır. Bu çalışma, Sentinel-2 uydu görüntüsü ile üretilmiş bir arazi örtüsü haritasının doğruluğunu artırmayı amaçlamaktadır. Bu amaçla, ilk aşamada, çalışma alanı Sentinel-2 verilerinin yalnızca geleneksel bantları kullanılarak sınıflandırılmıştır. Daha sonra, sınıflandırma doğruluğunu iyileştirmek için Sentinel-2 görüntüsünün geleneksel bantlarına Sentinel-1 Yapay Açıklıklı Radar (SAR) verisi, Gelişmiş Arazi Gözlem Uydusu (ALOS) 3 boyutlu sayısal yükseklik modeli verileri, çeşitli spektral indeksler ve Gri Seviyeli Eşdizimlilik Matrisi (GLCM) özellikleri eklenerek çok kaynaklı bir sınıflandırma prosedürü geliştirilmiştir. Trabzon ilinin şehir merkezinin çalışma bölgesi olarak seçildiği bu çalışmada, Rastgele Orman (RF) sınıflandırma algoritması kullanılarak üretilen arazi örtüsü haritasının doğruluğu kullanılan yardımcı veriler ile %83.51'den %92.78'e yükseltilmiştir.

Keywords

Google Earth Engine, Arazi örtüsü haritalama, Görüntü sınıflandırma, Sentinel-2

Improving the Land Cover Mapping Accuracy of the Sentinel-2 Imagery on Google Earth Engine

Abstract

Accurate land cover information is necessary for successful monitoring, planning and management of the land cover features. Thanks to free-access satellite images, studies have focused on the creation of more accurate thematic maps, which have been used as a base data in many applications. The cloud-based Google Earth Engine (GEE) service makes it easier to access, store and process these satellite images. This study aims to improve the accuracy of a land cover map produced with the Sentinel-2 satellite image. For this purpose, as the very first step, study site was classified using only traditional bands of the Sentinel-2 data. To improve the classification accuracy, Sentinel-1 Synthetic Aperture Radar (SAR) data, Advanced Land Observing Satellite (ALOS) World 3D data, various spectral indices and gray-level co-occurrence matrix (GLCM) features were added to the traditional bands of the Sentinel-2 data, leading to a multi-source classification process. In this study, where the city center of Trabzon was selected as the study area, the accuracy of the land cover map produced using the Random Forest (RF) classification algorithm was increased from 83.51% to 92.78% with the auxiliary data used.

Keywords

Google Earth Engine, Land cover mapping, Image classification, Sentinel-2

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