Çok Yüksek Çözünürlüklü Uydu Görüntülerinden Bina Çıkarımında Derin Öğrenme ve Çoklu-Çözünürlüklü Bölütleme Kullanılarak Nesne-Tabanlı Entegrasyon

Year 2023, , 67 - 77, 30.12.2023

Şaziye Özge Atik

https://doi.org/10.51489/tuzal.1337656

Abstract

Son yıllarda, kentsel alanlarda yapılan analizler ve değişimlerin tespitinin hızlı ve güvenilir şekilde gerçekleştirilmesi konusundaki çalışmalarda artış olmuştur. Bu doğrultuda, binaların sınıflandırılması bilgisayarlı görünün ön plana çıkan güncel konularından biridir. Birçok alanda olduğu gibi bu konuda da derin öğrenme mimarilerinin kullanımı trend uygulamalar arasındadır. Bina ayak izinin belirlenmesi amacıyla evrişimsel sinir ağları (ESA) kullanılarak semantik segmentasyon uygulamaları yaygınlaşmıştır. Ancak derin öğrenme ile segmentasyon işlemleri sonrası elde edilen tahmin görüntülerinde karşılaşılan problemlerin başında tuz-biber etkisiyle oluşmuş gürültüler gelmektedir. Bu çalışmada güncel ESA mimarilerinden olan U-Net ve SegNet algoritmalarının kullanımının, Nesne-Tabanlı Görüntü Analizinin (NTGA), Çoklu-Çözünürlüklü Bölütleme (ÇÇB) algoritmasıyla entegrasyonu kullanılmıştır. Deneyler çok yüksek çözünürlüklü uydu görüntülerinden (Gaofen-2, Worldview-2 ve Ikonos) oluşan açık paylaşımlı Wuhan Üniversitesi Bina Çıkarımı Veri seti (WHUBED) üzerinde gerçekleştirilmiştir. ESA+ÇÇB modeli genel doğruluk, F1 skor, Dice skoru ve Intersection over Union (IoU) metriklerinde, sadece ESA kullanımıyla elde edilen tahmin sonuçlarına göre iyileştirmeler sağlamıştır. Bina sınıflandırılması ile elde edilen haritalar karşılaştırılmalı görseller olarak son kısımda sunulmuştur.

Keywords

derin öğrenme, çoklu-çözünürlüklü bölütleme, nesne-tabanlı görüntü analizi, entegrasyon

Object-Based Integration Using Deep Learning and Multi-Resolution Segmentation in Building Extraction from Very High Resolution Satellite Imagery

Abstract

In recent years, there has been an increase in studies on the analysis of urban areas and the detection of changes in a fast and reliable way. In this respect, the classification of buildings is one of the prominent current issues of computer vision. As in many areas, the use of deep learning architectures is among the trending applications. Semantic segmentation applications have become widespread by using convolutional neural networks (CNN) to determine the building footprint. However, at the beginning of the problems encountered in the prediction images obtained after segmentation processes with deep learning, the noise formed by the effect of salt and pepper comes. In this study, the integration of the use of U-Net and SegNet algorithms, which are among the state-of-the-art CNN architectures, with the Object-Based Image Analysis (OBIA) and Multi-Resolution Segmentation (MRS) algorithm is used. Experiments were performed on the open shared Wuhan University Building Inference Dataset (WHUBED) consisting of very high-resolution satellite images (Gaofen-2, Worldview-2 and Ikonos). The model in the study, provides improvements in overall accuracy, F1 score, Dice score and Intersection over Union (IoU) metrics over the prediction results obtained using CNN alone. Building footprint maps obtained by building extraction are presented in the last section as comparative images.

Keywords

Deep learning, Object-Based Image Analysis, Multi-Resoluion Segmentation, İntegration

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