Uydu İmgelerine Derin Öğrenme Tabanlı Süper Çözünürlük Yöntemlerinin Uygulanması

Year 2021, , 1069 - 1077, 31.10.2021

Ayşe Cengiz , Derya Avcı

https://doi.org/10.35414/akufemubid.829644

Cited By: 1

Abstract

Askeri ve sivil hayatta önemli tüm uygulamalar için kullanılan görüntünün çözünürlüğünün yüksek olması çok önemlidir. Uydu imgeleri barındıran çalışmalarda süper çözünürlük ile iyileştirilmiş imgelerin kullanımı bina tespiti gibi uygulamalarda gereklidir. Düşük çözünürlüklü görüntünün giriş olarak verildiği süper çözünürlük algoritmalarında çeşitli iyileştirme adımları neticesinde çıktı olarak yüksek çözünürlüklü görüntü elde edilir. Bu çalışmada kullanıma açık uydu görüntülerinden alınan 6 sınıfa ayrılmış toplam 900 imge üzerinde, derin öğrenme tabanlı evrişimsel sinir ağları ile süper çözünürlük iyileştirilmesinin performansı analiz edilmiştir. Veri seti üzerinde derin öğrenme için test ve eğitim verileri ayrılmıştır. Verilere DenseNet201, SqueezeNet, Vgg16 olmak üzere toplam 3 derin öğrenme mimarisi ayrı ayrı uygulanmıştır. Süper çözünürlük adımı öncesinde ve sonrasında doğru sınıflandırılmış veri oranının kontrolü için evrişimsel sinir ağları uygulanmıştır. Sınıflandırma sonuçları karşılaştırılmış, sınıflandırma sonucunda evrişimsel sinir ağları öğrenme özellikleri süper çözünürlük sayesinde iyileştirilmiştir. Sınıflandırılma başarısı 6 sınıflandırılma mimarisi için en düşük %2,4 ve en yüksek %3,6 oranında arttırılmış olduğu kanıtlanmıştır.

Keywords

Süper Çözünürlük, Evrişimsel Sinir Ağları, Derin Öğrenme, Softmax

Supporting Institution

Fırat Üniversitesi

Application of Deep Learning Based Super Resolution Methods To Satellite Images And Improvement Of Images

Abstract

High resolution of the image used for all important applications in military and civil life is very important. In works with satellite images, the use of images enhanced with super resolution is necessary in applications such as building detection. In the super resolution algorithms where the low-resolution image is given as input, high resolution image is obtained as a result of various improvement steps. The performance of super resolution improvement with deep learning based convolutional neural networks on 900 images taken from available satellite images was analyzed. Test and training data are reserved for deep learning on the dataset. A total of 3 softmax functions (DenseNet201, SqueezeNet, Vgg16) were applied to the data separately. Evolutionary neural networks were applied to control the number of correctly classified data before and after the super resolution step. The classification results are compared and as a result of the classification, the learning properties of the convolutional neural networks are increased by super resolution. Classification success has proven to be increased by the lowest 2.4% and the highest 3.6% for the 6 classification architectures.

Keywords

Super Resolution, Convolutional Neural Network, Deep Learning, Softmax

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