Süper Çözünürlük Yönteminin Uydu İmgelerinin Sınıflandırma Performansına Etkisi

Year 2023, Volume: 18 Issue: 2, 331 - 344, 01.09.2023

Ayşe Cengiz Derya Avcı

https://doi.org/10.55525/tjst.1252420

Abstract

Görüntünün yüksek çözünürlüğü uygulamalar için çok önemlidir. Halka açık sunulan uydu görüntülerinin çözünürlükleri genellikle düşüktür. Düşük çözünürlük bilgi kaybına yol açtığından uzaktan algılama alanında çalışılan problemin türüne bağlı olarak istenilen başarım sağlanamamaktadır. Böyle bir durumda düşük çözünürlüklü görüntülerden yüksek çözünürlüklü görüntü elde etmek için süper-çözünürlük algoritmaları kullanılmaktadır. Uydu görüntüleri ile yapılan çalışmalarda süper çözünürlükle zenginleştirilmiş görüntülerin kullanılması önemlidir. Uydu görüntülerinin çözünürlükleri düşük olduğundan dolayı sınıflandırma işleminde başarım oranı düşük çıkmaktadır. Bu çalışmada, uydu görüntülerinin sınıflandırma başarımını artırmak için süper çözünürlük yöntemi önerilmiştir. Derin öğrenme mimarisinden AlexNet, ResNet50, Vgg19 kullanılarak uydu imgelerinin öznitelikleri çıkarılmıştır. Ardından çıkarılan öznitelikler, AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Destek Vektör Makinesi, K- En Yakın Komşu ve Naive Bayes sınıflandırma algoritmalarının girişine verilerek 6 sınıfa ayrılmıştır. Süper çözünürlük öncesi ve süper çözünürlük sonrası özellik çıkarma ve sınıflandırma işlemleri ayrı ayrı yapılmıştır. Süper çözünürlükten önce ve sonra sınıflandırma sonuçları karşılaştırılmıştır. Süper çözünürlük kullanılarak sınıflandırma performansında iyileşme gözlemlenmiştir.

Keywords

Süper Çözünürlük, Evrişimsel Sinir Ağları, Derin Öğrenme, Sınıflandırma

The Effect of Super Resolution Method on Classification Performance of Satellite Images

Abstract

The high resolution of the image is very important for applications. Publicly available satellite images generally have low resolutions. Since low resolution causes loss of information, the desired performance cannot be achieved depending on the type of problem studied in the field of remote sensing. In such a case, super resolution algorithms are used to render low resolution images high resolution. Super resolution algorithms are used to obtain high resolution images from low resolution images. In studies with satellite images, the use of images enhanced with super resolution is important. Since the resolution of satellite images is low, the success rate in the classification process is low. In this study, super resolution method is proposed to increase the classification performance of satellite images. The attributes of satellite images were extracted using AlexNet, ResNet50, Vgg19 from deep learning architecture. Then the extracted features were then classified into 6 classes by giving input to AlexNet-Softmax, ResNet50-Softmax, Vgg19-Softmax, Support Vector Machine, K-Nearest Neighbor, decision trees and Naive Bayes classification algorithms. Without super resolution and with super resolution feature extraction and classification processes were performed separately. Classification results without super resolution and with super resolution were compared. Improvement in classification performance was observed using super resolution.

Keywords

Super Resolution, Convolutional Neural Networks, Deep Learning, Classification

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