Otokodlayıcılar Kullanarak Uzaktan Algılama Görüntülerindeki Eksik Verilerin Yeniden Yapılandırılması

Year 2022, , 853 - 862, 30.12.2022

Serkan Kartal

https://doi.org/10.21605/cukurovaumfd.1230776

Abstract

Uzaktan algılama çalışmalarında uydu görüntülerindeki eksik verilerin yeniden yapılandırılması, veri kullanılabilirliğini artırmak ve analiz süreçlerini kolaylaştırmak açısından büyük önem taşımaktadır. Bu çalışmada, bu problemi çözmek için otokodlayıcı adı verilen Yapay Sinir Ağı (YSA) modeli kullanılmıştır. Çalışmanın amacı, büyük oranda eksik veri içeren ve bu nedenle interpolasyon gibi klasik yöntemlerle yüksek doğrulukla yeniden yapılandırılması zor olan uydu görüntülerini başarılı bir şekilde yeniden yapılandıracak bir YSA modelinin geliştirilmesidir. Model, Orta Çözünürlüklü Görüntüleme Spektroradyometresi (MODIS) sensörleri ile elde edilen 1-km çözünürlüğe sahip günlük (MYD11A1) yüzey sıcaklığı verileri üzerinde test edilmiştir. Çalışma alanı Türkiye’nin güneyinde yer alan, Antalya ilinin kuzeyi ile Burdur ve Isparta il sınırları içerisinde bulunan bir bölgeyi kapsamaktadır. 2017-2020 tarih aralığına ait 306 veri üzerinde yapılan çalışma sonucunda modelin %70 ve üzerinde eksik bilgi içeren verileri 1,79 Ortalama Mutlak Hata (OMH) değeri ile tamamlayabildiği görülmüştür.

Keywords

Yapay sinir ağları, Otokodlayıcılar, Uzaktan algılama, MODIS, Yeniden yapılandırma

Reconstruction of Missing Data in Remote Sensing Images Using

Abstract

Reconstruction of missing data in satellite images in remote sensing studies is of great importance in terms of increasing data availability and facilitating analysis processes. In this study, an Artificial Neural Network (ANN) model called an autoencoder was used to solve this problem. The study aims to develop an ANN model that will successfully reconstruct satellite images that contain largely missing data and are therefore difficult to reconstruct with high accuracy by classical methods such as interpolation. The model was tested on daily surface temperature data (MYD11A1) with 1-km resolution obtained with Moderate Resolution Imaging Spectroradiometry (MODIS) sensors. The study area covers a region located in the south of Turkey, in the north of Antalya province, and within the borders of Burdur and Isparta. As a result of the study carried out on 306 images belonging to the 2017-2020 date range, it was seen that the model was able to reconstruct the images containing 70% or more missing data with a Mean Absolute Error (MAE) value of 1.79.

Keywords

: Neural networks, Autoencoders, Remote sensing, Reconstruction

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