Remote sensing image scene classification is a contentious research area, particularly in difficult-to-classify regions. The Danube delta is a constantly changing and difficult to categorize region. Machine learning methods have recently been used for scene classification because of their beneficial results. For many remote sensing applications, co-registration of Multi Spectral Images (MS) and Synthetic Aperture Radar (SAR) data is crucial. This paper focuses on both supervised and unsupervised novel machine learning methods, such as t-SNE, k-means, and SVM, applied to co-registered Sentinel-1 and Sentinel-2 data of the Danube delta. The outcome demonstrates that Sentinel-1 vertical-vertical (VV) is a better band for data training since it exhibits more details, and the learned SVM classifier using t-SNE can be applied to other days with a respectable level of accuracy.
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