Research on remote sensing image classification significantly impacts essential human routine tasks such as urban planning and agriculture. Nowadays, the rapid advance in technology and the availability of many high-quality remote sensing images create a demand for reliable automation methods. The current paper proposes two novel deep learning-based architectures for image classification purposes, i.e., the Discriminant Deep Image Prior Network and the Discriminant Deep Image Prior Network+, which combine Deep Image Prior and Triplet Networks learning strategies. Experiments conducted over three well-known public remote sensing image datasets achieved state-of-the-art results, evidencing the effectiveness of using deep image priors for remote sensing image classification.
@article{arxiv.2212.10411,
title = {DDIPNet and DDIPNet+: Discriminant Deep Image Prior Networks for Remote Sensing Image Classification},
author = {Daniel F. S. Santos and Rafael G. Pires and Leandro A. Passos and João P. Papa},
journal= {arXiv preprint arXiv:2212.10411},
year = {2022}
}
Comments
Published in: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS