no code implementations • 8 Nov 2023 • Gulsen Taskin, Erchan Aptoula, Alp Ertürk
Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation.
no code implementations • 12 Aug 2023 • Berker Demirel, Erchan Aptoula, Huseyin Ozkan
To this end, most of existing studies focus on extracting domain invariant features across the available source domains in order to mitigate the effects of inter-domain distributional changes.
1 code implementation • IEEE Signal Processing and Communications Applications (SIU) 2023 • Efkan Durakli, Erchan Aptoula
In this paper, we proposed a domain generalization method to address domain shift at the instance and image level for roof type detection from remote sensing images.
1 code implementation • 7 Dec 2022 • Sarmad F. Ismael, Koray Kayabol, Erchan Aptoula
Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation.
no code implementations • 10 Mar 2022 • Karthik Seemakurthy, Charles Fox, Erchan Aptoula, Petra Bosilj
Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain specific features, so that a model can generalise well on previously unseen target domains.
no code implementations • 18 Jun 2018 • Minh-Tan Pham, Erchan Aptoula, Sébastien Lefèvre
The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures.
no code implementations • 27 Mar 2018 • Minh-Tan Pham, Sébastien Lefèvre, Erchan Aptoula, Lorenzo Bruzzone
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task.
no code implementations • 13 Dec 2017 • AmirAbbas Davari, Erchan Aptoula, Berrin Yanikoglu, Andreas Maier, Christian Riess
This synthetic data is sampled from a GMM fitted to each class of the limited training data.
Classification Of Hyperspectral Images General Classification +1