Details

Autor(en) / Beteiligte

• Pastorino, Martina
• Moser, Gabriele
• Serpico, Sebastiano B.
• Zerubia, Josiane

Titel

Learning CRF potentials through fully convolutional networks for satellite image semantic segmentation

Ist Teil von

• 2023 17th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), 2023, p.93-98

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• This paper introduces a method to automatically learn the unary and pairwise potentials of a conditional random field (CRF) from the input data in a non-parametric fashion, within the framework of the semantic segmentation of remote sensing images. The proposed model is based on fully convolutional networks (FCNs) and fully connected neural networks (FCNNs) to extensively exploit the semantic and spatial information contained in the input data and in the intermediate layers of an FCN. The idea of the model is twofold: first to learn the statistics of a CRF via a convolutional layer, whose kernel defines the clique of interest, and, second, to favor the interpretability of the intermediate layers as posterior probabilities through the FCNNs. The method was tested with the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, after modifying the ground truths to approximate the ones found in realistic remote sensing applications, characterized by scarce and spatially non-exhaustive annotations. The results confirm the effectiveness of the proposed technique for the semantic segmentation of satellite images.