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Poster Année : 2022

Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Semantic Segmentation of Remote Sensing Images

Résumé

This work deals with the challenge of semantic segmentation based on deep learning methods in the case of realistic scarce ground truth maps. Exhaustive ground truths usually found in benchmark datasets can be used to train deep learning architectures successfully. On the contrary, real-world ground truths are almost never exhaustive, they are spatially sparse and typically they don't represent the spatial borders among the classes, affecting the accuracy of the resulting segmentation maps significantly. Here, the proposed approach addresses precisely this challenge with a novel combination of hierarchical probabilistic graphical models (PGMs) and deep neural networks. The rationale is to exploit the spatial modeling capabilities of hierarchical PGMs to mitigate the impact of incomplete ground truth and obtain accurate classification results in scenarios where exhaustive ground truth does not exist.
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hal-04269412 , version 1 (03-11-2023)

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  • HAL Id : hal-04269412 , version 1

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Martina Pastorino, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia. Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Semantic Segmentation of Remote Sensing Images. Workshop on Machine Learning and Signal Processing on Graphs / Apprentissage Automatique et Traitement du Signal sur Graphes, Nov 2022, Marseille (CIRM, Centre International de Rencontres Mathématiques), France. ⟨hal-04269412⟩
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