Work

Semantic Segmentation of SAR Images Through Fully Convolutional Networks and Hierarchical Probabilistic Graphical Models

Year: 2022

Type: conference-paper

Abstract: This paper addresses the semantic segmentation of synthetic aperture radar (SAR) images through the combination of ful-ly convolutional networks (FCN s), hierarchical probabilistic graphical models (PGMs), and decision tree ensembles. The idea is to incorporate long-range spatial information together with the multiresolution information extracted by FCN s, through the multiresolution graph topology on which hierar-chical PGMs can be efficiently formulated. The objective is to obtain accurate classification results with small datasets and reduce problems of spatial inconsistency. The experimental validation is conducted with several COSMO-SkyMed satel-lite images over Northern Italy. The results are significant, as the proposed method obtains more accurate classification results than the standard FCN s considered.

Source: IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

DOI: 10.1109/igarss46834.2022.9883111

Authors: Martina Pastorino, Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia

Institutions: Université Côte d'Azur, AYANA: Télédétection et IA embarqués pour le "New Space", University of Genoa, Institut national de recherche en sciences et technologies du numérique

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Agence Nationale de la Recherche

Award: Idex UCA JEDI