Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification

Abstract

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs.
Fichier principal
Vignette du fichier
Pastorino-EUSIPCO-2021.pdf (620.47 Ko) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03252999 , version 1 (08-06-2021)

Identifiers

Cite

Martina Pastorino, Gabriele Moser, Sebastiano B Serpico, Josiane Zerubia. Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification. EUSIPCO 2021 - 29th IEEE European Signal Processing Conference, Aug 2021, Dublin / Virtual, Ireland. ⟨10.23919/EUSIPCO54536.2021.9616179⟩. ⟨hal-03252999⟩
108 View
176 Download

Altmetric

Share

Gmail Mastodon Facebook X LinkedIn More