Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models-Reference-Cited by-同舟云学术

Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models

Published:2022 Issue: Volume:60 Page:1-16
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Pastorino Martina¹^ORCID,Moser Gabriele¹^ORCID,Serpico Sebastiano B.¹,Zerubia Josiane²^ORCID

Affiliation:

1. Department of Electrical, Electronic, Telecommunications Engineering and Naval Architecture, University of Genoa, Genoa, Italy

2. Ayana Research Group, Institut National de Recherche en Informatique et en Automatique (INRIA), Université Côte d’Azur (UCA), Sophia Antipolis, France

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/9633014/09676578.pdf?arnumber=9676578

Reference57 articles.

1. VPRS-Based Regional Decision Fusion of CNN and MRF Classifications for Very Fine Resolution Remotely Sensed Images

2. Classification for High Resolution Remote Sensing Imagery Using a Fully Convolutional Network

3. Land Cover Mapping with Higher Order Graph-Based Co-Occurrence Model

4. Contextually guided very-high-resolution imagery classification with semantic segments

5. Hypercolumns for object segmentation and fine-grained localization

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