Rethinking Panoptic Segmentation in Remote Sensing: A Hybrid Approach Using Semantic Segmentation and Non-Learning Methods-Reference-Cited by-同舟云学术

Rethinking Panoptic Segmentation in Remote Sensing: A Hybrid Approach Using Semantic Segmentation and Non-Learning Methods

Published:2022 Issue: Volume:19 Page:1-5
ISSN:1545-598X
Container-title:IEEE Geoscience and Remote Sensing Letters
language:
Short-container-title:IEEE Geosci. Remote Sensing Lett.

Author:

de Carvalho Osmar L. F.¹,de Carvalho Junior Osmar A.²^ORCID,de Albuquerque Anesmar O.²^ORCID,Santana Nickolas C.²,Borges Dibio L.¹^ORCID

Affiliation:

1. Department of Computer Science, University of Brasilia, Brasília, Brazil

2. Department of Geography, University of Brasilia, Brasília, Brazil

Funder

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Geotechnical Engineering and Engineering Geology

Link

http://xplorestaging.ieee.org/ielx7/8859/9651998/09766343.pdf?arnumber=9766343

Reference16 articles.

1. Bounding box-free instance segmentation using semi-supervised learning for generating a city-scale vehicle dataset;de carvalho;arXiv 2111 12122,2021

2. HED-UNet: Combined Segmentation and Edge Detection for Monitoring the Antarctic Coastline

3. Vehicle Instance Segmentation From Aerial Image and Video Using a Multitask Learning Residual Fully Convolutional Network

4. Deep Semantic Segmentation of Center Pivot Irrigation Systems from Remotely Sensed Data

5. Instance segmentation for large, multi-channel remote sensing imagery using mask-RCNN and a mosaicking approach;carvalho;Remote Sens,2020

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