Rethinking panoptic segmentation in remote sensing : a hybrid approach using semantic segmentation and non-learning methods

Carvalho, Osmar Luiz Ferreira de; Carvalho Júnior, Osmar Abílio de; Albuquerque, Anesmar Olino de; Santana, Níckolas Castro; Borges, Díbio Leandro

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Campo DC	Valor	Idioma
dc.contributor.author	Carvalho, Osmar Luiz Ferreira de	-
dc.contributor.author	Carvalho Júnior, Osmar Abílio de	-
dc.contributor.author	Albuquerque, Anesmar Olino de	-
dc.contributor.author	Santana, Níckolas Castro	-
dc.contributor.author	Borges, Díbio Leandro	-
dc.date.accessioned	2023-09-21T12:15:56Z	-
dc.date.available	2023-09-21T12:15:56Z	-
dc.date.issued	2022-05-03	-
dc.identifier.citation	CARVALHO, Osmar L. F. de Carvalho et al. Rethinking panoptic segmentation in remote sensing: a hybrid approach using semantic segmentation and non-learning methods. IEEE Geoscience and Remote Sensing Letters, [S.l.], v. 19, art. n. 3512105, p. 1-5, 2022, DOI: 10.1109/LGRS.2022.3172207. Disponível em: https://ieeexplore.ieee.org/document/9766343.	pt_BR
dc.identifier.uri	http://repositorio2.unb.br/jspui/handle/10482/46526	-
dc.language.iso	eng	pt_BR
dc.publisher	IEEE	pt_BR
dc.rights	Acesso Restrito	pt_BR
dc.title	Rethinking panoptic segmentation in remote sensing : a hybrid approach using semantic segmentation and non-learning methods	pt_BR
dc.type	Artigo	pt_BR
dc.subject.keyword	Sensoriamento remoto	pt_BR
dc.subject.keyword	Segmentação semântica	pt_BR
dc.subject.keyword	Aprendizagem profunda	pt_BR
dc.subject.keyword	Segmentação de imagens	pt_BR
dc.subject.keyword	Segmentação panótica	pt_BR
dc.relation.publisherversion	https://ieeexplore.ieee.org/document/9766343	pt_BR
dc.description.abstract1	This letter proposes a novel method to obtain panoptic predictions by extending the semantic segmentation task with a few non-learning image processing steps, presenting the following benefits: 1) annotations do not require a specific format [e.g., common objects in context (COCO)]; 2) fewer parameters (e.g., single loss function and no need for object detection parameters); and 3) a more straightforward sliding windows implementation for large image classification (still unexplored for panoptic segmentation). Semantic segmentation models do not individualize touching objects, as their predictions can merge; i.e., a single polygon represents many targets. Our method overcomes this problem by isolating the objects using borders on the polygons that may merge. The data preparation requires generating a one-pixel border, and for unique object identification, we create a list with the isolated polygons, attribute a different value to each one, and use the expanding border (EB) algorithm for those with borders. Although any semantic segmentation model applies, we used the U-Net with three backbones (EfficientNet-B5, EfficientNet-B3, and EfficientNet-B0). The results show that the following hold: 1) the EfficientNet-B5 had the best results with 70% mean intersection over union (mIoU); 2) the EB algorithm presented better results for better models; 3) the panoptic metrics show a high capability of identifying things and stuff with 65 panoptic quality (PQ); and 4) the sliding windows on a 2560×2560 -pixel area has shown promising results, in which the ratio of merged objects by correct predictions was lower than 1% for all classes.	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-5619-8525	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-0346-1684	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0003-1561-7583	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0001-6133-6753	pt_BR
dc.identifier.orcid	https://orcid.org/0000-0002-4868-0629	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Computer Science	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Geography	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Geography	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Geography	pt_BR
dc.contributor.affiliation	University of Brasilia, Department of Computer Science	pt_BR
dc.description.unidade	Instituto de Ciências Exatas (IE)	pt_BR
dc.description.unidade	Departamento de Ciência da Computação (IE CIC)	pt_BR
dc.description.unidade	Instituto de Ciências Humanas (ICH)	pt_BR
dc.description.unidade	Departamento de Geografia (ICH GEA)	pt_BR
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