Title:	Rethinking panoptic segmentation in remote sensing : a hybrid approach using semantic segmentation and non-learning methods
Authors:	Carvalho, Osmar Luiz Ferreira de Carvalho Júnior, Osmar Abílio de Albuquerque, Anesmar Olino de Santana, Níckolas Castro Borges, Díbio Leandro
metadata.dc.identifier.orcid:	https://orcid.org/0000-0002-5619-8525 https://orcid.org/0000-0002-0346-1684 https://orcid.org/0000-0003-1561-7583 https://orcid.org/0000-0001-6133-6753 https://orcid.org/0000-0002-4868-0629
metadata.dc.contributor.affiliation:	University of Brasilia, Department of Computer Science University of Brasilia, Department of Geography University of Brasilia, Department of Geography University of Brasilia, Department of Geography University of Brasilia, Department of Computer Science
Assunto::	Sensoriamento remoto Segmentação semântica Aprendizagem profunda Segmentação de imagens Segmentação panótica
Issue Date:	3-May-2022
Publisher:	IEEE
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.
Abstract:	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.
metadata.dc.description.unidade:	Instituto de Ciências Exatas (IE) Departamento de Ciência da Computação (IE CIC) Instituto de Ciências Humanas (ICH) Departamento de Geografia (ICH GEA)
metadata.dc.relation.publisherversion:	https://ieeexplore.ieee.org/document/9766343
Appears in Collections:	Artigos publicados em periódicos e afins

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