Panoptic Segmentation Meets Remote Sensing

Carvalho, Osmar Luiz Ferreira de; Júnior, Osmar Abı́lio de Carvalho; Silva, Cristiano Rosa e; Albuquerque, Anesmar Olino de; Santana, Níckolas Castro; Borges, Díbio Leandro; Gomes, Roberto Arnaldo Trancoso; Guimarães, Renato Fontes

doi:10.3390/rs14040965

Cited by 27 publications

(20 citation statements)

References 82 publications

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“…Sen4AgriNet comes with a total of 168 labels including noncrop related classes which can serve as the broad "stuff" category in panoptic segmentation terminology, whereas parcel boundaries can assist the identification of each individual object in an image for both tasks. We believe that the proposed dataset will encourage further research in this area and provide the required momentum for the development of more models and approaches, especially as far as panoptic segmentation is concerned where research studies focused on remote sensing are few and far between (e.g., [11] and [64]).…”

Section: Discussionmentioning

confidence: 99%

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

Sykas

Sdraka

Zografakis

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this work, we introduce Sen4AgriNet, a Sentinel-2-based time series multicountry benchmark dataset, tailored for agricultural monitoring applications with machine and deep learning. Sen4AgriNet dataset is annotated from farmer declarations collected via the land parcel identification system (LPIS) for harmonizing country-wide labels. These declarations have only recently been made available as open data, allowing for the first time the labeling of satellite imagery from ground truth data. We proceed to propose and standardize a new crop type taxonomy across Europe that addresses common agriculture policy (CAP) needs, based on the Food and Agriculture Organization (FAO) indicative crop classification scheme. Sen4AgriNet is the only multicountry, multiyear dataset that includes all spectral information. It is constructed to cover the period 2016-2020 for Catalonia and France, while it can be extended to include additional countries. Currently, it contains 42.5 million parcels, which makes it significantly larger than other available archives. We extract two subdatasets to highlight its value for diverse deep learning applications-the object aggregated dataset (OAD) and the patches assembled dataset (PAD). OAD capitalizes zonal statistics of each parcel, thus creating a powerful label-to-features instance for classification algorithms. On the other hand, PAD structure generalizes the classification problem to parcel extraction and semantic segmentation and labeling. The PAD and OAD are examined under three different scenarios to showcase and model the effects of spatial and temporal variability across different years and different countries. 1

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Section: Discussionmentioning

confidence: 99%

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

Sykas

Sdraka

Zografakis

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…In panoptic segmentation, the prediction from both instance and semantic segmentation is combined into a unified output [81]. Studying things and stuff is part of panoptic segmentation.…”

Section: Panoptic Segmentationmentioning

confidence: 99%

Object-based Classification of Natural Scenes Using Machine Learning Methods

Jasim¹,

Younis²

2023

Technium

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The replication of human intellectual processes by machines, particularly computer systems, is known as artificial intelligence (AI). AI is an intelligent tool that is utilized across sectors to improve decision making, increase productivity, and eliminate repetitive tasks. Machine learning (ML) is a key component of AI since it includes understanding and developing ways that can learn or improve performance on tasks. For the last decade, ML has been applied in computer vision (CV) applications. In computer vision, systems and computers extract meaningful data from digital videos, photos, and other visual sources and use that information to conduct actions or make suggestions. In this work, we have solved the image segmentation problem for the natural images to segment out water, land, and sky. Instead of applying image segmentation directly to the images, images are pre-processed, and statistical and textural features are then passed through a neural network for the pixel-wise semantic segmentation of the images. We chose the 5X5 window over the pixel-by-pixel technique since it requires less resources and time for training and testing.

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“…Moreover, it introduces the MaskIoU module to improve the overall quality of the FG target mask. De Carvalho et al [142] offered an effective solution for employing panoptic segmentation in UAV remote sensing. Their approach includes providing a large dataset and introducing a novel pipeline/software that facilitates the generation of panoptic segmentation datasets with ease.…”

Section: Uav Filedmentioning

confidence: 99%

Deep learning‐based panoptic segmentation: Recent advances and perspectives

Chuang

Zhang

Zhao

2023

IET Image Processing

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In recent years, panoptic segmentation has drawn increasing amounts of attention, leading to the rapid emergence of numerous related algorithms. A variety of deep neural networks have been used more frequently for panoptic segmentation, which is motivated by the significant success of deep learning methods in other tasks. This article presents a comprehensive exploration of panoptic segmentation, focusing on the analysis and understanding of RGB image data. Initially, the authors introduce the background of panoptic segmentation, including deep learning models and image segmentation. Then, the authors thoroughly cover a variety of panoptic segmentation‐related topics, such as datasets connected to the field, evaluation metrics, panoptic segmentation models, and derived subfields based on panoptic segmentation. Finally, the authors examine the difficulties and possibilities in this area and identify its future paths.

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Panoptic Segmentation Meets Remote Sensing

Cited by 27 publications

References 82 publications

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

A Sentinel-2 Multiyear, Multicountry Benchmark Dataset for Crop Classification and Segmentation With Deep Learning

Object-based Classification of Natural Scenes Using Machine Learning Methods

Deep learning‐based panoptic segmentation: Recent advances and perspectives

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