LAUSR

Object Extraction From Very High-Resolution Images Using a Convolutional Neural Network Based on a Noisy Large-Scale Dataset

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0 Published in 2019 at "IEEE Access"

DOI: 10.1109/access.2019.2938215

Abstract: In recent years, convolutional neural networks (CNNs) have made great achievements in object extraction from very high-resolution (VHR) images. However, most existing approaches require large quantities of clean and accurate training data to achieve impressive… read more here.

Keywords: extraction high; high resolution; training; dataset ... See more keywords

OEBR-GAN: Object Extraction and Background Recovery Generative Adversarial Networks

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0 Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3011187

Abstract: Generative adversarial networks (GAN) have been widely used in the field of image-to-image translation. In this paper, we have proposed a novel object extraction and background recovery (OEBR-GAN) model, which can extract objects from an… read more here.

Keywords: generative adversarial; image; oebr gan; adversarial networks ... See more keywords

An Optimized Point Cloud Classification and Object Extraction Method Using Graph Cuts

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1 Published in 2020 at "IEEE Access"

DOI: 10.1109/access.2020.3030717

Abstract: The requirement for 3D scene classification and understanding has dramatically increased with the widespread use of airborne Light Detection And Ranging (LiDAR). This paper focuses on precise classification and object extraction based on point cloud… read more here.

Keywords: classification object; classification; object extraction; point cloud ... See more keywords

Prototype-Aligned Federated Learning for Robust Object Extraction in Heterogeneous Remote Sensing

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0 Published in 2025 at "IEEE Internet of Things Journal"

DOI: 10.1109/jiot.2025.3587683

Abstract: Federated learning (FL) has emerged as a pivotal collaborative machine learning framework, enabling privacy-preserving analytics for smart city applications using distributed data from Internet of Things (IoT) devices. However, the inherent data heterogeneity that arises… read more here.

Keywords: extraction heterogeneous; prototype; heterogeneous remote; remote sensing ... See more keywords

Federated Deep Learning With Prototype Matching for Object Extraction From Very-High-Resolution Remote Sensing Images

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2 Published in 2023 at "IEEE Transactions on Geoscience and Remote Sensing"

DOI: 10.1109/tgrs.2023.3244136

Abstract: Deep convolutional neural networks (DCNNs) have become the leading tools for object extraction from very-high-resolution (VHR) remote sensing images. However, the label scarcity problem of local datasets hinders the prediction performances of DCNNs, and privacy… read more here.

Keywords: object extraction; remote sensing; extraction high; prototype matching ... See more keywords