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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…
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Keywords:
extraction high;
high resolution;
training;
dataset ... See more keywords
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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…
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Keywords:
generative adversarial;
image;
oebr gan;
adversarial networks ... See more keywords
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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…
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Keywords:
classification object;
classification;
object extraction;
point cloud ... See more keywords
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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…
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Keywords:
object extraction;
remote sensing;
extraction high;
prototype matching ... See more keywords