A local–global dual-stream network for building extraction from very-high-resolution remote sensing images
Buildings constitute one of the most important landscapes in remote sensing (RS) images
and have been broadly analyzed in a wide range of applications from urban planning to …
and have been broadly analyzed in a wide range of applications from urban planning to …
BSNet: Dynamic hybrid gradient convolution based boundary-sensitive network for remote sensing image segmentation
J Hou, Z Guo, Y Wu, W Diao… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Boundary information is essential for the semantic segmentation of remote sensing images.
However, most existing methods were designed to establish strong contextual information …
However, most existing methods were designed to establish strong contextual information …
Few-shot common-object reasoning using common-centric localization network
In the few-shot common-localization task, given few support images without bounding box
annotations at each episode, the goal is to localize the common object in the query image of …
annotations at each episode, the goal is to localize the common object in the query image of …
Boundary-oriented binary building segmentation model with two scheme learning for aerial images
Various deep learning-based segmentation models have been developed to segment
buildings in aerial images. However, the segmentation maps predicted by the conventional …
buildings in aerial images. However, the segmentation maps predicted by the conventional …
Domain adaptive transfer attack-based segmentation networks for building extraction from aerial images
Semantic segmentation models based on convolutional neural networks (CNNs) have
gained much attention in relation to remote sensing and have achieved remarkable …
gained much attention in relation to remote sensing and have achieved remarkable …
A robust and high-precision edge segmentation and refinement method for high-resolution images
Q Li, C Chen - Mathematical biosciences and engineering …, 2023 - pubmed.ncbi.nlm.nih.gov
Limited by GPU memory, high-resolution image segmentation is a highly challenging task.
To improve the accuracy of high-resolution segmentation, High-Resolution Refine Net …
To improve the accuracy of high-resolution segmentation, High-Resolution Refine Net …
Revolutionizing remote sensing image analysis with BESSL-Net: a boundary-enhanced semi-supervised learning network
Z Yi, Y Wang, L Zhang - IEEE Transactions on Geoscience and …, 2023 - ieeexplore.ieee.org
Deep learning (DL) has become increasingly popular in remote sensing (RS) change
detection (CD), leading to the development of massive networks that surpass traditional …
detection (CD), leading to the development of massive networks that surpass traditional …
[BUCH][B] Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III
The six volume set LNCS 11361-11366 constitutes the proceedings of the 14th Asian
Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018 …
Conference on Computer Vision, ACCV 2018, held in Perth, Australia, in December 2018 …
Dense In Dense: Training Segmentation from Scratch
T Hu - Computer Vision–ACCV 2018: 14th Asian Conference …, 2019 - Springer
In recent years, training image segmentation networks often needs fine-tuning the model
which comes from the initial training upon large-scale classification datasets like ImageNet …
which comes from the initial training upon large-scale classification datasets like ImageNet …
[HTML][HTML] MACHINE LEARNING-BASED SYSTEM MODELING FOR SPECTRUM SENSING & SEMANTIC SEGMENTATION
JH Kim - 2020 - scholar.dgist.ac.kr
Machine-learning algorithms have attracted much attention in a wide range of areas.
Because machine-learning algorithms can extract patterns from data automatically, it is …
Because machine-learning algorithms can extract patterns from data automatically, it is …