Boundary loss for remote sensing imagery semantic segmentation
A Bokhovkin, E Burnaev - International Symposium on Neural Networks, 2019 - Springer
In response to the growing importance of geospatial data, its analysis including semantic
segmentation becomes an increasingly popular task in computer vision today. Convolutional …
segmentation becomes an increasingly popular task in computer vision today. Convolutional …
Classification with an edge: Improving semantic image segmentation with boundary detection
We present an end-to-end trainable deep convolutional neural network (DCNN) for semantic
segmentation with built-in awareness of semantically meaningful boundaries. Semantic …
segmentation with built-in awareness of semantically meaningful boundaries. Semantic …
Simple and efficient: A semisupervised learning framework for remote sensing image semantic segmentation
Semantic segmentation based on deep learning has achieved impressive results in recent
years, but these results are supported by a large amount of labeled data, which requires …
years, but these results are supported by a large amount of labeled data, which requires …
Combining deep learning and ontology reasoning for remote sensing image semantic segmentation
Y Li, S Ouyang, Y Zhang - Knowledge-based systems, 2022 - Elsevier
Because of its wide potential applications, remote sensing (RS) image semantic
segmentation has attracted increasing research interest in recent years. Until now, deep …
segmentation has attracted increasing research interest in recent years. Until now, deep …
A multi-attention UNet for semantic segmentation in remote sensing images
Y Sun, F Bi, Y Gao, L Chen, S Feng - Symmetry, 2022 - mdpi.com
In recent years, with the development of deep learning, semantic segmentation for remote
sensing images has gradually become a hot issue in computer vision. However …
sensing images has gradually become a hot issue in computer vision. However …
A deep learning method for optimizing semantic segmentation accuracy of remote sensing images based on improved UNet
X Wang, Z Hu, S Shi, M Hou, L Xu, X Zhang - Scientific reports, 2023 - nature.com
Semantic segmentation of remote sensing imagery (RSI) is critical in many domains due to
the diverse landscapes and different sizes of geo-objects that RSI contains, making …
the diverse landscapes and different sizes of geo-objects that RSI contains, making …
STransFuse: Fusing swin transformer and convolutional neural network for remote sensing image semantic segmentation
L Gao, H Liu, M Yang, L Chen, Y Wan… - IEEE journal of …, 2021 - ieeexplore.ieee.org
The applied research in remote sensing images has been pushed by convolutional neural
network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model …
network (CNN). Because of the fixed size of the perceptual field, CNN is unable to model …
DIResUNet: Architecture for multiclass semantic segmentation of high resolution remote sensing imagery data
Scene understanding is an important task in information extraction from high-resolution
aerial images, an operation which is often involved in remote sensing applications …
aerial images, an operation which is often involved in remote sensing applications …
LANet: Local attention embedding to improve the semantic segmentation of remote sensing images
The trade-off between feature representation power and spatial localization accuracy is
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …
crucial for the dense classification/semantic segmentation of remote sensing images (RSIs) …
An improved U-Net method for the semantic segmentation of remote sensing images
Z Su, W Li, Z Ma, R Gao - Applied Intelligence, 2022 - Springer
Foremost deep neural network models trained in natural scenes cannot transfer and apply to
remote sensing image semantic segmentation well. Studies have shown that fine-tuning …
remote sensing image semantic segmentation well. Studies have shown that fine-tuning …
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