scholarly journals ABCNet: Attentive bilateral contextual network for efficient semantic segmentation of Fine-Resolution remotely sensed imagery

2021 ◽  
Vol 181 ◽  
pp. 84-98
Author(s):  
Rui Li ◽  
Shunyi Zheng ◽  
Ce Zhang ◽  
Chenxi Duan ◽  
Libo Wang ◽  
...  
Keyword(s):  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Remotely Sensed Imagery ◽  
Fine Resolution

scholarly journals MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

2021 ◽  
pp. 1-5
Author(s):  
Rui Li ◽  
Chenxi Duan ◽  
Shunyi Zheng ◽  
Ce Zhang ◽  
Peter M. Atkinson
Keyword(s):  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Remotely Sensed Images ◽  
Fine Resolution

scholarly journals FCAU-Net for the Semantic Segmentation of Fine-Resolution Remotely Sensed Images

2022 ◽  
Vol 14 (1) ◽  
pp. 215
Author(s):  
Xuerui Niu ◽  
Qiaolin Zeng ◽  
Xiaobo Luo ◽  
Liangfu Chen
Keyword(s):  
Spatial Information ◽  
Satellite Image ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Feature Representation ◽  
Position Information ◽  
Feature Information ◽  
Remotely Sensed Images ◽  
Public Datasets ◽  
Fine Resolution

The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing. Solving this problem can help overcome various obstacles in urban planning, land cover classification, and environmental protection, paving the way for scene-level landscape pattern analysis and decision making. Encoder-decoder structures based on attention mechanisms have been frequently used for fine-resolution image segmentation. In this paper, we incorporate a coordinate attention (CA) mechanism, adopt an asymmetric convolution block (ACB), and design a refinement fusion block (RFB), forming a network named the fusion coordinate and asymmetry-based U-Net (FCAU-Net). Furthermore, we propose novel convolutional neural network (CNN) architecture to fully capture long-term dependencies and fine-grained details in fine-resolution remotely sensed imagery. This approach has the following advantages: (1) the CA mechanism embeds position information into a channel attention mechanism to enhance the feature representations produced by the network while effectively capturing position information and channel relationships; (2) the ACB enhances the feature representation ability of the standard convolution layer and captures and refines the feature information in each layer of the encoder; and (3) the RFB effectively integrates low-level spatial information and high-level abstract features to eliminate background noise when extracting feature information, reduces the fitting residuals of the fused features, and improves the ability of the network to capture information flows. Extensive experiments conducted on two public datasets (ZY-3 and DeepGlobe) demonstrate the effectiveness of the FCAU-Net. The proposed FCAU-Net transcends U-Net, Attention U-Net, the pyramid scene parsing network (PSPNet), DeepLab v3+, the multistage attention residual U-Net (MAResU-Net), MACU-Net, and the Transformer U-Net (TransUNet). Specifically, the FCAU-Net achieves a 97.97% (95.05%) pixel accuracy (PA), a 98.53% (91.27%) mean PA (mPA), a 95.17% (85.54%) mean intersection over union (mIoU), and a 96.07% (90.74%) frequency-weighted IoU (FWIoU) on the ZY-3 (DeepGlobe) dataset.

Remotely sensed imagery revealing the effects of hurricanes Gustav and Ike on coastal Louisiana

Data Series ◽  
10.3133/ds566 ◽  
2010 ◽  
Author(s):  
John A. Barras ◽  
John C. Brock ◽  
Robert A. Morton ◽  
Laurinda J. Travers
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Imagery ◽  
Coastal Louisiana

Map-guided interpretation of remotely-sensed imagery

1980 ◽  
Author(s):  
J. M. Tenenbaum ◽  
H. G. Barrow ◽  
R. C. Bolles ◽  
M. A. Fischler ◽  
H. C. Wolf
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Imagery

scholarly journals Transformer Meets Convolution: A Bilateral Awareness Network for Semantic Segmentation of Very Fine Resolution Urban Scene Images

2021 ◽  
Vol 13 (16) ◽  
pp. 3065
Author(s):  
Libo Wang ◽  
Rui Li ◽  
Dongzhi Wang ◽  
Chenxi Duan ◽  
Teng Wang ◽  
...  
Keyword(s):  
Large Scale ◽  
Texture Features ◽  
Semantic Segmentation ◽  
Autonomous Driving ◽  
Research Field ◽  
Learning Approaches ◽  
Fine Grained ◽  
Urban Scene ◽  
Fine Resolution ◽  
With Memory

Semantic segmentation from very fine resolution (VFR) urban scene images plays a significant role in several application scenarios including autonomous driving, land cover classification, urban planning, etc. However, the tremendous details contained in the VFR image, especially the considerable variations in scale and appearance of objects, severely limit the potential of the existing deep learning approaches. Addressing such issues represents a promising research field in the remote sensing community, which paves the way for scene-level landscape pattern analysis and decision making. In this paper, we propose a Bilateral Awareness Network which contains a dependency path and a texture path to fully capture the long-range relationships and fine-grained details in VFR images. Specifically, the dependency path is conducted based on the ResT, a novel Transformer backbone with memory-efficient multi-head self-attention, while the texture path is built on the stacked convolution operation. In addition, using the linear attention mechanism, a feature aggregation module is designed to effectively fuse the dependency features and texture features. Extensive experiments conducted on the three large-scale urban scene image segmentation datasets, i.e., ISPRS Vaihingen dataset, ISPRS Potsdam dataset, and UAVid dataset, demonstrate the effectiveness of our BANet. Specifically, a 64.6% mIoU is achieved on the UAVid dataset.

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