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An Attention-Fused Network for Semantic Segmentation of Very-High-Resolution Remote Sensing Imagery [article]

Xuan Yang, Shanshan Li, Zhengchao Chen, Jocelyn Chanussot, Xiuping Jia, Bing Zhang, Baipeng Li, Pan Chen
2021 arXiv   pre-print
Semantic segmentation is an essential part of deep learning. In recent years, with the development of remote sensing big data, semantic segmentation has been increasingly used in remote sensing.  ...  Deep convolutional neural networks (DCNNs) face the challenge of feature fusion: very-high-resolution remote sensing image multisource data fusion can increase the network's learnable information, which  ...  Acknowledgments The authors thank the International Society for Photogrammetry and Remote Sensing (ISPRS) for making the Vaihingen dataset and the Potsdam dataset available online.  ... 
arXiv:2105.04132v1 fatcat:75mqxagnqbhb7hp4q2qhfc2ugi


H. R. Hosseinpoor, F. Samadzadegan
2019 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
The results of this paper show that the proposed architecture improves building extraction in very high resolution remote sensing images compared to previous models.  ...  The precise extraction of buildings from remote sensing data has become a significant topic and has received much attention in recent years.  ...  Fully convolutional neural networks (F-CNNs) are adapted as effective tools for the semantic labelling of high-resolution remote sensing data.  ... 
doi:10.5194/isprs-archives-xlii-4-w18-507-2019 fatcat:kilsiilhurbypenfaziiv74gqe

Editorial for Special Issue: "Remote Sensing based Building Extraction"

Mohammad Awrangjeb, Xiangyun Hu, Bisheng Yang, Jiaojiao Tian
2020 Remote Sensing  
Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications [...]  ...  Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2020, 12, 549  ...  very constructive feedback.  ... 
doi:10.3390/rs12030549 fatcat:r2sqr6gem5bntci6kksgvk77mi

Semantic Segmentation Based Remote Sensing Data Fusion on Crops Detection

Jose Pena, Yumin Tan, Wuttichai Boonpook
2019 Journal of Computer and Communications  
In the paper, a method of fusing multi-source remote sensing images with convolution neural networks (CNN) for semantic segmentation is proposed and applied to identify crops.  ...  Venezuelan Remote Sensing Satellite-2 (VRSS-2) and the high-resolution of Google Earth (GE) imageries have been used and more than 1000 sample sets have been collected for supervised learning process.  ...  Google Earth (GE) provides an open data source with very high spatial resolution, which represents a very good alternative for crops detection.  ... 
doi:10.4236/jcc.2019.77006 fatcat:lnxfggpnarbjjhx736sqhxevoa

Road Extraction from High-Resolution Remote Sensing Imagery Using Deep Learning

Yongyang Xu, Zhong Xie, Yaxing Feng, Zhanlong Chen
2018 Remote Sensing  
Owing to the advancements in the field of high-resolution remote sensing, and the success of semantic segmentation success using deep learning in computer version, extracting the road network from high-resolution  ...  The aim of this work is to propose a novel road extraction method that can efficiently extract the road network from remote sensing imagery with local and global information.  ...  The authors also thank Mingyu Xie (University of California, Santa Barbara) for helping improve the language. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs10091461 fatcat:2vp4z2jsnvggrarwbf4khexb2a

Building Extraction Based on U-Net with an Attention Block and Multiple Losses

Mingqiang Guo, Heng Liu, Yongyang Xu, Ying Huang
2020 Remote Sensing  
Semantic segmentation of high-resolution remote sensing images plays an important role in applications for building extraction.  ...  To extract buildings with high accuracy, we propose a multiloss neural network based on attention.  ...  of the model on ultra-high-resolution remote sensing images.  ... 
doi:10.3390/rs12091400 fatcat:q36r25pc7ncy3bhbmaj7knfizi

Hierarchical self-attention embedded neural network with dense connection for remote-sensing image semantic segmentation

Chunhua Li, Xin Li, Runliang Xia, Tao Li, Xin Lyu, Yao Tong, Liancheng Zhao, Xinyuan Wang
2021 IEEE Access  
Further study is expected to follow two directions: One is low-shot learning approaches for very high-resolution remote-sensing imagery.  ...  Moreover, the recent proposed ResUNet-a [46] provides a reliable framework for semantic segmentation of monotemporal very high-resolution aerial images.  ... 
doi:10.1109/access.2021.3111899 fatcat:ljtv5cnlrfaylgypyigav7jxki

Hybridizing Cross-Level Contextual and Attentive Representations for Remote Sensing Imagery Semantic Segmentation

Xin Li, Feng Xu, Runliang Xia, Xin Lyu, Hongmin Gao, Yao Tong
2021 Remote Sensing  
Semantic segmentation of remote sensing imagery is a fundamental task in intelligent interpretation.  ...  Therefore, a remote sensing imagery semantic segmentation neural network, named HCANet, is proposed to generate representative and discriminative representations for dense predictions.  ...  Additionally, a remarkable neural network named ResUNet-a provided a framework for the task of semantic segmentation of monotemporal very high-resolution aerial images.  ... 
doi:10.3390/rs13152986 fatcat:l4ap4w66nbg3pdyhfkpc5dwdsq

ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features

Xiuwei Zhang, Jiaojiao Jin, Zeze Lan, Chunjiang Li, Minhao Fan, Yafei Wang, Xin Yu, Yanning Zhang
2020 Remote Sensing  
Meanwhile, a semantic segmentation deep convolution neural network by fusing positional and channel-wise attentive features is proposed for river ice semantic segmentation, named ICENET.  ...  So, we focused on river ice segmentation based on UAV remote sensing images.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12020221 fatcat:yfhhhlsqkjhglhcx3zmofqk2ja

Multiscale Semantic Feature Optimization and Fusion Network for Building Extraction Using High-Resolution Aerial Images and LiDAR Data

Qinglie Yuan, Helmi Zulhaidi Mohd Shafri, Aidi Hizami Alias, Shaiful Jahari Hashim
2021 Remote Sensing  
To address the above problems, this paper proposes an FCN framework based on the residual network and provides the training pattern for multi-modal data combining the advantage of high-resolution aerial  ...  A semantic guided spatial attention mechanism is introduced to refine shallow features and alleviate the semantic gap. Finally, hierarchical features are fused via the feature pyramid network.  ...  Proposed Method The developed network based on residual FCNs aims to build the encoder-decoder architecture using multi-modal high-spatial resolution remote sensing data for building extraction.  ... 
doi:10.3390/rs13132473 fatcat:thupwpioxvbthn65i2q2bkxkqu

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

Xuerui Niu, Qiaolin Zeng, Xiaobo Luo, Liangfu Chen
2022 Remote Sensing  
The semantic segmentation of fine-resolution remotely sensed images is an urgent issue in satellite image processing.  ...  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.  ...  An fusion network for semantic segmentation of very-high-resolution remote sensing imagery. ISPRS J. Photogramm. Remote Sens. 2021, 177, 238–262. [CrossRef] 8.  ... 
doi:10.3390/rs14010215 fatcat:gfnv5kbdk5a4tepwcwd7tmiihe

B-FGC-Net: A Building Extraction Network from High Resolution Remote Sensing Imagery

Yong Wang, Xiangqiang Zeng, Xiaohan Liao, Dafang Zhuang
2022 Remote Sensing  
B-FGC-Net is an effective and recommended method for extracting buildings from high resolution remote sensing images.  ...  Deep learning (DL) shows remarkable performance in extracting buildings from high resolution remote sensing images.  ...  Semantic segmentation of urban buildings using a high-resolution network (Hrnet) with channel and spatial attention gates. Remote Sens. 2021, 13, 3087. [CrossRef] 24.  ... 
doi:10.3390/rs14020269 fatcat:loucqgxrdbd6zi7w25iotaq2ry


C. Ayala, C. Aranda, M. Galar
2021 ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In the last decade, promising attempts have been carried out to fully-automatize the extraction of road networks from remote sensing imagery.  ...  For that purpose, a new deep learning architecture which combines semantic segmentation and super-resolution techniques is proposed.  ...  ACKNOWLEDGEMENTS Christian Ayala was partially supported by the Goverment of Navarra under the industrial PhD program 2020 reference 0011-1408-2020-000008.  ... 
doi:10.5194/isprs-annals-v-3-2021-9-2021 fatcat:fd2rcthxmnhslebhybz7krqvmm

Building Extraction in Very High Resolution Imagery by Dense-Attention Networks

Hui Yang, Penghai Wu, Xuedong Yao, Yanlan Wu, Biao Wang, Yongyang Xu
2018 Remote Sensing  
Building extraction from very high resolution (VHR) imagery plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications  ...  The DAN contains an encoder part and a decoder part which are separately composed of lightweight DenseNets and a spatial attention fusion module.  ...  Acknowledgments: The authors thank the ISPRS for making the Potsdam datasets available and organizing the semantic labeling challenge.  ... 
doi:10.3390/rs10111768 fatcat:mosiwkpzs5hbvjc7fuxdq4brqi

Boundary-Assisted Learning for Building Extraction from Optical Remote Sensing Imagery

Sheng He, Wanshou Jiang
2021 Remote Sensing  
Deep learning methods have been shown to significantly improve the performance of building extraction from optical remote sensing imagery.  ...  In this paper, we propose a novel fully convolutional network (FCN) for accurately extracting buildings, in which a boundary learning task is embedded to help maintain the boundaries of buildings.  ...  The authors would also like to express their gratitude to the editors and reviewers for their constructive and helpful comments for the substantial improvement of this paper.  ... 
doi:10.3390/rs13040760 fatcat:6qsdplftwrfjpm4caslvdq6syu
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