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Multi-scale Adaptive Feature Fusion Network for Semantic Segmentation in Remote Sensing Images

Ronghua Shang, Jiyu Zhang, Licheng Jiao, Yangyang Li, Naresh Marturi, Rustam Stolkin
2020 Remote Sensing  
semantic segmentation in remote sensing images.  ...  Handling this problem through multi-scale context extraction and efficient fusion of multi-scale features, in this paper we present an end-to-end multi-scale adaptive feature fusion network (MANet) for  ...  Acknowledgments: We are very grateful for the dataset provided by ISPRS. Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12050872 fatcat:wgtmjsyilncthpph5cm43tmi5y

High-Resolution Remote Sensing Image Segmentation Framework Based on Attention Mechanism and Adaptive Weighting

Yifan Liu, Qigang Zhu, Feng Cao, Junke Chen, Gang Lu
2021 ISPRS International Journal of Geo-Information  
Semantic segmentation has been widely used in the basic task of extracting information from images.  ...  In this paper, an Adaptive Multi-Scale Module (AMSM) and Adaptive Fuse Module (AFM) are proposed to solve these two problems.  ...  In particular, on behalf of all the authors, I would like to thank Zhang Shuaishuai again for vividly providing me with ideas for revising the paper and replying to the reviewers' opinions during the review  ... 
doi:10.3390/ijgi10040241 fatcat:wqkht2oavzesngpnlpgrhjh75y

Dual Attention Feature Fusion and Adaptive Context for Accurate Segmentation of Very High-Resolution Remote Sensing Images

Hao Shi, Jiahe Fan, Yupei Wang, Liang Chen
2021 Remote Sensing  
Land cover classification of high-resolution remote sensing images aims to obtain pixel-level land cover understanding, which is often modeled as semantic segmentation of remote sensing images.  ...  Concretely, when fusing multi-level features, we utilize the dual attention feature fusion module based on both spatial and channel attention mechanisms to relieve the influence of the large gap, and further  ...  [27] introduce the global convolutional network to capture different resolutions by extracting multi-scale features for better results on remotely sensed images. Li et al.  ... 
doi:10.3390/rs13183715 fatcat:k2m2lwgik5e65kmvfphbw3fify

Top-Down Pyramid Fusion Network for High-Resolution Remote Sensing Semantic Segmentation

Yuhang Gu, Jie Hao, Bing Chen, Hai Deng
2021 Remote Sensing  
In recent years, high-resolution remote sensing semantic segmentation based on data fusion has gradually become a research focus in the field of land classification, which is an indispensable task of a  ...  Experimental results show that the network proposed in this paper not only notably improves the segmentation accuracy, but also reduces the complexity of the multi-source semantic segmentation model.  ...  Acknowledgments: The authors would like to thank the anonymous reviewers for their very competent comments and helpful suggestions.  ... 
doi:10.3390/rs13204159 fatcat:bmqjmgalbbcq7n6xrxojkiojcy

A Multi-Level Feature Fusion Network for Remote Sensing Image Segmentation

Sijun Dong, Zhengchao Chen
2021 Sensors  
By aiming to achieve a large difference in the scale of the target objects in remote sensing images and achieving a poor recognition result for small objects, a multi-level feature fusion solution is proposed  ...  This study proposes a multi-level feature fusion network (MFNet) that can integrate the multi-level features in the backbone to obtain different types of image information.  ...  In this regard, multi-scale feature fusion is the focus of this experiment, which applies multi-scale feature fusion in remote sensing image segmentation [3] .  ... 
doi:10.3390/s21041267 pmid:33578885 pmcid:PMC7916606 fatcat:i5khqddy3ngq3bd7lwpgqmvofu

Multi-Modality and Multi-Scale Attention Fusion Network for Land Cover Classification from VHR Remote Sensing Images

Tao Lei, Linze Li, Zhiyong Lv, Mingzhe Zhu, Xiaogang Du, Asoke K. Nandi
2021 Remote Sensing  
To address the problem, we proposed a multi-modality and multi-scale attention fusion network for land cover classification from VHR remote sensing images.  ...  Second, a novel multi-scale spatial context enhancement module was introduced to improve feature fusion, which solves the problem of a large-scale variation of objects in remote sensing images, and captures  ...  of VHR remote sensing images.  ... 
doi:10.3390/rs13183771 fatcat:4szzb3krkbh2pnqgozxuvodgx4

Multi-Resolution Supervision Network with an Adaptive Weighted Loss for Desert Segmentation

Lexuan Wang, Liguo Weng, Min Xia, Jia Liu, Haifeng Lin
2021 Remote Sensing  
Desert segmentation of remote sensing images is the basis of analysis of desert area.  ...  The multi-scale fusion method is widely used in the existing deep learning segmentation models to solve the above problems.  ...  Multi-Resolution Supervision Network In the existing remote sensing image segmentation methods, the feature fusion model is often used to extract multi-scale features and preserve spatial details [31]  ... 
doi:10.3390/rs13112054 fatcat:sylniany5rdujdwkkjfonwsela

Scale-aware Neural Network for Semantic Segmentation of Multi-resolution Remote Sensing Images [article]

Libo Wang, Ce Zhang, Rui Li, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson
2021 arXiv   pre-print
To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for semantic segmentation of MSR remotely sensed imagery.  ...  Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis.  ...  Acknowledgements: The authors are very grateful to the many people who helped to comment on the article, and the Large Scale Environment Remote Sensing Platform (Facility No. 16000009, 16000011, 16000012  ... 
arXiv:2103.07935v4 fatcat:lyfg7cjcwzgg3huswrfc6q2jci

Scale-Aware Neural Network for Semantic Segmentation of Multi-Resolution Remote Sensing Images

Libo Wang, Ce Zhang, Rui Li, Chenxi Duan, Xiaoliang Meng, Peter M. Atkinson
2021 Remote Sensing  
To bridge these gaps, in this paper, we propose a novel scale-aware neural network (SaNet) for the semantic segmentation of MSR remotely sensed imagery.  ...  Assigning geospatial objects with specific categories at the pixel level is a fundamental task in remote sensing image analysis.  ...  In this paper, we propose a novel scale-aware neural network (SaNet) for the semantic segmentation of MSR remotely sensed images.  ... 
doi:10.3390/rs13245015 fatcat:aue3dhriabdf7h5gzatpuvuhly

Improved-Flow Warp Module for Remote Sensing Semantic Segmentation [article]

Yinjie Zhang, Yi Liu, Wei Guo
2022 arXiv   pre-print
In this letter, we proposed a new module, called improved-flow warp module (IFWM), to adjust semantic feature maps across different scales for remote sensing semantic segmentation.  ...  Remote sensing semantic segmentation aims to assign automatically each pixel on aerial images with specific label.  ...  ACKNOWLEDGMENT The numerical calculations in this paper have been done on the supercomputing system in the Supercomputing Center of Wuhan University.  ... 
arXiv:2205.04160v1 fatcat:2pr4op5xuneg7g7eia3w3iye3e

Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks

Nicolas Audebert, Bertrand Le Saux, Sébastien Lefèvre
2018 ISPRS journal of photogrammetry and remote sensing (Print)  
Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling.  ...  In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data.  ...  Acknowledgements The Vaihingen dataset was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [11] : http: //www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html.  ... 
doi:10.1016/j.isprsjprs.2017.11.011 fatcat:et734k3y3vhe3frej7hulxga6a

Beyond RGB: Very High Resolution Urban Remote Sensing With Multimodal Deep Networks [article]

Nicolas Audebert , Sébastien Lefèvre
2017 arXiv   pre-print
Especially, we study how deep fully convolutional networks can be adapted to deal with multi-modal and multi-scale remote sensing data for semantic labeling.  ...  In this work, we investigate various methods to deal with semantic labeling of very high resolution multi-modal remote sensing data.  ...  Acknowledgements The Vaihingen dataset was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [11]: http: //www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html.  ... 
arXiv:1711.08681v1 fatcat:ndaguoumsrdwholmyf6ru56xay

Attention Guided Encoder-Decoder Network with Multi-scale Context Aggregation for Land Cover Segmentation

Shuyang Wang, Xiaodong Mu, Dongfang Yang, Hao He, Peng Zhao
2020 IEEE Access  
ACKNOWLEDGMENT The authors would like to thank the state key laboratory LIESMARS of Wuhan university for providing the GF-2 dataset used in the experiments.  ...  It shows that multi-scale fusion and attention mechanisms improve the segmentation results of remote sensing images.  ...  Some studies have applied attention networks to the semantic segmentation of remote sensing images. Pan et al.  ... 
doi:10.1109/access.2020.3040862 fatcat:utjwwdhrqjbvxjfakj2neiax5q

MSST-Net: A Multi-Scale Adaptive Network for Building Extraction from Remote Sensing Images Based on Swin Transformer

Wei Yuan, Wenbo Xu
2021 Remote Sensing  
The network model proposed in this paper is a multi-scale adaptive network model that pays more attention to the global features for remote sensing segmentation.  ...  The segmentation of remote sensing images by deep learning technology is the main method for remote sensing image interpretation.  ...  Acknowledgments: We would like to thank the anonymous reviewers for their constructive and valuable suggestions on the earlier drafts of this manuscript.  ... 
doi:10.3390/rs13234743 fatcat:etkt7bex5jenbe6hprwpf63pnq

Accurate Instance Segmentation for Remote Sensing Images via Adaptive and Dynamic Feature Learning

Feng Yang, Xiangyue Yuan, Jie Ran, Wenqiang Shu, Yue Zhao, Anyong Qin, Chenqiang Gao
2021 Remote Sensing  
The cross-scale adaptive fusion (CSAF) module introduces a novel multi-scale feature fusion mechanism to enhance the capability of the model to detect and segment objects with noticeable size variation  ...  In this paper, we design an end-to-end multi-category instance segmentation network for HRSIs, where three new modules based on adaptive and dynamic feature learning are proposed to address the above issues  ...  Conclusions In this paper, we design an end-to-end multi-category instance segmentation network for high-resolution remote sensing images aiming at address the problems of the huge scale variation, arbitrary  ... 
doi:10.3390/rs13234774 fatcat:iytnatx4jvaltkdmv76au7lwxm
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