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MSF-Net: A multiscale supervised fusion network for building change detection in high-resolution remote sensing images

Jiahao Chen, Junfu Fan, Mengzhen Zhang, Yuke Zhou, Chen Shen
2022 IEEE Access  
Building change detection is a primary task in the application of remote sensing images, especially in city land resource management and urbanization process assesment.  ...  To fill these gaps" this study proposes a multiscale supervised fusion network (MSF-Net), which is an attention mechanismbased approach for building change detection using bi-temporal high-resolution satellite  ...  Conclusions In this paper we proposed a multiscale supervised fusion network based on the attention mechanism for building change detection in high-resolution remote sensing images.  ... 
doi:10.1109/access.2022.3160163 fatcat:fmh7uts5svfyznu2h55uenve5y

Multi-Stage Feature Enhancement Pyramid Network for Detecting Objects in Optical Remote Sensing Images

Kaihua Zhang, Haikuo Shen
2022 Remote Sensing  
The results demonstrate that Multi-stage FEPN provides a new solution for the intelligent detection of targets in optical remote sensing images.  ...  The intelligent detection of objects in remote sensing images has gradually become a research hotspot for experts from various countries, among which optical remote sensing images are considered to be  ...  that the Multi-stage Feature Enhance Pyramid Network is more effective in the optical remote sensing image multi-object detection task.  ... 
doi:10.3390/rs14030579 doaj:66176a268b9d4a17a6713867fe9d70a0 fatcat:vjhy4lcwmndanovikrdutpob7m

ESPFNet: An Edge-aware Spatial Pyramid Fusion Network for Salient Shadow Detection in Aerial Remote Sensing Images

Shuang Luo, Huifang Li, Ruzhao Zhu, Yuting Gong, Huanfeng Shen
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this work, a novel edge-aware spatial pyramid fusion network (ESPFNet) under a multi-task learning framework is proposed for salient shadow detection in aerial remote sensing images.  ...  Index Terms-Multi-task learning, convolutional neural network, salient shadow detection, aerial remote sensing images.  ...  ACKNOWLEDGMENT The authors appreciate the editors and anonymous reviewers for their valuable suggestions.  ... 
doi:10.1109/jstars.2021.3066791 fatcat:76fvjk7zxbdrncwmoqaurgynta

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

Landslide Extraction from High-Resolution Remote Sensing Imagery Using Fully Convolutional Spectral–Topographic Fusion Network

Wei Xia, Jun Chen, Jianbo Liu, Caihong Ma, Wei Liu
2021 Remote Sensing  
In this study, comprehensive research was carried out on the landslide features of high-resolution remote sensing images on the Mangkam dataset.  ...  In this paper, a high-resolution remote sensing image classification method based on a fully convolutional network was used to extract the landslide information, thereby realizing the accurate extraction  ...  Acknowledgments: We are especially grateful to the Key Laboratory of Active Tectonics and Volcano, Institute of Geology, China Earthquake Administration for the inventory vector file of landslides triggered  ... 
doi:10.3390/rs13245116 fatcat:mrppmxgicbcdrkrheyptqf2oqi

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

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

FUSING MULTI-MODAL DATA FOR SUPERVISED CHANGE DETECTION

P. Ebel, S. Saha, X. X. Zhu
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
With the rapid development of remote sensing technology in the last decade, different modalities of remote sensing data recorded via a variety of sensors are now easily accessible.  ...  We further propose a novel Siamese architecture for fusion of SAR and optical observations for multi-modal change detection, which underlines the value of our newly gathered data.  ...  ACKNOWLEDGEMENTS The work is supported by the German Federal Ministry of Education and Research (BMBF) in the framework of the international future AI lab "AI4EO -Artificial Intelligence for Earth Observation  ... 
doi:10.5194/isprs-archives-xliii-b3-2021-243-2021 fatcat:qvenbuqr6fgolhr4m4t5nxxskq

Attention-Guided Multispectral and Panchromatic Image Classification

Cheng Shi, Yenan Dang, Li Fang, Zhiyong Lv, Huifang Shen
2021 Remote Sensing  
In the proposed method, a share-split network (SSNet) including a shared branch and multiple split branches performs feature extraction for each sensor image, where the shared branch learns basis features  ...  However, the general deep neural network-based multi-sensor classification method learns each sensor image separately, followed by a stacked concentrate for feature fusion.  ...  Acknowledgments: The authors would like to thank Deimos Imaging for acquiring and providing the data used in this paper, and the IEEE GRSS Image Analysis and Data Fusion Technical Committee.  ... 
doi:10.3390/rs13234823 fatcat:4u3o5lrinfagfpibatj473ty7u

SSDBN: A Single-Side Dual-Branch Network with Encoder–Decoder for Building Extraction

Yang Li, Hui Lu, Qi Liu, Yonghong Zhang, Xiaodong Liu
2022 Remote Sensing  
The dual-branch decoding module contains a deconvolution branch and a feature enhancement branch, which are responsible for capturing multi-scale information and enhancing high-level semantic details,  ...  In the field of building detection research, an accurate, state-of-the-art semantic segmentation model must be constructed to classify each pixel of the image, which has an important reference value for  ...  Building Dataset and WHU Satellite Dataset I (global cities) showed that the network is suitable for building extraction in remote sensing images.2.  ... 
doi:10.3390/rs14030768 fatcat:woyqnhe6wvbqtetq2oybioglpu

RepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images

Liming Zhou, Chang Zheng, Haoxin Yan, Xianyu Zuo, Yang Liu, Baojun Qiao, Yong Yang
2022 ISPRS International Journal of Geo-Information  
such as cars, in remote sensing images.  ...  Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case.  ...  We proposed a cross-layer converged network for small targets in optical remote sensing images. The network contains multi-scale cross-layer detection and feature fusion networks. 3.  ... 
doi:10.3390/ijgi11030158 fatcat:xsv7fqkiofesdczxaqxfmdwq44

A Context Feature Enhancement Network for Building Extraction from High-Resolution Remote Sensing Imagery

Jinzhi Chen, Dejun Zhang, Yiqi Wu, Yilin Chen, Xiaohu Yan
2022 Remote Sensing  
Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing.  ...  The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks.  ...  [41] proposed a method based on Transformer and YOLOv5 for real-time object detection on side-scan sonar remote sensing images.  ... 
doi:10.3390/rs14092276 fatcat:hd66snowone7dme2gv4gbxjfey

MAFF-Net: Multi-Attention Guided Feature Fusion Network for Change Detection in Remote Sensing Images

Jinming Ma, Gang Shi, Yanxiang Li, Ziyu Zhao
2022 Sensors  
A Multi-Attention Guided Feature Fusion Network (MAFF-Net) for CD tasks has been designed. The network enhances feature extraction and feature fusion by building different blocks.  ...  One of the most important tasks in remote sensing image analysis is remote sensing image Change Detection (CD), and CD is the key to helping people obtain more accurate information about changes on the  ...  Conclusions In this paper, we propose a novel feature fusion network for remote sensing image CD tasks.  ... 
doi:10.3390/s22030888 pmid:35161634 pmcid:PMC8838741 fatcat:jdcd3sjyjrdhvhso3lb3anwvei

Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation [article]

Xiang Li, Lingjing Wang, Yi Fang
2020 arXiv   pre-print
Instead of using a single-stream encoder-decoder network for semantic labeling, we design a separate decoder branch to predict the height map and use the DSM images as side supervision to train this newly  ...  Moreover, we develop a new geometry-aware convolution module that fuses the 3D geometric features from the height decoder branch and the 2D contextual features from the semantic segmentation branch.  ...  ACKNOWLEDGEMENTS We would like to acknowledge the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) (http:// www.ifp.uni-stuttgart.de/dgpf/DKEP-Allg.html) for providing the Vaihingen  ... 
arXiv:2006.05848v2 fatcat:pk4y2g4wfvbhvdlmwtyhdyvra4

ADS-Net:An Attention-Based deeply supervised network for remote sensing image change detection

Decheng Wang, Xiangning Chen, Mingyong Jiang, Shuhan Du, Bijie Xu, Junda Wang
2021 International Journal of Applied Earth Observation and Geoinformation  
To solve such problems, we propose an attention mechanism-based deep supervision network (ADS-Net) for the change detection of bi-temporal remote sensing images.  ...  A B S T R A C T Change detection technology is an important key to analyze remote sensing data and is of great significance for accurate comprehension of the earth's surface changes.  ...  We would like to express our gratitude to EditSprings (https://www. editsprings.com/) for the expert linguistic services provided.  ... 
doi:10.1016/j.jag.2021.102348 fatcat:7liht765zzggnbqio55iw5fjza
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