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Attention-Guided Label Refinement Network for Semantic Segmentation of Very High Resolution Aerial Orthoimages

Jianfeng Huang, Xinchang Zhang, Ying Sun, Qinchuan Xin
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
In this article, we propose an attention-guided label refinement network (ALRNet) for improved semantic labeling of VHR images.  ...  The recent applications of fully convolutional networks (FCNs) have shown to improve the semantic segmentation of very high resolution (VHR) remote-sensing images because of the excellent feature representation  ...  ACKNOWLEDGMENT The authors would like to thank the ISPRS for making the Vaihingen and Potsdam datasets available and organizing the semantic labeling contest.  ... 
doi:10.1109/jstars.2021.3073935 fatcat:lrzyxfg2ujba7hinlpogrsv6rq

Building Extraction in Very High Resolution Remote Sensing Imagery Using Deep Learning and Guided Filters

Yongyang Xu, Liang Wu, Zhong Xie, Zhanlong Chen
2018 Remote Sensing  
so on, to label pixels, especially for the VHR remote sensing imagery [40, 41] .  ...  The reason for this is that although the resolution of remote sensing images have improved, which has been helpful to detect and distinguish various objects on the ground, these improvements have made  ...  Fully convolutional networks are adapted as effective tools for the semantic labelling of high-resolution remote sensing data.  ... 
doi:10.3390/rs10010144 fatcat:y7nwaxuv2vb5blsiuhjr2hn7ma

Survival study on cyclone prediction methods with remote sensing images

B. Suresh Kumar, D. Jayaraj
2022 International Journal of Health Sciences  
Image classification has large interest for many decades in the remote sensing communities to reduce injure caused by cyclones.  ...  Many existing works have been designed in cyclone prediction for attaining better prediction accuracy. But, it is difficult to enhance the cyclone prediction accuracy with minimum time complexity.  ...  An attention mechanism-based deep supervision network (ADS-Net) was designed in [3] for change detection of bi-temporal remote sensing images.  ... 
doi:10.53730/ijhs.v6ns1.6668 fatcat:g4gdygi7l5el5eynoqgvebvmze

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

Kaihua Zhang, Haikuo Shen
2022 Remote Sensing  
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  ...  Optical remote sensing image target detection is an important method for accomplishing tasks, such as land use, urban planning, traffic guidance, military monitoring and maritime rescue.  ...  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

Semantic Attention and Scale Complementary Network for Instance Segmentation in Remote Sensing Images [article]

Tianyang Zhang, Xiangrong Zhang, Peng Zhu, Xu Tang, Chen Li, Licheng Jiao, Huiyu Zhou
2021 arXiv   pre-print
In this paper, we focus on the challenging multicategory instance segmentation problem in remote sensing images (RSIs), which aims at predicting the categories of all instances and localizing them with  ...  To address the above problems, we propose an end-to-end multi-category instance segmentation model, namely Semantic Attention and Scale Complementary Network, which mainly consists of a Semantic Attention  ...  training set, 9,581 for validation set and 19,377 for test set. 2) NWPU VHR-10 instance segmentation: The NWPU VHR-10 instance segmentation dataset [33] is an extension of the remote sensing object detection  ... 
arXiv:2107.11758v1 fatcat:qhskmizuubfnvlcoqntxa3smju

Aircraft Detection for Remote Sensing Image Based on Bidirectional and Dense Feature Fusion

Liming Zhou, Haoxin Yan, Chang Zheng, Xiaohan Rao, Yahui Li, Wencheng Yang, Junfeng Tian, Minghu Fan, Xianyu Zuo, Qiangqiang Yuan
2021 Computational Intelligence and Neuroscience  
However, the current object detection methods cause a series of problems when applied to the aircraft detection for the remote sensing image, for instance, the problems of low rate of detection accuracy  ...  To address the problems of low rate of detection accuracy and high rate of missed detection, an object detection method for remote sensing image based on bidirectional and dense feature fusion is proposed  ...  VHR remote sensing images and discussing the future challenges and opportunities in applying VHR remote sensing images in LCCD.  ... 
doi:10.1155/2021/7618828 pmid:34567103 pmcid:PMC8457952 fatcat:xbvvpn6z7bdqzalu2dfot42neq

End-to-End Change Detection for High Resolution Satellite Images Using Improved UNet++

Peng, Zhang, Guan
2019 Remote Sensing  
Due to the great advantages in deep feature representation and nonlinear problem modeling, deep learning is becoming increasingly popular to solve CD tasks in remote-sensing community.  ...  Firstly, co-registered image pairs are concatenated as an input for the improved UNet++ network, where both global and fine-grained information can be utilized to generate feature maps with high spatial  ...  This means that our CD method might capture multi-scale object changes, which is critical for detecting objects with sharp changes in sizes and scales on VHR satellite images.  ... 
doi:10.3390/rs11111382 fatcat:jmdu2ygmqfhjdalfzg63viztey

Object Detection in Aerial Images Using a Multiscale Keypoint Detection Network

Jinhe Su, Jiajiao Liao, Dujuan Gu, Zongyue Wang, Guorong Cai
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This configuration significantly limits the ability to detect multiscale objects in aerial scenes.  ...  In this article, a novel network, called the multiscale keypoint detection network (MKD-Net), is proposed to address these challenges.  ...  for benchmarking object detection in remote sensing imagery.  ... 
doi:10.1109/jstars.2020.3044733 fatcat:smf7vg7bw5bczd4gxawsykahae

Semantic-Guided Attention Refinement Network for Salient Object Detection in Optical Remote Sensing Images

Zhou Huang, Huaixin Chen, Biyuan Liu, Zhixi Wang
2021 Remote Sensing  
Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various  ...  This paper explores the inherent properties of multi-level features to develop a novel semantic-guided attention refinement network (SARNet) for SOD of NSI.  ...  Acknowledgments: The authors would like to thank Dengping Fan for his guidance and help in this work. Conflicts of Interest: The authors declare no conflicts of interest.  ... 
doi:10.3390/rs13112163 fatcat:syufwr2xhnh3nchgftmjile6hi

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS June 2019 1882-1897 Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation.  ...  ., +, JSTARS June 2019 1882-1897 Densely Based Multi-Scale and Multi-Modal Fully Convolutional Networks for High-Resolution Remote-Sensing Image Semantic Segmentation.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

Semantic labeling in very high resolution images via a self-cascaded convolutional neural network

Yongcheng Liu, Bin Fan, Lingfeng Wang, Jun Bai, Shiming Xiang, Chunhong Pan
2017 ISPRS journal of photogrammetry and remote sensing (Print)  
Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications.  ...  For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet).  ...  The authors also wish to thank the ISPRS for providing the research community with the awesome challenge datasets, and thank Markus Gerke for the support of submissions.  ... 
doi:10.1016/j.isprsjprs.2017.12.007 fatcat:2dltz3ojdvatjj5qsqkhz7cyb4

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  ...  Experiments on ISPRS Vaihingen and Potsdam datasets demonstrate the effectiveness of the proposed method for the semantic segmentation of aerial images.  ...  ACKNOWLEDGEMENTS We would like to acknowledge the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) (http:// for providing the Vaihingen  ... 
arXiv:2006.05848v2 fatcat:pk4y2g4wfvbhvdlmwtyhdyvra4

Deep Learning and Earth Observation to Support the Sustainable Development Goals [article]

Claudio Persello, Jan Dirk Wegner, Ronny Hänsch, Devis Tuia, Pedram Ghamisi, Mila Koeva, Gustau Camps-Valls
2021 arXiv   pre-print
in Earth observation.  ...  This paper reviews current deep learning approaches for Earth observation data, along with their application towards monitoring and achieving the SDGs most impacted by the rapid development of deep learning  ...  from various sources of remotely sensed images, in situ data and models.  ... 
arXiv:2112.11367v1 fatcat:7eve5dr45vcublfqyzzrccuvxa

Coarse-to-Fine Satellite Images Change Detection Framework via Boundary-Aware Attentive Network

Yi Zhang, Shizhou Zhang, Ying Li, Yanning Zhang
2020 Sensors  
To deal with these problems, we design a coarse-to-fine detection framework via a boundary-aware attentive network with a hybrid loss to detect the change in high resolution satellite images.  ...  Existing approaches based on deep learning frameworks have achieved good performance for the task of change detection on satellite images.  ...  Conclusions In this paper, we propose a BA 2 Net with a hybrid loss to detect changes in VHR satellite images.  ... 
doi:10.3390/s20236735 pmid:33255688 fatcat:ij46sxbcqnfxtixx6653n4p3ey

DeepWindow: Sliding Window Based on Deep Learning for Road Extraction from Remote Sensing Images

Renbao Lian, Liqin Huang
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Inspired by the work of human pose estimation, we propose DeepWindow, a novel method to automatically extract the road network from remote sensing images.  ...  In our method, the CNN model is trained by point annotations, which greatly reduces the training costs comparing to those in semantic model training.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their valuable comments.  ... 
doi:10.1109/jstars.2020.2983788 fatcat:xz7miigswzbhzn67ctfkz53dtm
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