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Remote Sensing Image Ship Detection under Complex Sea Conditions Based on Deep Semantic Segmentation

Yantong Chen, Yuyang Li, Junsheng Wang, Weinan Chen, Xianzhong Zhang
2020 Remote Sensing  
Based on the Resnet architecture, the remote sensing image is roughly segmented using a deep convolution neural network as the input.  ...  In this paper, an end-to-end convolution neural network method is introduced that combines a deep convolution neural network with a fully connected conditional random field.  ...  Conflicts of Interest: The authors declare that they have no conflicts of interest. Remote Sens. 2020, 12, 625  ... 
doi:10.3390/rs12040625 fatcat:pur5sb5555gh3ommnk5ljuadeu

Ship Detection under Complex Backgrounds Based on Accurate Rotated Anchor Boxes from Paired Semantic Segmentation

Xiaowu Xiao, Zhiqiang Zhou, Bo Wang, Linhao Li, Lingjuan Miao
2019 Remote Sensing  
It is still challenging to effectively detect ship objects in optical remote-sensing images with complex backgrounds.  ...  To avoid the above problems, in this paper we design a paired semantic segmentation network to generate more accurate rotated anchors with smaller numbers.  ...  For generating anchors via semantic segmentation, a direct and natural idea is to segment the whole ship in the remote-sensing image, and then we can take the rotated minimum bounding box of ship segmentation  ... 
doi:10.3390/rs11212506 fatcat:dngcvt57nrd55lyhumfyurpu4i

DEVELOPING AN EFFECTIVE MODEL FOR THE SEMANTIC SEGMENTATION OF REMOTE SENSING IMAGERY

Muazu Aminu Aliyu, Souley Boukari, Abdullahi Gamsha Madaki
2021 IJARCCE  
This paper introduces an effective semantic segmentation of satellite imagery using 3D-Unet.  ...  This work underline that most RS and DL segmentation can be enhanced using DL models.  ...  DCNN segmentation frameworks via 3D U-Net convolutional neural network to perform semantic segmentation of a multispectral image with seven channels.  ... 
doi:10.17148/ijarcce.2021.101207 fatcat:2ulbb3sbmbchzgnfulprbr7e2a

2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14

2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, JSTARS 2021 9609-9623 STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation.  ...  ., +, JSTARS 2021 11655-11668 STransFuse: Fusing Swin Transformer and Convolutional Neural Network for Remote Sensing Image Semantic Segmentation.  ... 
doi:10.1109/jstars.2022.3143012 fatcat:dnetkulbyvdyne7zxlblmek2qy

Front Matter: Volume 11430

Zhenbing Liu, Jayaram K. Udupa, Nong Sang, Yuehuan Wang
2020 MIPPR 2019: Pattern Recognition and Computer Vision  
Publication of record for individual papers is online in the SPIE Digital Library. SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model.  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  -93] 11430 1U A new aircraft classification algorithm based on sum pooling feature with remote sensing image [11430-94] 11430 1V Crop extraction based on ultra-simple neural network modeling in  ... 
doi:10.1117/12.2565844 fatcat:kapfwn4xajdt3jzin3mks33oam

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery

Zhuo Zheng, Yanfei Zhong, Junjue Wang, Ailong Ma
2020 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)  
high spatial resolution (HSR) remote sensing imagery.  ...  In this paper, we argue that the problems lie on the lack of foreground modeling and propose a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based  ...  Convolutional neural network (CNN), as structured feature representation framework in deep learning, has been explored for semantic segmentation via patch-wise classification [11, 17, 19, 18, 37] .  ... 
doi:10.1109/cvpr42600.2020.00415 dblp:conf/cvpr/ZhengZWM20 fatcat:lfl2xo7zarfvjase5e5tthwwwi

Maritime Semantic Labeling of Optical Remote Sensing Images with Multi-Scale Fully Convolutional Network

Haoning Lin, Zhenwei Shi, Zhengxia Zou
2017 Remote Sensing  
In this paper, we propose to address the sea-land segmentation and ship detection at the same time, with a deep neural network, in a semantic labeling perspective.  ...  Two of the most important tasks in understanding remote sensing images that is maritime-related, would be sea-land segmentation and ship detection.  ...  Conflicts of Interest: The authors declare no conflict of interest. Remote Sens. 2017, 9, 480  ... 
doi:10.3390/rs9050480 fatcat:wzx5zq56rffybozlz4u3ieahla

CISP-BMEI 2020 TOC

2020 2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)  
........................................................................175 Hyperspectral Remote Sensing Image Segmentation Based on Fuzzy Deep Convolutional Neural Network XiaoyingWei, Yanhua Cao  ...  Graph Theory Jing Ai, Tiantian Liu, Kexin Wang, Jian Zhang, Tianlin Huang ... ....407 Sentence Modeling via Graph Construction and Graph Neural Networks for Semantic Textual Similarity Ke Zhou, Ke Xu  ... 
doi:10.1109/cisp-bmei51763.2020.9263536 fatcat:7ulpvhnt35d2lg5dwzu4kexley

HQ-ISNet: High-Quality Instance Segmentation for Remote Sensing Imagery

Hao Su, Shunjun Wei, Shan Liu, Jiadian Liang, Chen Wang, Jun Shi, Xiaoling Zhang
2020 Remote Sensing  
In this article, a novel instance segmentation approach of HR remote sensing imagery based on Cascade Mask R-CNN is proposed, which is called a high-quality instance segmentation network (HQ-ISNet).  ...  Instance segmentation in high-resolution (HR) remote sensing imagery is one of the most challenging tasks and is more difficult than object detection and semantic segmentation tasks.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/rs12060989 fatcat:fataurpmhfbh3n43l63cdedo6a

Special Section Guest Editorial: Feature and Deep Learning in Remote Sensing Applications

John E. Ball, Derek T. Anderson, Chee Seng Chan
2018 Journal of Applied Remote Sensing  
Most articles used or extended convolutional neural networks (CNNs) and were application oriented, with a few providing new deep learning models and modules.  ...  The most famous of these revolts is deep learning, a resurrection of neural networks. The crux of this approach is that machines are better than humans at tasks like those outlined above.  ...  Yao et al. in "Ship detection in optical remote sensing images based on deep convolutional neural networks" is a complicated problem due to the small size of ships and interference from clouds, waves,  ... 
doi:10.1117/1.jrs.11.042601 fatcat:pq3xg2sggfdtljjs3hrmp7tzdm

Foreground-Aware Relation Network for Geospatial Object Segmentation in High Spatial Resolution Remote Sensing Imagery [article]

Zhuo Zheng, Yanfei Zhong, Junjue Wang, Ailong Ma
2020 arXiv   pre-print
high spatial resolution (HSR) remote sensing imagery.  ...  In this paper, we argue that the problems lie on the lack of foreground modeling and propose a foreground-aware relation network (FarSeg) from the perspectives of relation-based and optimization-based  ...  Convolutional neural network (CNN), as structured feature representation framework in deep learning, has been explored for semantic segmentation via patch-wise classification [11, 17, 19, 18, 37] .  ... 
arXiv:2011.09766v1 fatcat:ylngdx3fqjcm3d7hie25wr2w4e

Geospatial Object Detection in Remote Sensing Imagery Based on Multiscale Single-Shot Detector with Activated Semantics

Shiqi Chen, Ronghui Zhan, Jun Zhang
2018 Remote Sensing  
Geospatial object detection from high spatial resolution (HSR) remote sensing imagery is a heated and challenging problem in the field of automatic image interpretation.  ...  Since the region-based convolutional neural network (R-CNN) [25] has made breakthroughs on the PASCAL VOC dataset, the procedure consisting of the region proposal-based extractor with a detection network  ...  Conflicts of Interest: The authors declare no conflicts of interest. Remote Sens. 2018, 10, 820  ... 
doi:10.3390/rs10060820 fatcat:32n6fpvqx5fyvoh3viizllp76q

Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images

Lianfa Li
2019 Remote Sensing  
Semantic segmentation is a fundamental means of extracting information from remotely sensed images at the pixel level.  ...  The studies of deep learning for semantic segmentation of remotely sensed images are limited.  ...  Conflicts of Interest: The author declares no conflict of interest.  ... 
doi:10.3390/rs11182142 fatcat:fcwid63pqfampp5ryrvlsfdbde

An Improved Swin Transformer-Based Model for Remote Sensing Object Detection and Instance Segmentation

Xiangkai Xu, Zhejun Feng, Changqing Cao, Mengyuan Li, Jin Wu, Zengyan Wu, Yajie Shang, Shubing Ye
2021 Remote Sensing  
A convolutional neural network (CNN) has shown defects in the object detection of remote sensing images.  ...  Remote sensing image object detection and instance segmentation are widely valued research fields.  ...  Acknowledgments: The authors thank the team of optical sensing and measurement of Xidian University for their help.  ... 
doi:10.3390/rs13234779 fatcat:qclznhggczcojcc5okxgypyqfm

2020 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 13

2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., +, JSTARS 2020 5011-5026 BAS 4 Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images.  ...  ., +, JSTARS 2020 5847-5861 BAS 4 Net: Boundary-Aware Semi-Supervised Semantic Segmentation Network for Very High Resolution Remote Sensing Images.  ...  A New Deep-Learning-Based Approach for Earthquake-Triggered Landslide Detection From Single-Temporal RapidEye Satellite Imagery. Yi, Y., +, JSTARS 2020  ... 
doi:10.1109/jstars.2021.3050695 fatcat:ycd5qt66xrgqfewcr6ygsqcl2y
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