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SGMNet: Scene Graph Matching Network for Few-Shot Remote Sensing Scene Classification [article]

Baoquan Zhang, Shanshan Feng, Xutao Li, Yunming Ye, Rui Ye
<span title="2021-10-09">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To fully exploit these characteristics, we propose a novel scene graph matching-based meta-learning framework for FSRSSC, called SGMNet.  ...  Such unique characteristics are very beneficial for FSRSSC, which can effectively alleviate the scarcity issue of labeled remote sensing images since they can provide more refined descriptions for each  ...  To fully leverage these two characteristics (i.e., object cooccurrence and object spatial correlation) of remote sensing images for FSRSSC, in this paper, we propose a scene graph matching-based meta-learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.04494v1">arXiv:2110.04494v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u4wvek6ypzf4bdjtgoyydrscge">fatcat:u4wvek6ypzf4bdjtgoyydrscge</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211015034708/https://arxiv.org/pdf/2110.04494v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/05/28/0528b7ebaf13b3dfbd46a2aaf1da136135eb8f9a.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2110.04494v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Survey on Deep Learning-Driven Remote Sensing Image Scene Understanding: Scene Classification, Scene Retrieval and Scene-Guided Object Detection

Yating Gu, Yantian Wang, Yansheng Li
<span title="2019-05-23">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
To facilitate the sustainable progress of RSISU, this paper presents a comprehensive review of deep-learning-based RSISU methods, and points out some future research directions and potential applications  ...  RSISU includes the following sub-tasks: remote sensing image scene classification, remote sensing image scene retrieval, and scene-driven remote sensing image object detection.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app9102110">doi:10.3390/app9102110</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/oj3acgbmwnhzppxvvjbsn5cfzq">fatcat:oj3acgbmwnhzppxvvjbsn5cfzq</a> </span>
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Improved Attention Mechanism and Residual Network for Remote Sensing Image Scene Classification

Jiayuan Kong, Yurong Gao, Yanjun Zhang, Huimin Lei, Yao Wang, Hesheng Zhang
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Aiming at the problem that the accuracy of traditional remote sensing image classification model is not ideal, a classification method based on improved attention mechanism and residual network is proposed  ...  By optimizing the representation of the feature map from the spatial dimension and channel dimension of the feature map, more detailed image information is learned and image recognition errors are reduced  ...  To sum up, in order to further improve the effect of remote sensing image scene classification, this paper proposes a remote sensing image scene classification method based on improved attention mechanism  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3116968">doi:10.1109/access.2021.3116968</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ufbc7zp3wzgtrcsp2csyzvqvai">fatcat:ufbc7zp3wzgtrcsp2csyzvqvai</a> </span>
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Stack Attention-Pruning Aggregates Multiscale Graph Convolution Networks for Hyperspectral Remote Sensing Image Classification

Na Liu, Bin Zhang, Qiuhuan Ma, Qingqing Zhu, Xiaoling Liu
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
INDEX TERMS Hyperspectral remote sensing image classification, stack attention-pruning, multiscale graph convolution networks, longdistances joint interaction, multiscale spatial-temporal information,  ...  The hyperspectral remote sensing images are classified by traditional neural networks methods can achieve promising performance, but only operate on regular square regions with fixed.  ...  The classification method of hyperspectral remote sensing image based on deep learning can automatically capture the high-level discriminant features of image and effectively improve the classification  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3061489">doi:10.1109/access.2021.3061489</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jqsobopyxnhb7ptvcldvepwk5e">fatcat:jqsobopyxnhb7ptvcldvepwk5e</a> </span>
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Comparison Between Multitemporal Graph Based Classical Learning and LSTM Model Classifications for Sits Analysis

Ferdaous Chaabane, Safa Rejichi, Florence Tupin
<span title="2020-09-26">2020</span> <i title="IEEE"> IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium </i> &nbsp;
This paper proposes a comparison between a SOTAG (Spatial-Object Temporal Adjacency Graphs) SVM based spatio-temporal classification approach and the Recurrent Neuronal Network (RNN), LSTM (Long Short-Term  ...  Besides, statistical learning methods applied to SITS monitoring and analysis have created relatively efficient semi-automatic classification techniques.  ...  This constitutes a major disadvantage of deep learning algorithms used in remote sensing applications.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss39084.2020.9323777">doi:10.1109/igarss39084.2020.9323777</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/n3os7o37bbhmxkjttzuwi3drdi">fatcat:n3os7o37bbhmxkjttzuwi3drdi</a> </span>
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A Deep Neural Network Combined CNN and GCN for Remote Sensing Scene Classification

Jiali Liang, Yufan Deng, Dan Zeng
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/b2n2tpw5ang73osulebz6bm4ju" style="color: black;">IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing</a> </i> &nbsp;
There are a large number of scene-related ground objects in remote sensing images, as well as Graph convolutional network (GCN) has the potential to capture the dependencies among objects.  ...  Learning powerful discriminative features is the key for remote sensing scene classification. Most existing approaches based on convolutional neural network (CNN) have achieved great results.  ...  GCN-Based Branch In our work, we view remote sensing scene classification task as a graph classification problem.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jstars.2020.3011333">doi:10.1109/jstars.2020.3011333</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/pqihvgxr4zha3evylpnhg3sdey">fatcat:pqihvgxr4zha3evylpnhg3sdey</a> </span>
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Accurate Annotation of Remote Sensing Images via Active Spectral Clustering with Little Expert Knowledge

Gui-Song Xia, Zifeng Wang, Caiming Xiong, Liangpei Zhang
<span title="2015-11-10">2015</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
To address this issue, this paper introduces a novel active clustering method for the annotation of high-resolution remote sensing images.  ...  More precisely, given a set of remote sensing images, we first build a graph based on these images and then gradually optimize the structure of the graph using a cut-collect process, which relies on a  ...  Author Contributions Gui-Song Xia conceived the original idea for the study and contributed to the article's organization. Zifeng Wang contributed to the implementation of the research.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs71115014">doi:10.3390/rs71115014</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jwbzycqrgnhr3jwiiqrlp43zae">fatcat:jwbzycqrgnhr3jwiiqrlp43zae</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180727110620/https://res.mdpi.com/def502003071396ab490c529dfaf45243890a689353603fb8b5dc76c4e2aaeda93f03e6d89e946174e759e6e0f14c7503016f9965626c584a272b6a269f4b8aeeda0e9207ceb6401a782c05edd33b1acbaf9cac0e5640f803005d1afcb734df60c9119f7484267cab5f10855a8ba96e0b9b3ed0499f916960179aae244f0db91b2e41999ae214c3c3df8be965b1369bdf11f06661a99da619be28e470fec83eab7df?filename=&amp;attachment=1" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8c/94/8c94ddfbea0bca02557b3547b50c7c2fac79eebe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs71115014"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Machine learning in remote sensing data processing

Gustavo Camps-Valls
<span title="">2009</span> <i title="IEEE"> 2009 IEEE International Workshop on Machine Learning for Signal Processing </i> &nbsp;
This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.  ...  For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues.  ...  Active Learning In remote sensing, application of active learning methods that select the most relevant samples for training is quite recent.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mlsp.2009.5306233">doi:10.1109/mlsp.2009.5306233</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tb3on4evwvdvpkri67dbph7zfy">fatcat:tb3on4evwvdvpkri67dbph7zfy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20120713235257/http://www.uv.es/gcamps/papers/MLSP09_review.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b3/9b/b39b1b0655c779cae63733c7821da786971b6855.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/mlsp.2009.5306233"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Model-based active learning for SVM classification of remote sensing images

Edoardo Pasolli, Farid Melgani
<span title="">2010</span> <i title="IEEE"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6i67v2zuujhfvocqaapmnsoifm" style="color: black;">2010 IEEE International Geoscience and Remote Sensing Symposium</a> </i> &nbsp;
However, few works have been found for the problem of remote sensing image classification.  ...  In this work, an alternative approach of active learning for the SVM classification of remote sensing images is proposed and described in more details in the next Section.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss.2010.5652171">doi:10.1109/igarss.2010.5652171</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/igarss/PasolliM10.html">dblp:conf/igarss/PasolliM10</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3wdom5tc6vcwvaipvnzvihbf7a">fatcat:3wdom5tc6vcwvaipvnzvihbf7a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809071914/http://geodesy.unr.edu/hanspeterplag/library/IGARSS2010/pdfs/3614.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/ed/ce/edce6f071de689449999ae10d2284c3759d1d6a5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/igarss.2010.5652171"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Mapping Land Use from High Resolution Satellite Images by Exploiting the Spatial Arrangement of Land Cover Objects

Mengmeng Li, Alfred Stein
<span title="2020-12-18">2020</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
particular interest in comparing land use classifications between different graph-based methods and between different remote sensing images.  ...  This study investigates the use of graph convolutional networks (GCNs) in order to characterize spatial arrangement features for land use classification from high resolution remote sensing images, with  ...  from Fuzhou University for the support on the collection of ZY3 imagery.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs12244158">doi:10.3390/rs12244158</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sz3xaidddzfjbkzqiybaimwdga">fatcat:sz3xaidddzfjbkzqiybaimwdga</a> </span>
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INTERACTIVE CHANGE DETECTION USING HIGH RESOLUTION REMOTE SENSING IMAGES BASED ON ACTIVE LEARNING WITH GAUSSIAN PROCESSES

Hui Ru, Huai Yu, Pingping Huang, Wen Yang
<span title="2016-06-07">2016</span> <i title="Copernicus GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/yhzu63ehjfe2dbswnsdfm5vlba" style="color: black;">ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences</a> </i> &nbsp;
In this study, we present a method for interactive change detection using high resolution remote sensing images with active learning to overcome the shortages of existing remote sensing image change detection  ...  Although there have been many studies for change detection, the effective and efficient use of high resolution remote sensing images is still a problem.  ...  And each step of this interactive change detection for high resolution remote sensing images based on active learning will be introduced in the following parts.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprs-annals-iii-7-141-2016">doi:10.5194/isprs-annals-iii-7-141-2016</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/723j6gib5ncx3bii7tyhmsce5m">fatcat:723j6gib5ncx3bii7tyhmsce5m</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190428145503/https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/III-7/141/2016/isprs-annals-III-7-141-2016.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f6/e2/f6e20e0ddf70af30967eb1acbb57ca58d7324e70.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprs-annals-iii-7-141-2016"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Segmentation of Remote Sensing Images Based on U-Net Multi-Task Learning

Ni Ruiwen, Mu Ye, Li Ji, Zhang Tong, Luo Tianye, Feng Ruilong, Gong He, Hu Tianli, Sun Yu, Guo Ying, Li Shijun, Thobela Louis Tyasi
<span title="">2022</span> <i title="Computers, Materials and Continua (Tech Science Press)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/amujz7fcqna6do727z6ev3ueo4" style="color: black;">Computers Materials &amp; Continua</a> </i> &nbsp;
In order to accurately segment architectural features in highresolution remote sensing images, a semantic segmentation method based on U-net network multi-task learning is proposed.  ...  First, a boundary distance map was generated based on the remote sensing image of the ground truth map of the building.  ...  In order to achieve high-precision segmentation of building features in remote sensing images, this study proposes a multi-task learning U-net network based on ResNet50.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/cmc.2022.026881">doi:10.32604/cmc.2022.026881</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5y64tlwfofcejdceulsbxn67iu">fatcat:5y64tlwfofcejdceulsbxn67iu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220704085453/https://file.techscience.com/ueditor/files/cmc/TSP_CMC-73-2/TSP_CMC_26881/TSP_CMC_26881.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/c6/e3/c6e3845060fcbef9650d380f8507ec5255cc2235.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.32604/cmc.2022.026881"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Graph-Based Embedding Smoothing Network for Few-Shot Scene Classification of Remote Sensing Images

Zhengwu Yuan, Wendong Huang, Chan Tang, Aixia Yang, Xiaobo Luo
<span title="2022-02-26">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kay2tsbijbawliu45dnhvyvgsq" style="color: black;">Remote Sensing</a> </i> &nbsp;
Existing methods seek to take advantage of transfer knowledge or meta-knowledge to resolve the scene classification issue of remote sensing images with a handful of labeled samples while ignoring various  ...  For this reason, in this paper, an end-to-end graph neural network is presented to enhance the performance of scene classification in few-shot scenarios, referred to as the graph-based embedding smoothing  ...  To this end, a novel graph-based embedding smoothing network, called GES-Net, is presented in this paper for remote sensing scene classification, which not only has the ability to learn from few samples  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs14051161">doi:10.3390/rs14051161</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hvxzgzeg2bblvnpppdblgogd7y">fatcat:hvxzgzeg2bblvnpppdblgogd7y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220503102535/https://mdpi-res.com/d_attachment/remotesensing/remotesensing-14-01161/article_deploy/remotesensing-14-01161.pdf?version=1645867146" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f2/17/f217f4611eef4306896c9a215a9f7ff1446503cb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/rs14051161"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Multimodal hyperspectral remote sensing: an overview and perspective

Yanfeng Gu, Tianzhu Liu, Guoming Gao, Guangbo Ren, Yi Ma, Jocelyn Chanussot, Xiuping Jia
<span title="2021-01-21">2021</span> <i title="Springer Science and Business Media LLC"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ikvx2lmj7rew7jpw4lygqgjpby" style="color: black;">Science China Information Sciences</a> </i> &nbsp;
Through the analysis of development trend of hyperspectral imaging and current research situation, we hope to provide a direction for future research on multimodal hyperspectral remote sensing.  ...  Nowadays, with the fast development of new technology in the fields of information and photoelectricity sensing, and the popularity of unmanned aerial vehicle, hyperspectral remote sensing imaging presents  ...  As for machine learning methods, a representative kind is kernel-based methods and support vector machines [40, 41] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-020-3084-1">doi:10.1007/s11432-020-3084-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tivcc4l5efh5zg62t37stswqgu">fatcat:tivcc4l5efh5zg62t37stswqgu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210428105849/https://www.sciengine.com/doi/pdf/7B947919B06943EEAE29176B05B7CFED" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/90/0e/900e091f1031f0e84cee846037871a0a95e93186.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11432-020-3084-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Semantic Segmentation of Remote Sensing Image Based on Convolutional Neural Network and Mask Generation

Binglin Niu, Yi-Zhang Jiang
<span title="2021-06-01">2021</span> <i title="Hindawi Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wpareqynwbgqdfodcyhh36aqaq" style="color: black;">Mathematical Problems in Engineering</a> </i> &nbsp;
To resolve the problem of inadequate utilization of multilayer features by existing methods, a semantic segmentation method for remote sensing images based on convolutional neural network and mask generation  ...  In order to solve the difficulty of deep neural network training and the problem of degeneration after convergence, a framework based on residual learning was adopted, which can simplify the training of  ...  Literature [26] proposed a 5-layer network structure to complete the target classification of remote sensing images. Hu et al.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2472726">doi:10.1155/2021/2472726</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s4rurt3n2racpacnzcp7tcdziy">fatcat:s4rurt3n2racpacnzcp7tcdziy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210604235044/https://downloads.hindawi.com/journals/mpe/2021/2472726.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/19/24/19243fb63cc1f6e8c1c60abea988abc494d9a046.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1155/2021/2472726"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> hindawi.com </button> </a>
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