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Graph Relation Network: Modeling Relations Between Scenes for Multilabel Remote-Sensing Image Classification and Retrieval

Jian Kang, Ruben Fernandez-Beltran, Danfeng Hong, Jocelyn Chanussot, Antonio Plaza
2020 IEEE Transactions on Geoscience and Remote Sensing  
Owing to the proliferation of large-scale remote sensing (RS) archives with multiple annotations, multi-label RS scene classification and retrieval are becoming increasingly popular.  ...  Our GRN is able to model the relations between samples (or scenes) by making use of a graph structure which is fed into network learning.  ...  ACKNOWLEDGMENT The authors would like to thank the authors for their efforts in creating the multi-label datasets based on UCM, AID and DFC15, and the reviewers for their valuable suggestions.  ... 
doi:10.1109/tgrs.2020.3016020 fatcat:qrjfmxi5vfe2hldfbu5hf5ytaq

Relation Network for Multilabel Aerial Image Classification

Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu
2020 IEEE Transactions on Geoscience and Remote Sensing  
To address this, we propose a novel aerial image multilabel classification network, attention-aware label relational reasoning network.  ...  Multilabel classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years.  ...  Liu for supporting this work with data annotation.  ... 
doi:10.1109/tgrs.2019.2963364 fatcat:lpk2hmjhc5doplnxuyvyyen3fy

Multi-Label Remote Sensing Image Scene Classification by Combining a Convolutional Neural Network and a Graph Neural Network

Yansheng Li, Ruixian Chen, Yongjun Zhang, Mi Zhang, Ling Chen
2020 Remote Sensing  
As one of the fundamental tasks in remote sensing (RS) image understanding, multi-label remote sensing image scene classification (MLRSSC) is attracting increasing research interest.  ...  Based on the trained CNN, one scene graph for each scene is further constructed, where nodes of the graph are represented by superpixel regions of the scene.  ...  In addition, Kang et al. proposed a graph relation network to model the relationships between image scenes for MLRSSC [49] .  ... 
doi:10.3390/rs12234003 fatcat:nv3a55bhcre6xbd6lr2xbw4a7e

Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network

Zhenfeng Shao, Weixun Zhou, Xueqing Deng, Maoding Zhang, Qimin Cheng
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Index Terms-Fully convolutional networks (FCN), multilabel retrieval, multilabel vector, region convolutional features (RCFs), remote sensing image retrieval (RSIR), single-label retrieval.  ...  In this scenario, however, the scene complexity of remote sensing images is ignored, where an image might have multiple classes (i.e., multiple labels), resulting in poor retrieval performance.  ...  ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their comments to improve this article.  ... 
doi:10.1109/jstars.2019.2961634 fatcat:tcl3assckjevrezbqkogcn2dna

Relation Network for Multi-label Aerial Image Classification [article]

Yuansheng Hua, Lichao Mou, Xiao Xiang Zhu
2020 arXiv   pre-print
To address this, we propose a novel aerial image multi-label classification network, attention-aware label relational reasoning network.  ...  Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years.  ...  For this reason, the aerial image classification has become one of the fundamental visual tasks in the remote sensing community and drawn a plethora of research interests [14] , [15] , [16] , [17]  ... 
arXiv:1907.07274v3 fatcat:2lqaw6qxp5cavel44qytlz7ali

Multilabel Annotation of Multispectral Remote Sensing Images using Error-Correcting Output Codes and Most Ambiguous Examples

Anamaria Radoi, Mihai Datcu
2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
This paper presents a novel framework for multilabel classification of multispectral remote sensing images using errorcorrecting output codes.  ...  Index Terms-Error-correcting output codes (ECOCs), multilabel image classification, pretrained convolutional neural networks, support vector machines (SVMs).  ...  Recent advances have shown that convolutional neural networks (CNNs) achieve high accuracy values for remote sensing image classification [5] .  ... 
doi:10.1109/jstars.2019.2916838 fatcat:4rpi56umubhcpj55kz6nxbl4cq

A Discriminative Distillation Network for Cross-Source Remote Sensing Image Retrieval

Wei Xiong, Zhenyu Xiong, Yaqi Cui, Yafei Lv
2020 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Cross-source CBRSIR (CS-CBRSIR) is used to find relevant remote sensing images across different remote sensing sources (i.e., multispectral images and panchromatic images).  ...  Index Terms-Cross-source content-based remote sensing image retrieval (CS-CBRSIR), discriminative features, distillation network, joint optimization configuration (JOC).  ...  These three studies introduce the pioneer cross-modal retrieval works, allowing the model between remote sensing images and spoken audio, remote sensing images and sentences and panchromatic and multispectral  ... 
doi:10.1109/jstars.2020.2980870 fatcat:xusbxjahe5boblgc2bwynoo2ki

Gated Recurrent Multiattention Network for VHR Remote Sensing Image Classification

Boyang Li, Yulan Guo, Jungang Yang, Longguang Wang, Yingqian Wang, Wei An
2021 IEEE Transactions on Geoscience and Remote Sensing  
In very high-resolution (VHR) remote sensing images, the contributions of different regions to image classification can vary significantly, because informative areas are generally limited and scattered  ...  With the advances of deep learning, many recent CNN-based methods have yielded promising results for image classification.  ...  RELATED WORK In this section, we briefly review the related work for VHR remote sensing scene classification and attention mechanism. A.  ... 
doi:10.1109/tgrs.2021.3093914 fatcat:srynjanpo5dwnebepktnmzw2wu

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 1784-1797 Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network.  ...  ., +, JSTARS 2020 1119-1133 Multilabel Remote Sensing Image Retrieval Based on Fully Convolutional Network.  ...  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

Inter-band Retrieval and Classification Using the Multi-labeled Sentinel-2 BigEarthNet Archive

Ushasi Chaudhuri, Subhadip Dey, Mihai Datcu, Biplab Banerjee, Avik Bhattacharya
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
We also propose a novel triplet-loss function for multi-labeled images and use it to design an inter-band group retrieval framework.  ...  Experimental results for the classification and retrieval framework on the benchmarked BigEarthNet dataset exhibit marked improvements over existing studies.  ...  This characteristic becomes especially more crucial and challenging for multilabeled remote sensing datasets.  ... 
doi:10.1109/jstars.2021.3112209 fatcat:jx3wuv2k3nb6the5ofuojgdbaq

AN EFFICIENT REPRESENTATION OF 3D BUILDINGS: APPLICATION TO THE EVALUATION OF CITY MODELS

O. Ennafii, A. Le Bris, F. Lafarge, C. Mallet
2021 The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences  
In this paper, we propose two solutions that take into account the specificity of 3D urban models. They are based on graph kernels and Scattering Network.  ...  Most modeling methods focus on 3D buildings with Very High Resolution overhead data (images and/or 3D point clouds).  ...  Scattering Networks (ScatNets) Convolutional Neural Networks (ConvNets) are state-of-the-art feature extractors in image classification.  ... 
doi:10.5194/isprs-archives-xliii-b2-2021-329-2021 fatcat:cuqaeqzln5hvpmi66xd6kjr22e

Multi-label Remote Sensing Image Annotation with Multi-scale attention and Label Correlation

Rui Huang, Fengcai Zheng, Wei Huang
2021 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
Deep learning based multi-label image annotation is receiving increasing attention in the field of remote sensing due to the great success of deep networks in single-label remote sensing image classification  ...  In this paper, we propose an end-to-end deep learning framework for multi-label remote sensing image annotation.  ...  Mingyi He for his valuable comments.  ... 
doi:10.1109/jstars.2021.3091134 fatcat:ttznft4psreqbnkldnminajepi

BigEarthNet-MM: A Large Scale Multi-Modal Multi-Label Benchmark Archive for Remote Sensing Image Classification and Retrieval [article]

Gencer Sumbul, Arne de Wall, Tristan Kreuziger, Filipe Marcelino, Hugo Costa, Pedro Benevides, Mário Caetano, Begüm Demir, Volker Markl
2021 arXiv   pre-print
multi-label remote sensing (RS) image retrieval and classification.  ...  In our experiments, we show the potential of BigEarthNet-MM for multi-modal multi-label image retrieval and classification problems by considering several state-of-the-art DL models.  ...  For all the models, we added a fully connected layer that includes 19 neurons at the end of the network for the classification.  ... 
arXiv:2105.07921v1 fatcat:pptg5dlcrbdcvldbyfxkykxfju

UAV Image Multi-Labeling with Data-Efficient Transformers

Laila Bashmal, Yakoub Bazi, Mohamad Mahmoud Al Rahhal, Haikel Alhichri, Naif Al Ajlan
2021 Applied Sciences  
In this paper, we present an approach for the multi-label classification of remote sensing images based on data-efficient transformers.  ...  During the training phase, we generated a second view for each image from the training set using data augmentation.  ...  Recently, a method for multilabel classification and retrieval based on metric learning was proposed [18] .  ... 
doi:10.3390/app11093974 doaj:d8c45fb6d39f40d38e6ad4400a21d159 fatcat:zbmmbpnhfnd7rjevp2akf6yddu

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  
., and Lopez, J.F  ...  ., +, JSTARS July 2019 2107-2120 Feature and Model Level Fusion of Pretrained CNN for Remote Sensing Scene Classification.  ...  ., +, JSTARS July 2019 2107-2120 Feature and Model Level Fusion of Pretrained CNN for Remote Sensing Scene Classification.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u
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