Our project is done in close collaboration with the Technical University of Munich. In particular with the TUM Data Science in Earth Observation (Sipeo) group. The complete list of associated publications might be also interesting for you and is available here.
Publications
Publications
2021 |
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 | Kochupillai, M Outline of a Novel Approach for Indentifying Ethical Issues in Early Stages of AI4EO Research,Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS) Conference 2021. BibTeX | Tags: @conference{RN2472, title = {Outline of a Novel Approach for Indentifying Ethical Issues in Early Stages of AI4EO Research,Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS)}, author = {M Kochupillai}, year = {2021}, date = {2021-00-00}, journal = {Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS),}, keywords = {}, pubstate = {published}, tppubtype = {conference} } |
 | Hua, Y; Mou, L; Jin, P; Zhu, X X MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{hua2021multiscene, title = {MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images}, author = {Y Hua and L Mou and P Jin and X X Zhu}, year = {2021}, date = {2021-00-00}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
Kochupillai, Mrinalini Outline of a Novel Approach for Identifying Ethical Issues in Early Stages of AI4EO Research Conference 2021. @conference{Kochupillai2021, title = {Outline of a Novel Approach for Identifying Ethical Issues in Early Stages of AI4EO Research}, author = {Mrinalini Kochupillai}, url = {https://igarss2021.com/view_paper.php?PaperNum=1525}, year = {2021}, date = {2021-00-00}, journal = {Conference paper for the IEEE’s International Geoscience and Remote Sensing Symposium (IGARSS),}, keywords = {}, pubstate = {published}, tppubtype = {conference} } | |
2020 |
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 | Mou, Lichao; Hua, Yuansheng; Jin, Pu; Zhu, Xiao Xiang ERA: A dataset and deep learning benchmark for event recognition in aerial videos Journal Article IEEE Geoscience and Remote Sensing Magazine, 2020, (in press). BibTeX | Tags: @article{Mou2020, title = {ERA: A dataset and deep learning benchmark for event recognition in aerial videos}, author = {Lichao Mou and Yuansheng Hua and Pu Jin and Xiao Xiang Zhu}, year = {2020}, date = {2020-01-01}, journal = {IEEE Geoscience and Remote Sensing Magazine}, note = {in press}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Mou, Lichao; Hua, Yuansheng; Zhu, Xiao Xiang Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high resolution aerial images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2020, (in press). BibTeX | Tags: @article{Mou2020a, title = {Relation matters: Relational context-aware fully convolutional network for semantic segmentation of high resolution aerial images}, author = {Lichao Mou and Yuansheng Hua and Xiao Xiang Zhu}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, note = {in press}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Hua, Yuansheng; Mou, Lichao; Zhu, Xiao Xiang Relation network for multilabel aerial image classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 58 (7), pp. 4558-4572, 2020. BibTeX | Tags: @article{Hua2020, title = {Relation network for multilabel aerial image classification}, author = {Yuansheng Hua and Lichao Mou and Xiao Xiang Zhu}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {58}, number = {7}, pages = {4558-4572}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Rußwurm, Marc; Ali, Mohsin; Zhu, Xiaoxiang; Gal, Yarin; Körner, Marco Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models Inproceedings IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, IEEE 2020. BibTeX | Tags: @inproceedings{russwurm2020c, title = {Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models}, author = {Marc Rußwurm and Mohsin Ali and Xiaoxiang Zhu and Yarin Gal and Marco Körner}, year = {2020}, date = {2020-01-01}, booktitle = {IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium}, organization = {IEEE}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } |
 | Kochupillai, Mrinalini; Lütge, Christoph; Poszler, Franziska Programming Away Human Rights and Responsibilities?“The Moral Machine Experiment” and the Need for a More “Humane” AV Future Journal Article NanoEthics, 14 (3), pp. 285-299, 2020, ISSN: 1871-4765. BibTeX | Tags: @article{RN2469, title = {Programming Away Human Rights and Responsibilities?“The Moral Machine Experiment” and the Need for a More “Humane” AV Future}, author = {Mrinalini Kochupillai and Christoph Lütge and Franziska Poszler}, issn = {1871-4765}, year = {2020}, date = {2020-01-01}, journal = {NanoEthics}, volume = {14}, number = {3}, pages = {285-299}, keywords = {}, pubstate = {published}, tppubtype = {article} } |
 | Brinkmann, Johannes; Kochupillai, Mrinalini Law, Business, and Legitimacy Book Chapter pp. 489-507, 2020, ISSN: 3030146219. BibTeX | Tags: @inbook{RN2470, title = {Law, Business, and Legitimacy}, author = {Johannes Brinkmann and Mrinalini Kochupillai}, issn = {3030146219}, year = {2020}, date = {2020-01-01}, journal = {Handbook of Business Legitimacy: Responsibility, Ethics and Society}, pages = {489-507}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } |
Mou, Lichao; Hua, Yuansheng; Zhu, Xiao Xiang Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 58 , pp. 7557-7569, 2020, ISSN: 15580644, (in press). Abstract | Links | BibTeX | Tags: @article{Mou2020c, title = {Relation Matters: Relational Context-Aware Fully Convolutional Network for Semantic Segmentation of High-Resolution Aerial Images}, author = {Lichao Mou and Yuansheng Hua and Xiao Xiang Zhu}, doi = {10.1109/TGRS.2020.2979552}, issn = {15580644}, year = {2020}, date = {2020-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {58}, pages = {7557-7569}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations. Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs. In this article, we introduce two simple yet effective network units, the spatial relation module, and the channel relation module to learn and reason about global relationships between any two spatial positions or feature maps, and then produce Relation-Augmented (RA) feature representations. The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image data sets, namely International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam data sets, which fundamentally depend on long-range spatial relational reasoning. The networks achieve very competitive results, a mean score of 88.54% on the Vaihingen data set and a mean score of 88.01% on the Potsdam data set, bringing significant improvements over baselines.}, note = {in press}, keywords = {}, pubstate = {published}, tppubtype = {article} } Most current semantic segmentation approaches fall back on deep convolutional neural networks (CNNs). However, their use of convolution operations with local receptive fields causes failures in modeling contextual spatial relations. Prior works have sought to address this issue by using graphical models or spatial propagation modules in networks. But such models often fail to capture long-range spatial relationships between entities, which leads to spatially fragmented predictions. Moreover, recent works have demonstrated that channel-wise information also acts a pivotal part in CNNs. In this article, we introduce two simple yet effective network units, the spatial relation module, and the channel relation module to learn and reason about global relationships between any two spatial positions or feature maps, and then produce Relation-Augmented (RA) feature representations. The spatial and channel relation modules are general and extensible, and can be used in a plug-and-play fashion with the existing fully convolutional network (FCN) framework. We evaluate relation module-equipped networks on semantic segmentation tasks using two aerial image data sets, namely International Society for Photogrammetry and Remote Sensing (ISPRS) Vaihingen and Potsdam data sets, which fundamentally depend on long-range spatial relational reasoning. The networks achieve very competitive results, a mean score of 88.54% on the Vaihingen data set and a mean score of 88.01% on the Potsdam data set, bringing significant improvements over baselines. | |
Kochupillai, Mrinalini; Lütge, Christoph; Poszler, Franziska Programming Away Human Rights and Responsibilities? “The Moral Machine Experiment” and the Need for a More “Humane” AV Future Journal Article NanoEthics, 14 (3), pp. 285-299, 2020, ISSN: 18714765. Abstract | Links | BibTeX | Tags: @article{Kochupillai2020, title = {Programming Away Human Rights and Responsibilities? “The Moral Machine Experiment” and the Need for a More “Humane” AV Future}, author = {Mrinalini Kochupillai and Christoph Lütge and Franziska Poszler}, url = {https://link.springer.com/article/10.1007/s11569-020-00374-4}, doi = {10.1007/S11569-020-00374-4/TABLES/2}, issn = {18714765}, year = {2020}, date = {2020-01-01}, journal = {NanoEthics}, volume = {14}, number = {3}, pages = {285-299}, publisher = {Springer Science and Business Media Deutschland GmbH}, abstract = {Dilemma situations involving the choice of which human life to save in the case of unavoidable accidents are expected to arise only rarely in the context of autonomous vehicles (AVs). Nonetheless, the scientific community has devoted significant attention to finding appropriate and (socially) acceptable automated decisions in the event that AVs or drivers of AVs were indeed to face such situations. Awad and colleagues, in their now famous paper “The Moral Machine Experiment”, used a “multilingual online ‘serious game’ for collecting large-scale data on how citizens would want AVs to solve moral dilemmas in the context of unavoidable accidents.” Awad and colleagues undoubtedly collected an impressive and philosophically useful data set of armchair intuitions. However, we argue that applying their findings to the development of “global, socially acceptable principles for machine learning” would violate basic tenets of human rights law and fundamental principles of human dignity. To make its arguments, our paper cites principles of tort law, relevant case law, provisions from the Universal Declaration of Human Rights, and rules from the German Ethics Code for Autonomous and Connected Driving.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Dilemma situations involving the choice of which human life to save in the case of unavoidable accidents are expected to arise only rarely in the context of autonomous vehicles (AVs). Nonetheless, the scientific community has devoted significant attention to finding appropriate and (socially) acceptable automated decisions in the event that AVs or drivers of AVs were indeed to face such situations. Awad and colleagues, in their now famous paper “The Moral Machine Experiment”, used a “multilingual online ‘serious game’ for collecting large-scale data on how citizens would want AVs to solve moral dilemmas in the context of unavoidable accidents.” Awad and colleagues undoubtedly collected an impressive and philosophically useful data set of armchair intuitions. However, we argue that applying their findings to the development of “global, socially acceptable principles for machine learning” would violate basic tenets of human rights law and fundamental principles of human dignity. To make its arguments, our paper cites principles of tort law, relevant case law, provisions from the Universal Declaration of Human Rights, and rules from the German Ethics Code for Autonomous and Connected Driving. | |
Brinkmann, Johannes; Kochupillai, Mrinalini Law, Business, and Legitimacy Book Chapter pp. 489-507, Elsevier BV, 2020, ISSN: 3030146219. @inbook{Brinkmann2020, title = {Law, Business, and Legitimacy}, author = {Johannes Brinkmann and Mrinalini Kochupillai}, url = {https://papers.ssrn.com/abstract=3373565}, doi = {10.2139/SSRN.3373565}, issn = {3030146219}, year = {2020}, date = {2020-01-01}, journal = {Handbook of Business Legitimacy: Responsibility, Ethics and Society}, pages = {489-507}, publisher = {Elsevier BV}, keywords = {}, pubstate = {published}, tppubtype = {inbook} } | |
Mou, Lichao; Hua, Yuansheng; Jin, Pu; Zhu, Xiao Xiang; Member, Senior ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos Journal Article IEEE Geoscience and Remote Sensing Magazine, 2020, (in press). Abstract | Links | BibTeX | Tags: @article{Mou2020d, title = {ERA: A Dataset and Deep Learning Benchmark for Event Recognition in Aerial Videos}, author = {Lichao Mou and Yuansheng Hua and Pu Jin and Xiao Xiang Zhu and Senior Member}, url = {https://arxiv.org/abs/2001.11394v4}, doi = {10.48550/arxiv.2001.11394}, year = {2020}, date = {2020-01-01}, journal = {IEEE Geoscience and Remote Sensing Magazine}, abstract = {Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is https://lcmou.github.io/ERA_Dataset/}, note = {in press}, keywords = {}, pubstate = {published}, tppubtype = {article} } Along with the increasing use of unmanned aerial vehicles (UAVs), large volumes of aerial videos have been produced. It is unrealistic for humans to screen such big data and understand their contents. Hence methodological research on the automatic understanding of UAV videos is of paramount importance. In this paper, we introduce a novel problem of event recognition in unconstrained aerial videos in the remote sensing community and present a large-scale, human-annotated dataset, named ERA (Event Recognition in Aerial videos), consisting of 2,864 videos each with a label from 25 different classes corresponding to an event unfolding 5 seconds. The ERA dataset is designed to have a significant intra-class variation and inter-class similarity and captures dynamic events in various circumstances and at dramatically various scales. Moreover, to offer a benchmark for this task, we extensively validate existing deep networks. We expect that the ERA dataset will facilitate further progress in automatic aerial video comprehension. The website is https://lcmou.github.io/ERA_Dataset/ | |
Behrens, Freya; Sauder, Jonathan; Jung, Peter Neurally Augmented ALISTA Journal Article 2020. Abstract | Links | BibTeX | Tags: @article{Behrens2020, title = {Neurally Augmented ALISTA}, author = {Freya Behrens and Jonathan Sauder and Peter Jung}, url = {https://arxiv.org/abs/2010.01930v1}, doi = {10.48550/arxiv.2010.01930}, year = {2020}, date = {2020-01-01}, abstract = {It is well-established that many iterative sparse reconstruction algorithms can be unrolled to yield a learnable neural network for improved empirical performance. A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data. Recently, Analytic LISTA (ALISTA) has been introduced, combining the strong empirical performance of a fully learned approach like LISTA, while retaining theoretical guarantees of classical compressed sensing algorithms and significantly reducing the number of parameters to learn. However, these parameters are trained to work in expectation, often leading to suboptimal reconstruction of individual targets. In this work we therefore introduce Neurally Augmented ALISTA, in which an LSTM network is used to compute step sizes and thresholds individually for each target vector during reconstruction. This adaptive approach is theoretically motivated by revisiting the recovery guarantees of ALISTA. We show that our approach further improves empirical performance in sparse reconstruction, in particular outperforming existing algorithms by an increasing margin as the compression ratio becomes more challenging.}, keywords = {}, pubstate = {published}, tppubtype = {article} } It is well-established that many iterative sparse reconstruction algorithms can be unrolled to yield a learnable neural network for improved empirical performance. A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data. Recently, Analytic LISTA (ALISTA) has been introduced, combining the strong empirical performance of a fully learned approach like LISTA, while retaining theoretical guarantees of classical compressed sensing algorithms and significantly reducing the number of parameters to learn. However, these parameters are trained to work in expectation, often leading to suboptimal reconstruction of individual targets. In this work we therefore introduce Neurally Augmented ALISTA, in which an LSTM network is used to compute step sizes and thresholds individually for each target vector during reconstruction. This adaptive approach is theoretically motivated by revisiting the recovery guarantees of ALISTA. We show that our approach further improves empirical performance in sparse reconstruction, in particular outperforming existing algorithms by an increasing margin as the compression ratio becomes more challenging. | |
Rußwurm, Marc; Ali, Mohsin; Zhu, Xiaoxiang; Gal, Yarin; Körner, Marco Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models Inproceedings IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, pp. 1–4, IEEE 2020. Abstract | Links | BibTeX | Tags: @inproceedings{Russwurm2020b, title = {Model and Data Uncertainty for Satellite Time Series Forecasting with Deep Recurrent Models}, author = {Marc Rußwurm and Mohsin Ali and Xiaoxiang Zhu and Yarin Gal and Marco Körner}, url = {https://elib.dlr.de/139306/}, year = {2020}, date = {2020-01-01}, booktitle = {IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium}, pages = {1--4}, organization = {IEEE}, abstract = {Deep Learning is often criticized as black-box method which often provides accurate predictions, but limited explanation of the underlying processes and no indication when to not trust those predictions. Equipping existing deep learning models with an (approximate) notion of uncertainty can help mitigate both these issues therefore their use should be known more broadly in the community. The Bayesian deep learning community has developed model-agnostic and easy to-implement methodology to estimate both data and model uncertainty within deep learning models which is hardly applied in the remote sensing community. In this work, we adopt this methodology for deep recurrent satellite time series forecasting, and test its assumptions on data and model uncertainty. We demonstrate its effectiveness on two applications on climate change, and event change detection and outline limitations.}, keywords = {}, pubstate = {published}, tppubtype = {inproceedings} } Deep Learning is often criticized as black-box method which often provides accurate predictions, but limited explanation of the underlying processes and no indication when to not trust those predictions. Equipping existing deep learning models with an (approximate) notion of uncertainty can help mitigate both these issues therefore their use should be known more broadly in the community. The Bayesian deep learning community has developed model-agnostic and easy to-implement methodology to estimate both data and model uncertainty within deep learning models which is hardly applied in the remote sensing community. In this work, we adopt this methodology for deep recurrent satellite time series forecasting, and test its assumptions on data and model uncertainty. We demonstrate its effectiveness on two applications on climate change, and event change detection and outline limitations. | |
2019 |
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Hua, Yuansheng; Mou, Lichao; Zhu, Xiao Xiang Relation Network for Multi-label Aerial Image Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 58 (7), pp. 4558-4572, 2019. Abstract | Links | BibTeX | Tags: @article{Hua2019, title = {Relation Network for Multi-label Aerial Image Classification}, author = {Yuansheng Hua and Lichao Mou and Xiao Xiang Zhu}, url = {http://arxiv.org/abs/1907.07274 http://dx.doi.org/10.1109/TGRS.2019.2963364}, doi = {10.1109/TGRS.2019.2963364}, year = {2019}, date = {2019-01-01}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {58}, number = {7}, pages = {4558-4572}, publisher = {Institute of Electrical and Electronics Engineers Inc.}, abstract = {Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an LSTM layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multi-label classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module, 2) an attentional region extraction module, and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features and yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features in a proposal-free way and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on the UCM multi-label dataset and a newly produced dataset, AID multi-label dataset. Quantitative and qualitative results on these two datasets demonstrate the effectiveness of our model. To facilitate progress in the multi-label aerial image classification, the AID multi-label dataset will be made publicly available.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Multi-label classification plays a momentous role in perceiving intricate contents of an aerial image and triggers several related studies over the last years. However, most of them deploy few efforts in exploiting label relations, while such dependencies are crucial for making accurate predictions. Although an LSTM layer can be introduced to modeling such label dependencies in a chain propagation manner, the efficiency might be questioned when certain labels are improperly inferred. To address this, we propose a novel aerial image multi-label classification network, attention-aware label relational reasoning network. Particularly, our network consists of three elemental modules: 1) a label-wise feature parcel learning module, 2) an attentional region extraction module, and 3) a label relational inference module. To be more specific, the label-wise feature parcel learning module is designed for extracting high-level label-specific features. The attentional region extraction module aims at localizing discriminative regions in these features and yielding attentional label-specific features. The label relational inference module finally predicts label existences using label relations reasoned from outputs of the previous module. The proposed network is characterized by its capacities of extracting discriminative label-wise features in a proposal-free way and reasoning about label relations naturally and interpretably. In our experiments, we evaluate the proposed model on the UCM multi-label dataset and a newly produced dataset, AID multi-label dataset. Quantitative and qualitative results on these two datasets demonstrate the effectiveness of our model. To facilitate progress in the multi-label aerial image classification, the AID multi-label dataset will be made publicly available. | |
0000 |
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in press. Lin, Lichao Mou Yuansheng Hua Xiao Xiang Zhu Jane Wang Tianze Yu Jianzhe Z SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images Journal Article IEEE Transactions on Geoscience and Remote Sensing, 0000. BibTeX | Tags: @article{Yuinpress, title = {SCIDA: Self-Correction Integrated Domain Adaptation from Single- to Multi-label Aerial Images}, author = {Lichao Mou Yuansheng Hua Xiao Xiang Zhu Jane Wang Z in press. Tianze Yu Jianzhe Lin}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, keywords = {}, pubstate = {published}, tppubtype = {article} } | |
Kandala, Hitesh ; Saha, Sudipan ; Banerjee, Biplab ; Zhu, Xiao Xiang Exploring Transformer and Multi-label Classification for Remote Sensing Image Captioning Journal Article IEEE Geoscience and Remote Sensing Letters, 0000. @article{Kandala2022, title = {Exploring Transformer and Multi-label Classification for Remote Sensing Image Captioning}, author = {Kandala, Hitesh and Saha, Sudipan and Banerjee, Biplab and Zhu, Xiao Xiang}, journal = {IEEE Geoscience and Remote Sensing Letters}, keywords = {.}, pubstate = {published}, tppubtype = {article} } |