2021
|
 | Hua, Yuansheng; Mou, Lichao; Lin, Jianzhe; Heidler, Konrad; Zhu, Xiao Xiang Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks Journal Article ISPRS Journal of Photogrammetry and Remote Sensing, 2021. BibTeX | Tags: @article{hua2021prototype,
title = {Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks},
author = {Yuansheng Hua and Lichao Mou and Jianzhe Lin and Konrad Heidler and Xiao Xiang Zhu},
year = {2021},
date = {2021-04-09},
journal = {ISPRS Journal of Photogrammetry and Remote Sensing},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Klemmer, Konstantin; Saha, Sudipan; Kahl, Matthias; Xu, Tianlin; Zhu, Xiao Xiang Generative modeling of spatio-temporal weather patterns with extreme event conditioning Inproceedings AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop, ICLR 2021, 2021. Abstract | Links | BibTeX | Tags: @inproceedings{Klemmer2021,
title = {Generative modeling of spatio-temporal weather patterns with extreme event conditioning},
author = {Konstantin Klemmer and Sudipan Saha and Matthias Kahl and Tianlin Xu and Xiao Xiang Zhu},
url = {http://arxiv.org/abs/2104.12469},
year = {2021},
date = {2021-04-01},
booktitle = {AI: Modeling Oceans and Climate Change (AIMOCC 2021) Workshop, ICLR 2021},
abstract = {Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Deep generative models are increasingly used to gain insights in the geospatial data domain, e.g., for climate data. However, most existing approaches work with temporal snapshots or assume 1D time-series; few are able to capture spatio-temporal processes simultaneously. Beyond this, Earth-systems data often exhibit highly irregular and complex patterns, for example caused by extreme weather events. Because of climate change, these phenomena are only increasing in frequency. Here, we proposed a novel GAN-based approach for generating spatio-temporal weather patterns conditioned on detected extreme events. Our approach augments GAN generator and discriminator with an encoded extreme weather event segmentation mask. These segmentation masks can be created from raw input using existing event detection frameworks. As such, our approach is highly modular and can be combined with custom GAN architectures. We highlight the applicability of our proposed approach in experiments with real-world surface radiation and zonal wind data. |
 | Perko, Roland; Klopschitz, Manfred; Almer, Alexander; Roth, Peter M Critical Aspects of Person Couting and Density Estimation Journal Article Journal of Imaging, 7 (2), pp. 21, 2021. BibTeX | Tags: @article{perko21a,
title = {Critical Aspects of Person Couting and Density Estimation},
author = {Roland Perko and Manfred Klopschitz and Alexander Almer and Peter M Roth},
year = {2021},
date = {2021-01-01},
journal = {Journal of Imaging},
volume = {7},
number = {2},
pages = {21},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Basirat, Mina; Roth, Peter M S*ReLU: Learning Piecewise Linear Activation Functions via ParticleSwarm Optimization Inproceedings International Conf. on Computer Vision Theory and Applications, 2021. BibTeX | Tags: @inproceedings{basirat21ab,
title = {S*ReLU: Learning Piecewise Linear Activation Functions via ParticleSwarm Optimization},
author = {Mina Basirat and Peter M Roth},
year = {2021},
date = {2021-01-01},
booktitle = {International Conf. on Computer Vision Theory and Applications},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
|
 | Editors:Camps-Valls, Gustau; Tuia, Devis; Zhu, Xiao Xiang; Reichstein, Markus Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences Book Wiley & Sons, 2021, ISBN: 978-1-119-64614-3. BibTeX | Tags: @book{CampsValls21wiley,
title = {Deep learning for the Earth Sciences: A comprehensive approach to remote sensing, climate science and geosciences},
author = {Gustau Editors:Camps-Valls and Devis Tuia and Xiao Xiang Zhu and Markus Reichstein },
isbn = {978-1-119-64614-3},
year = {2021},
date = {2021-01-01},
publisher = {Wiley & Sons},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
|
 | Mou, Lichao; Saha, Sudipan; Hua, Yuansheng; Bovolo, Francesca; Bruzzone, Lorenzo; Zhu, Xiao Xiang Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification Journal Article IEEE Transactions on Geoscience and Remote Sensing, 2021. BibTeX | Tags: @article{deepReinforcementTgrs2021,
title = {Deep Reinforcement Learning for Band Selection in Hyperspectral Image Classification},
author = {Lichao Mou and Sudipan Saha and Yuansheng Hua and Francesca Bovolo and Lorenzo Bruzzone and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
journal = {IEEE Transactions on Geoscience and Remote Sensing},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Saha, Sudipan; Banerjee, Biplab; Zhu, Xiao Xiang Trusting Small Training Dataset for Supervised Change Detection Journal Article 2021. BibTeX | Tags: @article{trustingSmallDatasetSudipanIgarss2021,
title = {Trusting Small Training Dataset for Supervised Change Detection},
author = {Sudipan Saha and Biplab Banerjee and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Prexl, Jonathan; Saha, Sudipan; Zhu, Xiao Xiang Mitigating Spatial and Spectral Differences for Change Detection using Super-resolution and Unsupervised learning Journal Article 2021. BibTeX | Tags: @article{mitigatingJonathanIgarss2021,
title = {Mitigating Spatial and Spectral Differences for Change Detection using Super-resolution and Unsupervised learning},
author = {Jonathan Prexl and Sudipan Saha and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang Towards Out-of-distribution Detection for Remote Sensing Journal Article 2021. BibTeX | Tags: @article{towardsGawlikowskiIgarss2021,
title = {Towards Out-of-distribution Detection for Remote Sensing},
author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {IEEE Geoscience and Remote Sensing Symposium 2021 (Accepted)},
publisher = {IEEE},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Saha, Sudipan; Kondmann, Lukas; Zhu, Xiao Xiang Deep No Learning Approach for Unsupervised Change Detection in Hyperspectral Images Journal Article 2021. BibTeX | Tags: @article{deepNoLearningSudipanIsprs2021,
title = {Deep No Learning Approach for Unsupervised Change Detection in Hyperspectral Images},
author = {Sudipan Saha and Lukas Kondmann and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {XXIV ISPRS Congress 2021},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Ebel, Patrick; Saha, Sudipan; Zhu, Xiao Xiang Fusing Multi-modal Data for Supervised Change Detection Journal Article 2021. BibTeX | Tags: @article{fusingPatrickIsprs2021,
title = {Fusing Multi-modal Data for Supervised Change Detection},
author = {Patrick Ebel and Sudipan Saha and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {XXIV ISPRS Congress 2021},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
 | Nandy, Jay; Saha, Sudipan; Hsu, Wynne; Lee, Mong Li; Zhu, Xiao Xiang Covariate Shift Adaptation for Adversarially Robust Classifier Journal Article 2021. BibTeX | Tags: @article{covariateJayIclrW2021,
title = {Covariate Shift Adaptation for Adversarially Robust Classifier},
author = {Jay Nandy and Sudipan Saha and Wynne Hsu and Mong Li Lee and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {ICLR 2021 Workshop on Security and Safety in Machine Learning Systems},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
| Gawlikowski, Jakob; Saha, Sudipan; Kruspe, Anna; Zhu, Xiao Xiang Out-of-distribution Detection in Satellite Image Classification Journal Article 2021. BibTeX | Tags: @article{oodJakobIclrW2021,
title = {Out-of-distribution Detection in Satellite Image Classification},
author = {Jakob Gawlikowski and Sudipan Saha and Anna Kruspe and Xiao Xiang Zhu},
year = {2021},
date = {2021-01-01},
booktitle = {RobustML workshop at ICLR 2021},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
|
2020
|
 | 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}
}
|
