gremlin97/RemoteSensingCorpus · Datasets at Hugging Face

text stringlengths 0 820
In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling,
editors, Computer Vision – ECCV 2016 , Lecture Notes in
Computer Science, pages 519–534, Cham, 2016. Springer
International Publishing.
[24] Ian J. Goodfellow, Yoshua Bengio, and Aaron Courville.
Deep Learning . MIT Press, Cambridge, MA, USA, 2016.
http://www.deeplearningbook.org .
[25] Ritwik Gupta, Colorado Reed, Anja Rohrbach, and Trevor
Darrell. Accelerating Ukraine Intelligence Analysis
with Computer Vision on Synthetic Aperture Radar Im-
agery. http://bair.berkeley.edu/blog/2022/03/21/ukraine-sar-
maers/, Mar. 2022.
[26] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr
Doll´ar, and Ross Girshick. Masked Autoencoders Are Scal-
able Vision Learners, Dec. 2021.
[27] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross
Girshick. Momentum contrast for unsupervised visual rep-
resentation learning. In Proceedings of the IEEE/CVF con-
ference on computer vision and pattern recognition , pages
9729–9738, 2020.
[28] Yutong He, Dingjie Wang, Nicholas Lai, William Zhang,
Chenlin Meng, Marshall Burke, David Lobell, and Stefano
Ermon. Spatial-temporal super-resolution of satellite im-
agery via conditional pixel synthesis. Advances in Neural
Information Processing Systems , 34:27903–27915, 2021.
[29] Patrick Helber, Benjamin Bischke, Andreas Dengel, and
Damian Borth. Introducing Eurosat: A Novel Dataset and
Deep Learning Benchmark for Land Use and Land Cover
Classification. In IGARSS 2018 - 2018 IEEE International
Geoscience and Remote Sensing Symposium , pages 204–207,
July 2018.
[30] Olivier Henaff. Data-efficient image recognition with con-
trastive predictive coding. In International conference on
machine learning , pages 4182–4192. PMLR, 2020.
[31] X Hu, H Mu, X Zhang, Z Wang, T Tan, and J Meta-SR Sun.
A magnification-arbitrary network for super-resolution. In
Proceedings of the 2019 IEEE/CVF Conference on Computer
Vision and Pattern Recognition (CVPR), Long Beach, CA,
USA, pages 15–20, 2019.
[32] Damian Iba ˜nez, Ruben Fernandez-Beltran, Filiberto Pla, and
Naoto Yokoya. Masked auto-encoding spectral-spatial trans-
former for hyperspectral image classification. IEEE Transac-
tions on Geoscience and Remote Sensing , 2022.
[33] Pengpeng Ji, Shengye Yan, and Qingshan Liu. Region-Based
Spatial Sampling for Image Classification. In 2013 Seventh
International Conference on Image and Graphics , pages 874–
879, July 2013.
[34] Jan J. Koenderink. The structure of images. Biological
Cybernetics , 50(5):363–370, Aug. 1984.
[35] Pawel Kowaleczko, Tomasz Tarasiewicz, Maciej Ziaja, Daniel
Kostrzewa, Jakub Nalepa, Przemyslaw Rokita, and Michal
Kawulok. Mus2: A benchmark for sentinel-2 multi-image
super-resolution. arXiv preprint arXiv:2210.02745 , 2022.
[36] S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of
Features: Spatial Pyramid Matching for Recognizing Natural
Scene Categories. In 2006 IEEE Computer Society Confer-
ence on Computer Vision and Pattern Recognition (CVPR’06) ,
volume 2, pages 2169–2178, June 2006.
[37] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep
learning. nature , 521(7553):436–444, 2015.
[38] Christian Ledig, Lucas Theis, Ferenc Husz ´ar, Jose Caballero,
Andrew Cunningham, Alejandro Acosta, Andrew Aitken,
Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-
realistic single image super-resolution using a generative ad-
versarial network. In Proceedings of the IEEE conference on
computer vision and pattern recognition , pages 4681–4690,
2017.
[39] Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He,
Bharath Hariharan, and Serge Belongie. Feature Pyramid
Networks for Object Detection. In Proceedings of the IEEE
Conference on Computer Vision and Pattern Recognition ,
pages 2117–2125, 2017.
[40] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian
Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg.
SSD: Single Shot MultiBox Detector. In Bastian Leibe, Jiri
Matas, Nicu Sebe, and Max Welling, editors, Computer Vi-
sion – ECCV 2016 , Lecture Notes in Computer Science, pages
21–37, Cham, 2016. Springer International Publishing.
[41] Yufei Liu, Xiaorun Li, Ziqiang Hua, Chaoqun Xia, and Liaoy-
ing Zhao. A band selection method with masked convolu-
tional autoencoder for hyperspectral image. IEEE Geoscience
and Remote Sensing Letters , 2022.
[42] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feicht-
enhofer, Trevor Darrell, and Saining Xie. A ConvNet for the
2020s. In Proceedings of the IEEE/CVF Conference on Com-
puter Vision and Pattern Recognition , pages 11976–11986,
2022.
[43] Gabriel Machado, Edemir Ferreira, Keiller Nogueira, Hugo
Oliveira, Matheus Brito, Pedro Henrique Targino Gama, and
Jefersson Alex dos Santos. AiRound and CV-BrCT: Novel
Multiview Datasets for Scene Classification. IEEE Journal
of Selected Topics in Applied Earth Observations and Remote
Sensing , 14:488–503, 2021.
[44] Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat,
and Pierre Alliez. Can semantic labeling methods generalize
to any city? the inria aerial image labeling benchmark. In
IEEE International Geoscience and Remote Sensing Sympo-
sium (IGARSS) . IEEE, 2017.
[45] Malachy Moran, Kayla Woputz, Derrick Hee, Manuela
Girotto, Paolo D’Odorico, Ritwik Gupta, Daniel Feldman,

In Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling,

editors, Computer Vision – ECCV 2016 , Lecture Notes in

Computer Science, pages 519–534, Cham, 2016. Springer

International Publishing.

[24] Ian J. Goodfellow, Yoshua Bengio, and Aaron Courville.

Deep Learning . MIT Press, Cambridge, MA, USA, 2016.

http://www.deeplearningbook.org .

[25] Ritwik Gupta, Colorado Reed, Anja Rohrbach, and Trevor

Darrell. Accelerating Ukraine Intelligence Analysis

with Computer Vision on Synthetic Aperture Radar Im-

agery. http://bair.berkeley.edu/blog/2022/03/21/ukraine-sar-

maers/, Mar. 2022.

[26] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr

Doll´ar, and Ross Girshick. Masked Autoencoders Are Scal-

able Vision Learners, Dec. 2021.

[27] Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross

Girshick. Momentum contrast for unsupervised visual rep-

resentation learning. In Proceedings of the IEEE/CVF con-

ference on computer vision and pattern recognition , pages

9729–9738, 2020.

[28] Yutong He, Dingjie Wang, Nicholas Lai, William Zhang,

Chenlin Meng, Marshall Burke, David Lobell, and Stefano

Ermon. Spatial-temporal super-resolution of satellite im-

agery via conditional pixel synthesis. Advances in Neural

Information Processing Systems , 34:27903–27915, 2021.

[29] Patrick Helber, Benjamin Bischke, Andreas Dengel, and

Damian Borth. Introducing Eurosat: A Novel Dataset and

Deep Learning Benchmark for Land Use and Land Cover

Classification. In IGARSS 2018 - 2018 IEEE International

Geoscience and Remote Sensing Symposium , pages 204–207,

July 2018.

[30] Olivier Henaff. Data-efficient image recognition with con-

trastive predictive coding. In International conference on

machine learning , pages 4182–4192. PMLR, 2020.

[31] X Hu, H Mu, X Zhang, Z Wang, T Tan, and J Meta-SR Sun.

A magnification-arbitrary network for super-resolution. In

Proceedings of the 2019 IEEE/CVF Conference on Computer

Vision and Pattern Recognition (CVPR), Long Beach, CA,

USA, pages 15–20, 2019.

[32] Damian Iba ˜nez, Ruben Fernandez-Beltran, Filiberto Pla, and

Naoto Yokoya. Masked auto-encoding spectral-spatial trans-

former for hyperspectral image classification. IEEE Transac-

tions on Geoscience and Remote Sensing , 2022.

[33] Pengpeng Ji, Shengye Yan, and Qingshan Liu. Region-Based

Spatial Sampling for Image Classification. In 2013 Seventh

International Conference on Image and Graphics , pages 874–

879, July 2013.

[34] Jan J. Koenderink. The structure of images. Biological

Cybernetics , 50(5):363–370, Aug. 1984.

[35] Pawel Kowaleczko, Tomasz Tarasiewicz, Maciej Ziaja, Daniel

Kostrzewa, Jakub Nalepa, Przemyslaw Rokita, and Michal

Kawulok. Mus2: A benchmark for sentinel-2 multi-image

super-resolution. arXiv preprint arXiv:2210.02745 , 2022.

[36] S. Lazebnik, C. Schmid, and J. Ponce. Beyond Bags of

Features: Spatial Pyramid Matching for Recognizing Natural

Scene Categories. In 2006 IEEE Computer Society Confer-

ence on Computer Vision and Pattern Recognition (CVPR’06) ,

volume 2, pages 2169–2178, June 2006.

[37] Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. Deep

learning. nature , 521(7553):436–444, 2015.

[38] Christian Ledig, Lucas Theis, Ferenc Husz ´ar, Jose Caballero,

Andrew Cunningham, Alejandro Acosta, Andrew Aitken,

Alykhan Tejani, Johannes Totz, Zehan Wang, et al. Photo-

realistic single image super-resolution using a generative ad-

versarial network. In Proceedings of the IEEE conference on

computer vision and pattern recognition , pages 4681–4690,

2017.

[39] Tsung-Yi Lin, Piotr Dollar, Ross Girshick, Kaiming He,

Bharath Hariharan, and Serge Belongie. Feature Pyramid

Networks for Object Detection. In Proceedings of the IEEE

Conference on Computer Vision and Pattern Recognition ,

pages 2117–2125, 2017.

[40] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian

Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg.

SSD: Single Shot MultiBox Detector. In Bastian Leibe, Jiri

Matas, Nicu Sebe, and Max Welling, editors, Computer Vi-

sion – ECCV 2016 , Lecture Notes in Computer Science, pages

21–37, Cham, 2016. Springer International Publishing.

[41] Yufei Liu, Xiaorun Li, Ziqiang Hua, Chaoqun Xia, and Liaoy-

ing Zhao. A band selection method with masked convolu-

tional autoencoder for hyperspectral image. IEEE Geoscience

and Remote Sensing Letters , 2022.

[42] Zhuang Liu, Hanzi Mao, Chao-Yuan Wu, Christoph Feicht-

enhofer, Trevor Darrell, and Saining Xie. A ConvNet for the

2020s. In Proceedings of the IEEE/CVF Conference on Com-

puter Vision and Pattern Recognition , pages 11976–11986,

2022.

[43] Gabriel Machado, Edemir Ferreira, Keiller Nogueira, Hugo

Oliveira, Matheus Brito, Pedro Henrique Targino Gama, and

Jefersson Alex dos Santos. AiRound and CV-BrCT: Novel

Multiview Datasets for Scene Classification. IEEE Journal

of Selected Topics in Applied Earth Observations and Remote

Sensing , 14:488–503, 2021.

[44] Emmanuel Maggiori, Yuliya Tarabalka, Guillaume Charpiat,

and Pierre Alliez. Can semantic labeling methods generalize

to any city? the inria aerial image labeling benchmark. In

IEEE International Geoscience and Remote Sensing Sympo-

sium (IGARSS) . IEEE, 2017.

[45] Malachy Moran, Kayla Woputz, Derrick Hee, Manuela

Girotto, Paolo D’Odorico, Ritwik Gupta, Daniel Feldman,