The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models
Open accessAbstract
Abstract In remote sensing images, the absolute orientation of objects is arbitrary. Depending on an object’s orientation and on a sensor’s flight path, objects of the same semantic class can be observed in different orientations in the same image. Equivariance to rotation, in this context understood as responding with a rotated semantic label map when subject to a rotation of the input image, is therefore a very desirable feature, in particular for high capacity models, such as Convolutional Neural Networks (CNNs). If rotation equivariance is encoded in the network, the model is confronted with a simpler task and does not need to learn specific (and redundant) weights to address rotated versions of the same object class. In this work we propose a CNN architecture called Rotation Equivariant Vector Field Network (RotEqNet) to encode rotation equivariance in the network itself. By using rotating convolutions as building blocks and passing only the values corresponding to the maximally activating orientation throughout the network in the form of orientation encoding vector fields, RotEqNet treats rotated versions of the same object with the same filter bank and therefore achieves state-of-the-art performances even when using very small architectures trained from scratch. We test RotEqNet in two challenging sub-decimeter resolution semantic labeling problems, and show that we can perform better than a standard CNN while requiring one order of magnitude less parameters.
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Deep learning in remote sensing applications: A meta-analysis and review
Ma, L., Liu, Y., Zhang, X., Ye, Y., Yin, G., & Johnson, B. A. (2019) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2019.04.015 · Cited by 2,456
LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation
Wang, J., Zheng, Z., Ma, A., Lu, X., & Zhong, Y. (2021) · arXiv · doi:10.48550/arxiv.2110.08733 · Cited by 431
A Relation-Augmented Fully Convolutional Network for Semantic Segmentation in Aerial Scenes
Mou, L., Hua, Y., & Zhu, X. X. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01270 · Cited by 183
Enabling country-scale land cover mapping with meter-resolution satellite imagery
Tong, X.-Y., Xia, G.-S., & Zhu, X. X. (2023) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2022.12.011 · Cited by 125
GeoAI at ACM SIGSPATIAL: progress, challenges, and future directions
Hu, Y., Gao, S., Lunga, D., Li, W., Newsam, S., & Bhaduri, B. (2019) · SIGSPATIAL Special · doi:10.1145/3377000.3377002 · Cited by 58
Metadata
- Authors
- Marcos, D., Volpi, M., Kellenberger, B., & Tuia, D.
- Venue
- ISPRS Journal of Photogrammetry and Remote Sensing
- Publisher
- Elsevier BV
- Published
- 2018-11
- Volume
- 145
- Pages
- 96-107
- ISSN
- 0924-2716 (ISSN-L)
- OpenAlex
- W2793461576
- Cited by
- 282
- References
- 34
- FWCI
- 24.22
- Type
- journal article
- Language
- en
- Open access
- green
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- Funders
- Swiss National Science Foundation (PZ00P2-136827)
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- Marcos, D., Volpi, M., Kellenberger, B., & Tuia, D. (2018). Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models. ISPRS Journal of Photogrammetry and Remote Sensing. https://doi.org/10.1016/j.isprsjprs.2018.01.021
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