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Leveraging Bioclimatic Context for Supervised and Self-supervised Land Cover Classification

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Pattern Recognition (DAGM GCPR 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14264))

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Abstract

Modern neural networks achieve state-of-the-art results on land cover classification from satellite imagery, as is the case for almost all vision tasks. One of the main challenges in this context is dealing with geographic variability in both image and label distributions. To tackle this problem, we study the effectiveness of incorporating bioclimatic information into neural network training and prediction. Such auxiliary data can easily be extracted from freely available rasters at satellite images’ georeferenced locations. We compare two methods of incorporation, learned embeddings and conditional batch normalization, to a bioclimate-agnostic baseline ResNet18. In our experiments on the EuroSAT and BigEarthNet datasets, we find that especially the use of conditional batch normalization improves the network’s overall accuracy, generalizability, as well as training efficiency, in both a supervised and a self-supervised learning setup. Code and data are publicly available at https://t.ly/NDQFF.

This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) - SFB 1502/1-2022 - Projektnummer: 450058266 and partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC 2070 – 390732324.

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References

  1. Alemohammad, H., Booth, K.: LandCoverNet: a global benchmark land cover classification training dataset. In: AI for Earth Sciences Workshop at NeurIPS (2020)

    Google Scholar 

  2. Audebert, N., Le Saux, B., Lefèvre, S.: Beyond RGB: very high resolution urban remote sensing with multimodal deep networks. ISPRS J. Photogramm. Remote. Sens. 140, 20–32 (2018)

    Article  Google Scholar 

  3. Ayush, K., et al.: Geography-aware self-supervised learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 10181–10190 (2021)

    Google Scholar 

  4. Cao, Y., Steffey, S., He, J., Xiao, D., Tao, C., Chen, P., Müller, H.: Medical image retrieval: a multimodal approach. Cancer Inform. 13, CIN–S14053 (2014)

    Google Scholar 

  5. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20, 273–297 (1995)

    Article  Google Scholar 

  6. De Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., Courville, A.C.: Modulating early visual processing by language. In: Advances in Neural Information Processing Systems, vol. 30 (2017)

    Google Scholar 

  7. Demir, I., et al.: DeepGlobe 2018: a challenge to parse the earth through satellite images. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 172–181 (2018)

    Google Scholar 

  8. Dumoulin, V., Shlens, J., Kudlur, M.: A learned representation for artistic style. In: International Conference on Learning Representations (2016)

    Google Scholar 

  9. Eddin, M.H.S., Roscher, R., Gall, J.: Location-aware adaptive normalization: a deep learning approach for wildfire danger forecasting. IEEE Trans. Geosci. Remote Sens. 61, 1–18 (2023)

    Article  Google Scholar 

  10. EEA: Biogeographical regions Europe 2016 (2016). https://www.eea.europa.eu/ds_resolveuid/9b7911cc33ad4a9c940847a7ff653a40

  11. Farr, T.G., et al.: The shuttle radar topography mission. Rev. Geophys. 45(2) (2007)

    Google Scholar 

  12. Fick, S.E., Hijmans, R.J.: WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017). https://worldclim.org/data/worldclim21.html

  13. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

    Google Scholar 

  14. Heidler, K., et al.: Self-supervised audiovisual representation learning for remote sensing data. Int. J. Appl. Earth Obs. Geoinf. 116, 103130 (2023)

    Google Scholar 

  15. Helber, P., Bischke, B., Dengel, A., Borth, D.: EuroSAT: a novel dataset and deep learning benchmark for land use and land cover classification. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(7), 2217–2226 (2019)

    Article  Google Scholar 

  16. Hu, L., Robinson, C., Dilkina, B.: Model generalization in deep learning applications for land cover mapping. arXiv preprint arXiv:2008.10351 (2020)

  17. Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1501–1510 (2017)

    Google Scholar 

  18. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)

    Google Scholar 

  19. Jain, A., Singh, A., Koppula, H.S., Soh, S., Saxena, A.: Recurrent neural networks for driver activity anticipation via sensory-fusion architecture. In: IEEE International Conference on Robotics and Automation, pp. 3118–3125 (2016)

    Google Scholar 

  20. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference for Learning Representations (2014)

    Google Scholar 

  21. Kramer, M.A.: Nonlinear principal component analysis using autoassociative neural networks. AIChE J. 37(2), 233–243 (1991)

    Article  Google Scholar 

  22. Lenz, I., Lee, H., Saxena, A.: Deep learning for detecting robotic grasps. Int. J. Robot. Res. 34(4–5), 705–724 (2015)

    Article  Google Scholar 

  23. Li, W., Chen, K., Chen, H., Shi, Z.: Geographical knowledge-driven representation learning for remote sensing images. IEEE Trans. Geosci. Remote Sens. 60, 1–16 (2021)

    Google Scholar 

  24. Li, Y., Wang, N., Shi, J., Hou, X., Liu, J.: Adaptive batch normalization for practical domain adaptation. Pattern Recogn. 80, 109–117 (2018)

    Article  Google Scholar 

  25. Liang, M., Li, Z., Chen, T., Zeng, J.: Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach. IEEE/ACM Trans. Comput. Biol. Bioinf. 12(4), 928–937 (2014)

    Article  Google Scholar 

  26. Lu, X., Gong, T., Zheng, X.: Multisource compensation network for remote sensing cross-domain scene classification. IEEE Trans. Geosci. Remote Sens. 58(4), 2504–2515 (2019)

    Article  Google Scholar 

  27. Marín, J., Escalera, S.: SSSGAN: satellite style and structure generative adversarial networks. Remote Sens. 13(19), 3984 (2021)

    Article  Google Scholar 

  28. Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning, pp. 689–696 (2011)

    Google Scholar 

  29. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (2019)

    Google Scholar 

  30. Perez, E., Strub, F., De Vries, H., Dumoulin, V., Courville, A.: FiLM: visual reasoning with a general conditioning layer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)

    Google Scholar 

  31. Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96–108 (2017)

    Article  Google Scholar 

  32. Scheibenreif, L., Hanna, J., Mommert, M., Borth, D.: Self-supervised vision transformers for land-cover segmentation and classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1422–1431 (2022)

    Google Scholar 

  33. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  34. Sheth, I., Rahman, A.A., Havaei, M., Kahou, S.E.: Pitfalls of conditional batch normalization for contextual multi-modal learning. In: I Can’t Believe It’s Not Better Workshop at NeurIPS (2022)

    Google Scholar 

  35. Srivastava, N., Salakhutdinov, R.R.: Multimodal learning with deep Boltzmann machines. In: Advances in Neural Information Processing Systems, vol. 25 (2012)

    Google Scholar 

  36. Suel, E., Bhatt, S., Brauer, M., Flaxman, S., Ezzati, M.: Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas. Remote Sens. Environ. 257, 112339 (2021)

    Article  Google Scholar 

  37. Sumbul, G., Charfuelan, M., Demir, B., Markl, V.: BigEarthNet: a large-scale benchmark archive for remote sensing image understanding. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 5901–5904. IEEE (2019)

    Google Scholar 

  38. Sumbul, G., et al.: BigEarthNet-MM: a large-scale, multimodal, multilabel benchmark archive for remote sensing image classification and retrieval [software and data sets]. IEEE Geosci. Remote Sens. Mag. 9(3), 174–180 (2021)

    Article  Google Scholar 

  39. Sun, Y., Hua, Y., Mou, L., Zhu, X.X.: CG-Net: conditional GIS-aware network for individual building segmentation in VHR SAR images. IEEE Trans. Geosci. Remote Sens. 60, 1–15 (2021)

    Google Scholar 

  40. Tang, K., Paluri, M., Fei-Fei, L., Fergus, R., Bourdev, L.: Improving image classification with location context. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1008–1016 (2015)

    Google Scholar 

  41. Townshend, J.G.: Land cover. Int. J. Remote Sens. 13(6–7), 1319–1328 (1992)

    Article  Google Scholar 

  42. Tuia, D., Persello, C., Bruzzone, L.: Domain adaptation for the classification of remote sensing data: an overview of recent advances. IEEE Geosci. Remote Sens. Mag. 4(2), 41–57 (2016)

    Article  Google Scholar 

  43. Turner, M.G., Gardner, R.H.: Landscape Ecology in Theory and Practice. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-2794-4

    Book  Google Scholar 

  44. Wang, J., Zheng, Z., Ma, A., Lu, X., Zhong, Y.: LoveDA: a remote sensing land-cover dataset for domain adaptive semantic segmentation. In: NeurIPS Datasets and Benchmarks Track (2021)

    Google Scholar 

  45. Wang, X., Yu, K., Dong, C., Loy, C.C.: Recovering realistic texture in image super-resolution by deep spatial feature transform. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 606–615 (2018)

    Google Scholar 

  46. Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)

    Article  Google Scholar 

  47. Wickramasinghe, C.S., Marino, D.L., Manic, M.: ResNet autoencoders for unsupervised feature learning from high-dimensional data: deep models resistant to performance degradation. IEEE Access 9, 40511–40520 (2021)

    Article  Google Scholar 

  48. Wulder, M.A., Coops, N.C., Roy, D.P., White, J.C., Hermosilla, T.: Land cover 2.0. Int. J. Remote Sens. 39(12), 4254–4284 (2018)

    Article  Google Scholar 

  49. Zhao, H., Gallo, O., Frosio, I., Kautz, J.: Loss functions for image restoration with neural networks. IEEE Trans. Comput. Imaging 3(1), 47–57 (2016)

    Article  Google Scholar 

  50. Zhu, P., Abdal, R., Qin, Y., Wonka, P.: SEAN: image synthesis with semantic region-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5104–5113 (2020)

    Google Scholar 

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Authors and Affiliations

  1. University of Bonn, Bonn, Germany

    Johannes Leonhardt, Lukas Drees, Jürgen Gall & Ribana Roscher

  2. Lamarr Institute for Machine Learning and Artificial Intelligence, Dortmund, Germany

    Jürgen Gall

  3. Forschungszentrum Jülich GmbH, Jülich, Germany

    Ribana Roscher

Authors
  1. Johannes Leonhardt
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  2. Lukas Drees
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  3. Jürgen Gall
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  4. Ribana Roscher
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Correspondence to Johannes Leonhardt .

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Editors and Affiliations

  1. IWR, Heidelberg University, Heidelberg, Germany

    Ullrich Köthe

  2. IWR, Heidelberg University, Heidelberg, Germany

    Carsten Rother

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Leonhardt, J., Drees, L., Gall, J., Roscher, R. (2024). Leveraging Bioclimatic Context for Supervised and Self-supervised Land Cover Classification. In: Köthe, U., Rother, C. (eds) Pattern Recognition. DAGM GCPR 2023. Lecture Notes in Computer Science, vol 14264. Springer, Cham. https://doi.org/10.1007/978-3-031-54605-1_15

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  • DOI: https://doi.org/10.1007/978-3-031-54605-1_15

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