论文笔记6：Multi-level Feature Fusion-based CNN for Local Climate Zone Classification
Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
- So2Sat LCZ42 benchmark dataset
- Sen2LCZ-Net
- 实验结果
So2Sat LCZ42 benchmark dataset
遥感影像数据集：
[1] M. Schmitt, L. H. Hughes, C. Qiu, and X. X. Zhu, “Sen12ms–a curated dataset of georeferenced multi-spectral sentinel-1/2 imagery for deep learning and data fusion,” arXiv preprint arXiv:1906.07789, 2019.（多sensors）
[2] P. Helber, B. Bischke, A. Dengel, and D. Borth, “Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp. 2217–2226, 2019.
[3] G. Sumbul, M. Charfuelan, B. Demir, and V. Markl, “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 5901–5904, IEEE, 2019.
Sen2LCZ-Net
The So2Sat LCZ42 dataset consists of LCZ labels of 400673 Sentinel-1 and Sentinel-2 image patches (with a size of 32 × 32) in 42 urban agglomerations (plus 10 additional smaller areas) across the world.
labeling work flow similar to that in WUDAPT
数据集论文：X. X. Zhu, J. Hu, C. Qiu, Y. Shi, J. Kang, L. Mou, H. Bagheri, M. Haberle, Y. Hua, R. Huang, ¨ et al., “So2sat lcz42: A benchmark dataset for global local climate zones classification,” arXiv preprint arXiv:1912.12171, 2019.
实验结果
It can be seen that LCZswith a low producer’s accuracy (lower than 50%) include LCZ5, 7, 10, B, C, and E
J. Rosentreter, R. Hagensieker, and B. Waske, “Towards large-scale mapping of local climate zones using multitemporal sentinel 2 data and convolutional neural networks,” Remote Sensing of Environment, vol. 237, p. 111472, 2020
C. Qiu, M. Schmitt, and X. X. Zhu, “Fusing multi-seasonal sentinel- 2 images with residual convolutional neural networks for local climate zone-derived urban land cover classification,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 5037– 5040, 2019.
G. C. Iannelli and P. Gamba, “Urban extent extraction combining sentinel data in the optical and microwave range,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 12, no. 7, pp. 2209–2216, 2019.