Abstract
Polarimetric SAR data has the characteristics of all-weather, all-time and so on, which is widely used in many fields. However, the data of annotation is relatively small, which is not conducive to our research. In this paper, we have collected five open polarimetric SAR images, which are images of the San Francisco area. These five images come from different satellites at different times, and has great scientific research value. We annotate the collected images at the pixel level for image classification and segmentation. For the convenience of researchers, the annotated data is open source https://github.com/liuxuvip/PolSF.
Supported in part by the Key Scientific Technological Innovation Research Project by Ministry of Education, the National Natural Science Foundation of China Innovation Research Group Fund (61621005), the State Key Program and the Foundation for Innovative Research Groups of the National Natural Science Foundation of China (61836009), the Major Research Plan of the National Natural Science Foundation of China (91438201, 91438103, and 91838303), the National Natural Science Foundation of China (U1701267, 62076192, 62006177, 61902298, 61573267, and 61906150), the Fund for Foreign Scholars in University Research and Teaching Program’s 111 Project (B07048), the Program for Cheung Kong Scholars and Innovative Research Team in University (IRT 15R53), the ST Innovation Project from the Chinese Ministry of Education, the Key Research and Development Program in Shaanxi Province of China (2019ZDLGY03-06), the National Science Basic Research Plan in Shaanxi Province of China (2019JQ-659, 2022JQ-607).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Chen, B., Huang, B., Bing, X.: Multi-source remotely sensed data fusion for improving land cover classification. ISPRS J. Photogramm. Remote. Sens. 124, 27–39 (2017)
Liu, X., Licheng Jiao, X., Tang, Q.S., Zhang, D.: Polarimetric convolutional network for PolSAR image classification. IEEE Trans. Geosci. Remote Sens. 57(5), 3040–3054 (2019)
Zhang, Z., Wang, H., Feng, X., Jin, Y.-Q.: Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Trans. Geosci. Remote Sens. 55(12), 7177–7188 (2017)
Guo, Y., Jiao, L., Wang, S., Wang, S., Liu, F., Hua, W.: Fuzzy superpixels for polarimetric SAR images classification. IEEE Trans. Fuzzy Syst. 26(5), 2846–2860 (2018)
Bi, H., Sun, J., Xu, Z.: Unsupervised PolSAR image classification using discriminative clustering. IEEE Trans. Geosci. Remote Sens. 55(6), 3531–3544 (2017)
Chen, S., Tao, C.: PolSAR image classification using polarimetric-feature-driven deep convolutional neural network. IEEE Geosci. Remote Sens. Lett. 15(4), 627–631 (2018)
Liu, F., Jiao, L., Hou, B., Yang, S.: Pol-SAR image classification based on Wishart DBN and local spatial information. IEEE Trans. Geosci. Remote Sens. 54(6), 3292–3308 (2016)
Yin, Q., Hong, W., Zhang, F., Pottier, E.: Optimal combination of polarimetric features for vegetation classification in PolSAR image. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 12(10), 3919–3931 (2019)
Zhou, Z.-H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5(1), 44–53 (2017)
San Francisco Polarimetric SAR Datasets. https://www.ietr.fr/polsarpro-bio/san-francisco/
Pottier, E., Sarti, F., Fitrzyk, M., Patruno, J.: PolSARpro-biomass edition: the new ESA polarimetric SAR data processing and educational toolbox for the future ESA & third party fully polarimetric SAR missions (2019)
The ESA PolSARpro v6.0 (Biomass Edition) Software. https://www.ietr.fr/polsarpro-bio/
LabelMe: the open annotation tool. http://labelme.csail.mit.edu/Release3.0/
Acknowledgments
The authors would like to thank IETR provide the PolSAR data.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Liu, X., Jiao, L., Liu, F., Zhang, D., Tang, X. (2022). PolSF: PolSAR Image Datasets on San Francisco. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_23
Download citation
DOI: https://doi.org/10.1007/978-3-031-14903-0_23
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-14902-3
Online ISBN: 978-3-031-14903-0
eBook Packages: Computer ScienceComputer Science (R0)
