# PolSF: PolSAR Image Datasets on San Francisco

Xu Liu, Licheng Jiao, Fang Liu, Dan Zhang, Xu Tang

► To cite this version:

Xu Liu, Licheng Jiao, Fang Liu, Dan Zhang, Xu Tang. PolSF: PolSAR Image Datasets on San Francisco. 5th International Conference on Intelligence Science (ICIS), Oct 2022, Xi'an, China. pp.214-219, 10.1007/978-3-031-14903-0_23. hal-04666445

HAL Id: hal-04666445

https://hal.science/hal-04666445v1

Submitted on 1 Aug 2024

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>
<figure><img src="image_2.png" /></figure>

ifip

International Federation for Information Processing

This document is the original author manuscript of a paper submitted to an IFIP
conference proceedings or other IFIP publication by Springer Nature. As such, there
may be some differences in the official published version of the paper. Such
differences, if any, are usually due to reformatting during preparation for publication or
minor corrections made by the author(s) during final proofreading of the publication
manuscript.
# PolSF: PolSAR image datasets on San Francisco *

Xu Liu, Licheng Jiao, Fang Liu, Dan Zhang, and Xu Tang

Xidian University, xi'an, shannxi, China
xuliu361@163.com

**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.

**Keywords:** PolSAR image classification segmentation.

## 1 Introduction

With the development of sensors, studying their differences and characteristics are significative research topics [1]. Different sensors produce different characteristics of data and images. That is to say, each kind of data has its own characteristics and advantages. Making full use of these data is significative and challenging.

In the literature, polarimetric SAR image classification is a hot topic [2], [3], [4], [5], [6], [7], [8]. polarimetric SAR image classification is also a pixel level remote sensing image interpretation task, which has the characteristics of fine recognition and need to determine the category of each pixel.

Polarimetric SAR image classification tasks can be divided into unsupervised classification, semi-supervised classification and supervised classification according to the label of training data. According to the quality of the label, it can be

* 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 at ÁŽs 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)
divided into strong supervised learning and weak supervised learning, the latter
includes incomplete supervised learning, inexact supervised learning and inaccu-
rate supervised learning [9]. Therefore, it can adopt different learning methods
and ideas to deal with polarimetric SAR image classification problems. However,
there are very few labeled data that can be used in practical research.

In this paper, we introduced the data and gave our labeled ground truth.
To meet the needs of researchers. First, we download the original data from
the website [10]. Second, we use the ESA PolSARpro v6.0 (Biomass Edition)
software [11], [12] read the original data files and get the PauliRGB images.
Third, we get the high-resolution Google map of the same period, mark the image
with its information by the labelme software [13]. Finally, the marked color map
is remapped and encoded to get the mark file. In the following chapters, dataset
details will be introduced.

<figure><img src="image_3.png" /><figcaption>Fig. 1. The PolSAR data in the web.</figcaption></figure>

## 2 PolSF

In this section, the PolSAR dataset PolSF is presented [See Fig 4]. The original PolSAR data is downloaded the IETR website[See Fig 4] [10]. We select five the PolSAR data of five satellites for the San Francisco area, cut and mark the adjacent area. Fig 2 show different raw data packages, these data are uploaded
in August 9th, 2019. In addition, we can see from Fig 3 that the data formats of different satellite imaging are different. Table 1 also give the details of the satelites.

<figure><img src="image_4.png" /><figcaption>Fig. 2. PolSF dataset files.</figcaption></figure>

## 2.1 PolSF-AIRSAR

The AIRSAR data is shown in Fig4(a) and Fig4(f). The imaging time is 1989.08. The spatial resolution is 10m. Fig4(a) is a pseudo color image formed by PauliRG-B decomposition. The size of original image is 1024×900. Fig4(b) is the ground truth marked by our team IPIU, there are five categories of objectives. i.e. 1, Montain, 2, Water, 3, Urban, 4, Vegetation, 5, Bare soil.

## 2.2 PolSF-ALOS2

The ALOS2 data is shown in Fig. 4(b) and Fig. 4(g). The imaging time is 2015.03. The spatial resolution is 18m. The size of original image is 8080×22608, the coordinates of cut region is (x1:736, y1:2832, x2:3520, y2:7888), which is
the coordinates of the upper left corner and the lower right corner. Fig. 4(a)
is a pseudo color image formed by PauliRGB decomposition. Fig. 4(b) is the
ground truth marked by our team IPIU, there are six categories of objectives.
i.e. 1, Montain, 2, Water, 3, Vegetation, 4, High-Density Urban, 5, Low-Density
Urban, 6, Developd.

AIRSAR

<figure><img src="image_5.png" /></figure>

san_frankisco900x1024.stk

ALOS1

<figure><img src="image_6.png" /></figure>

GF3

<figure><img src="image_7.png" /></figure>

RISAT

<figure><img src="image_8.png" /></figure>

S1A

<figure><img src="image_9.png" /></figure>

S1B

<caption>Fig. 3. The data format in each satellite.</caption>

## 2.3 PolSF-GF3

The GF3 data is shown in Fig. 4(c) and Fig. 4(h). The imaging time is 2018.08.
The spatial resolution is 8m. The size of original image is 5829×7173, the coordi-
nates of cut region is (1144, 3464, 3448, 6376). Fig. 4(a) is a pseudo color image
formed by PauliRGB decomposition. Fig. 4. (b) is the ground truth marked by
our team IPIU, there are six categories of objectives. i.e. 1, Montain, 2, Water,
3, Vegetation, 4, High-Density Urban, 5, Low-Density Urban, 6, Developd.

## 2.4 PolSF-RISAT

For this image, the size of original image is 8719×13843, the coordinates of cut
region is (2486, 4257, 7414, 10648). The RISAT data is shown in Fig. 4(d) and
Fig. 4(i). The imaging time is 2016.08. The spatial resolution is 2.33m. Fig. 4(a)
is a pseudo color image formed by PauliRGB decomposition. Fig. 4(b) is the
ground truth marked by our team IPIU, there are six categories of objectives.
i.e. 1, Montain, 2, Water, 3, Vegetation, 4, High-Density Urban, 5, Low-Density
Urban, 6, Developd.

## 2.5 PolSF-RS2

The RS2 data is shown in Fig. 4(e) and Fig. 4(j). The size of original image is
2823×14416, the coordinates of cut region is (7326,661,9125,2040). The imaging
time is 2008.04. The spatial resolution is 8m. Fig. 4(a) is a pseudo color image
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** PolSF Dataset visualization and annotation. (a)-(e), PauliRGB image of SF-AIRSAR, SF-ALOS2, SF-GF3, SF-RISAT and SF-RS2. (f)-(j), Color ground truth of SF-AIRSAR, SF-ALOS2, SF-GF3, SF-RISAT and SF-RS2.</figcaption></figure>

formed by PauliRGB decomposition. Fig. 4(b) is the ground truth, there are five categories of objectives. i.e. 1, Water, 2, Vegetation, 3, High-Density Urban, 4, Low-Density Urban, 5, Developd.

<table><caption>**Table 1.** The details of each image.</caption><thead><tr><td>Sensors</td><td>Time</td><td>Resolution</td><td>Dimensions</td><td>Class</td><td>PolarType</td></tr></thead><tbody><tr><td>SF-AIRSAR</td><td>1989.08</td><td>10m</td><td>1024*900</td><td>5</td><td>full</td></tr><tr><td>SF-ALOS2</td><td>2015.03</td><td>18m</td><td>2784*5056</td><td>6</td><td>full</td></tr><tr><td>SF-GF3</td><td>2018.08</td><td>8m</td><td>2304*2912</td><td>6</td><td>full</td></tr><tr><td>SF-RISAT</td><td>2016.08</td><td>2.33m</td><td>4928*6391</td><td>6</td><td>ppl</td></tr><tr><td>SF-RS2</td><td>2008.04</td><td>8m</td><td>1380*1800</td><td>5</td><td>full</td></tr></tbody></table>

## 3 CONCLUSIONS

In this paper, we have published five image marker maps. These five images come from different satellites and have different data characteristics. There are similarities in the changes. They are suitable for further scientific research, such
as single source image pixel level classification, multi-source image pixel level
fusion classification, etc.

4 Acknowledgments

The authors would like to thank IETR provide the PolSAR data.

References

1. Bin Chen, Bo Huang, and Bing Xu, "Multi-source remotely sensed data fusion for improving land cover classification," *ISPRS Journal of Photogrammetry and Remote Sensing*, vol. 124, pp. 27-39, 2017.

2. Xu Liu, Licheng Jiao, Xu Tang, Qigong Sun, and Dan Zhang, "Polarimetric convolutional network for polsar image classification," *IEEE Trans. Geosci. Remote Sens.*, vol. 57, no. 5, pp. 3040-3054, May 2019.

3. Zhimian Zhang, Haipeng Wang, Feng Xu, and Ya-Qiu Jin, "Complex-valued convolutional neural network and its application in polarimetric sar image classification," *IEEE Trans. Geosci. Remote Sens.*, vol. 55, no. 12, pp. 7177-7188, 2017.

4. Y. Guo, L. Jiao, S. Wang, S. Wang, F. Liu, and W. Hua, "Fuzzy superpixels for polarimetric sar images classification," *IEEE Transactions on Fuzzy Systems*, vol. 26, no. 5, pp. 2846-2860, Oct 2018.

5. H. Bi, J. Sun, and Z. Xu, "Unsupervised polsar image classification using discriminative clustering," *IEEE Transactions on Geoscience and Remote Sensing*, vol. 55, no. 6, pp. 3531-3544, June 2017.

6. S. Chen and C. Tao, "Polsar image classification using polarimetric-feature-driven deep convolutional neural network," *IEEE Geoscience and Remote Sensing Letters*, vol. 15, no. 4, pp. 627-631, April 2018.

7. F. Liu, L. Jiao, B. Hou, and S. Yang, "Pol-sar image classification based on wishart dbn and local spatial information," *IEEE Transactions on Geoscience and Remote Sensing*, vol. 54, no. 6, pp. 3292-3308, June 2016.

8. Q. Yin, W. Hong, F. Zhang, and E. Pottier, "Optimal combination of polarimetric features for vegetation classification in polsar image," *IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing*, vol. 12, no. 10, pp. 3919-3931, Oct 2019.

9. Zhi-Hua Zhou, "A brief introduction to weakly supervised learning," *National Science Review*, vol. 5, no. 1, pp. 44-53, 2017.

10. "San Francisco Polarimetric SAR Datasets," Online: https://www.ietr.fr/polsarpro-bio/san-francisco/.

11. E Pottier, F Sarti, M Fitrzyk, and Jolanda Patruno, "Polsarpro-biomass edition: The new esa polarimetric sar data processing and educational toolbox for the future esa & third party fully polarimetric sar missions," 2019.

12. "The ESA PolSARpro v6.0 (Biomass Edition) Software," Online: https://www.ietr.fr/polsarpro-bio/.

13. "LabelMe: the open annotation tool.," Online: http://labelme.csail.mit.edu/Release3.0/.