# Deep Learning-Based Classification of High-Resolution Satellite Images for Mangrove Mapping
## 2 Materials and Methods
### Satellite Data  
High-resolution remote sensing images used in this study were from Chinese GF-1, GF-2, GF-6, and ZY-3 satellites. There are currently four GF-1 series satellites operating in orbit. The first satellite, GF-1 01, was successfully launched on 26 April 2013. The PMS (panchromatic/multi-spectra) onboard GF-1 satellites provide panchromatic and multispectral images with resolutions of 2 m and 8 m, respectively. GF-2, which was launched on 19 August 2014, has a spatial resolution of 0.8 m in the panchromatic band and 3.24 m in the multi-spectral band. GF-6 is a low-orbit optical satellite. The onboard PMS provides images with the same resolution as GF-1. ZY-3 01 is the first autonomous high-resolution stereoscopic mapping satellite for civil use in China, launched on 9 January 2012, which provides panchromatic and multispectral images with resolutions of 2.1 m and 5.8 m, respectively.  
In order to extract information of small mangrove patches, we used optical images with a resolution higher than 2 m in this study. In total, 35 scenes of images were collected with cloud coverage values of less than 10%. The information of these images is shown in Table 1. These images were processed by atmospheric correction, orthophoto correction, image fusion, and true color synthesis (Figure 2).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite-Sensor** &
\begin{tabular}{c} **Date** \\ **(MM/DD/YY)** \\ \end{tabular} & **Area** & **Spatial Resolution** & **Image Size** \\ \hline \multirow{6}{*}{GF1-PMS1} & 09/20/2018 & \multirow{6}{*}{Maoming} & Multi Spectral & \multirow{2}{*}{36 km \(\times\) 36 km} \\ \cline{1-1}  & 05/16/2019 & & Scanner (MSS): 8 m & \\ \cline{1-1}  & 11/25/2019 & & Panchromatic & \\ \cline{1-1}  & 10/20/2015 & & (PAN): 2 m & \\ \cline{1-1}  & 01/13/2018 & Taishan & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Information of satellite images.  
Figure 1: Study area.  
spectral hypercubes (FLAASH) model [20]. These images were then orthorectified using reference images and DEM data (resolution of 30 m; [[https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)]([https://earthexplorer.usgs.gov/](https://earthexplorer.usgs.gov/)) (accessed on 25 October 2019)), and the Rational Polynomial Coefficients (RPC) model was applied for correcting the projection error and image geocoding. Next, the multispectral image and the panchromatic band image were registered using the orthorectified panchromatic band image as a reference to ensure that the registration accuracy between the two types of images did not exceed 0.5 pixels. The pansharpen algorithm [21] was used to fuse data, making the multispectral image highly similar to the original panchromatic band image and to reduce spectral distortion. Finally, a true color image was synthesized. These images were mosaicked by county-level administrative districts.  
GF-2 images with a resolution of 0.8 m were all resampled to a grid size of 2 m. Since mangroves are mainly distributed in the coastal intertidal zone, the buffer zone was set to within 1 km of the coastline. Finally, all images were segmented into 2237 samples with a size of 320 \(\times\) 320 pixels.