# Evaluating the Efficacy of Segment Anything Model for
## 3 Methodology
### Data Preprocessing and Image Segmentation  
This study delves into the feasibility of employing SAM for diverse types of remote sensing data segmentation, encompassing various stages such as data preprocessing, segmentation, accuracy evaluation, data analysis, and more. The comprehensive operational workflow is elucidated in Figure 5. In preparation for image segmentation, preliminary data preprocessing is essential for the original images. Due to variations in data acquisition methods, distinct types of data necessitate separate processing approaches. For high-resolution remote sensing images, such as 0.5 m, 1 m, and 3 m, RGB bands were used for segmentation. In the case of 10 m and 30 m resolution remote sensing images, bands 2, 3, and 4 were combined to generate three-band RGB images. Beyond data fusion, to facilitate a more systematic evaluation, we have categorized the data into two distinct types:  
1. Evaluation of SAM results for images with varied resolutions covering the same study area frame;  
Figure 2: Schematic diagram of SAM operation for ARSI segmentation by selecting samples. Evaluation of SAM results for images of different study areas at the same spatial resolution. To mitigate the impact of variable images on test results, image cropping becomes imperative, requiring standardization and harmonization of variables. Comprehensive details regarding the original image size, resolution, and the resulting cut image size and resolution are presented in Tables 2 and 3. To ensure uniformity in image format, we converted all images to JPG format. Typically, remote sensing images downloaded directly from websites are in TIF format, which is presently not compatible with SAM. This comprehensive testing approach enables us to validate SAM's applicability in the segmentation of various types of remote sensing images. Figure 4: Visualization of a single segmented image. Figure 3: Original image and corresponding segmented image. Upon completing the data preprocessing, the data can be systematically input into the SAM for segmentation testing. We employ two segmentation methods, condition-based segmentation and unsupervised segmentation, to assess and compare the segmentation results across various types of scenarios depicted in Tables 2 and 3. Conditional-based segmentation, also recognized as a form of approximate supervised learning as described in Section 3.2. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Spatial Resolution** & \
\begin{tabular}{c} **Original** \\ **Image Size** \\ **(in Pixels)** \\ \end{tabular} &
\begin{tabular}{c} **Output Image Size (in** \\ **Pixels)** \\ \end{tabular} & **Sensor** & **Spectral Bands Used** \\ \hline
0.5 m & 10,000 \(\times\) 10,000 & 8000 \(\times\) 8000 & Z/1 DMC & RGB \\
1 m & 5000 \(\times\) 5000 & 4000 \(\times\) 4000 & Leica ADS80 & RGB \\
3 m & 8908 \(\times\) 4319 & 2000 \(\times\) 2000 & Dove Classic (PS2) & RGB \\
10 m & 10,980 \(\times\) 10,980 & 400 \(\times\) 400 & MSI & RGB \\
20 m & 5490 \(\times\) 5490 & 200 \(\times\) 200 & MSI & RGB \\
30 m & 1830 \(\times\) 1830 & 150 \(\times\) 150 & OLI & RGB \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Images with varied resolutions within the same study area frame size. For 30 m resolution, we have used the slightly bigger frame size to make the computations easier, while it covers the same study area as others. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Resolution** & \begin{tabular}{c} **Large Size** \\ **(in Pixels)** \\ \end{tabular}  & \begin{tabular}{c} **Medium Size (in** \\ **Pixels)** \\ \end{tabular}  & \ \begin{tabular}{c} **Small Area Size** \\ **(in Pixels)** \\ \end{tabular}  & **Sensor** & \begin{tabular}{c} **Spectral Bands** \\ **Used** \\ \end{tabular}