## 3 Selection of Methods for Methodology
### Visual Characteristics (Features) of the Satellite Image Content  
Satellite images capture a wide range of visual characteristics that provide valuable information about the Earth's surface and the objects it contains. Our methodology is designated for selecting the optimal segmentation method considering these characteristics of the image content. Many visual features may significantly influence the results of segmentation algorithms. Thus, in our proposed methodology, the quality of image segmentation is determined by the image content and the sensitivity of the segmentation algorithm, i.e., how certain visual features influence the segmentation result. For this research, we selected five distinct visual features to construct the initial image vector: contrast, roughness, regions of very small size, distortions of regions, and smoothness (continuity) of region edges. All mentioned visual features are discussed below. Satellite images with high contrast make distinguishing between different land covers easier, as there is a clear separation between the pixel intensities, improving the segmentation method accuracy for identifying the region boundaries. Brightness contrast (or global contrast) determines the difference in brightness between the lightest and darkest parts of an image. An image with high brightness contrast will  
Figure 1: General framework for the selection of optimal segmentation algorithm for satellite images. have bright highlights and dark shadows, while an image with low contrast will have a uniform brightness across the entire image. Color contrast refers to the distinction in brightness between two neighboring colors or overlapping colors, typically involving a foreground and background color [1]. An image with high color contrast will have vivid and distinct colors, while an image with low color contrast will have muted or similar colors. Color-based image segmentation can sometimes encounter challenges when visually distinct colors are close to each other in the chosen color space and are incorrectly assigned to the same cluster. Contrast adjustment is a common preprocessing step incorporated into various segmentation algorithms to enhance segmentation accuracy by emphasizing edge pixels. Figure 2 shows low brightness contrast (Figure 2a,b) compared to high color contrast in images (Figure 2c,d). **Roughness** refers to the degree of irregularity or coarseness in an image's texture or surface [22]. This irregularity or coarseness can manifest as pixel color values, intensity levels, or texture pattern fluctuations. In a color image, roughness can be used to describe how color values change spatially. Rapid fluctuations in pixel color values across an area of the image indicate a high roughness (Figure 3c,d). Low roughness (Figure 3a,b) suggests a smoother, uniform appearance, such as water, and is easier to segment. While high-roughness regions can be more challenging to segment, roughness can help identify different regions of an image if they have different color fluctuations. Low-level features like roughness may not necessarily correspond to objects with semantic meaning, such as bushes in the desert. High roughness is related to the inherent characteristics of the image or object and represents meaningful visual content. However, high roughness and noise can sometimes be related or occur together. Noise is unrelated to the image and represents unwanted variations introduced during satellite image acquisition or processing. Figure 2: Contrast features of different regions: (**a**,**b**) low contrast between river and forest, (**c**,**d**) high contrast between river and forest. (**a**) “river_5.jpg”, (**b**) “river_10.jpg”, (**c**) “river_327.jpg”, (**d**) “river_249.jpg”. Images from the Aerial Image Dataset (AID) [39]. The characteristics of texture or roughness can vary depending on the direction. Anisotropic texture in one direction (e.g., along the dune crests) might appear relatively smooth and homogeneous.