Figure 3. An example of the shadow convolution method applied to test data. The shadow area is fixed at a value of 1 and the rest of the image is randomly sampled from the image population. Convolution with the HiRISE Point Spread Function (PSF) yields the theoretical observation by HiRISE. After convolution, the original shadow area, marked in black, is examined to understand the post-convolution brightness within the shadow.

3.2. Boulder Segmentation

The second major step is to select and define individual shadows in the segmented image created in the first step, Image Preparation and Boundary Selection. We use a watershed algorithm to identify individual shadows, treating the dark shadows as basins and the relatively bright exteriors as plateaus and separating shadows that touch one another (e.g., Fig. 4d, S2). Local minima in the DN are used as seeds (i.e., starting points) for the watershed algorithm. This process readily identifies individual shadows and divides multiple shadows that form a merged shadow region. This process can create some false splitting in shadows with multiple minima which is corrected later in the process. The watershed process also creates unique IDs (called flags) for each shadow. In this step, shadows that are too small (<4 pixel area) to reliably identify, or too large to likely be boulders (>30 m, i.e., very coarse blocks or other megagravel, (Blair and McPherson, 1999)) are disregarded. With each shadow identified and flagged, MBARS begins the process of determining boulder morphometry based on the shadow dimensions.