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.