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Techniques such as clinometry, stereoscopy, interferometry, and polarimetry are used for Digital Elevation Model
(DEM) generation from Synthetic Aperture Radar (SAR) images. The choice of technique depends on the SAR
configuration, the means used for image acquisition, and the relief type. The most popular techniques are interferometry
for regions of high coherence and stereoscopy for regions such as steep forested mountain slopes. Stereo matching,
which is finds the disparity map or correspondence points between two images acquired from different sensor positions,
is a core process in stereoscopy. Additionally, automatic stereo processing, which involves stereo matching, is an
important process in other applications including vision-based obstacle avoidance for unmanned air vehicles (UAVs),
extraction of weak targets in clutter, and automatic target detection. Due to its high computational complexity, stereo
matching has traditionally been, and continues to be, one of the most heavily investigated topics in computer vision. A
stereo matching algorithm performs a subset of the following four steps: cost computation, cost (support) aggregation,
disparity computation/optimization, and disparity refinement. Based on the method used for cost computation, the
algorithms are classified into feature-, phase-, and area-based algorithms; and they are classified as local or global based
on how they perform disparity computation/optimization. We present a comparative performance study of two pairs, i.e.,
four versions, of global stereo matching codes. Each pair uses a different minimization technique: a simulated annealing
or graph cut algorithm. And, the codes of a pair differ in terms of the employed global cost function: absolute difference
(AD) or a variation of normalized cross correlation (NCC). The performance comparison is in terms of execution time,
the global minimum cost achieved, power and energy consumption, and the quality of generated output. The results of
this preliminary study provide insights into the suitability and relative merits of these algorithms and cost functions for
execution on field-deployable and on-board computer systems with size, weight, and power (SWaP) constraints. The
results show that for 12 out of 14 instances the graph cut codes, compared to their simulated annealing counterparts
provided a 35-85% improvement in energy consumption and, therefore, are promising candidates for use in field-deployable
and on-board systems.
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