Sessions: Fix compilation without ISIS

An include for an ISIS header was incorrectly placed outside of a
bundle_adjust: More fine-tuning
pc_align: Try to read any format ASP supports
stereo.cc: Sanity checks
NEWS: Read DEMs in the ISIS cube format
stereo: Put messages later so they make it to the log
Clarify install guide
OrthoRasterizer: Relax the assumption that a point cloud is roughly continuous
NEWS: Robustness to large jumps in point cloud values.
OrthoRasterizer: Simplify code, this is no longer necessary
parallel_stereo: Rm debug statement
stereo_tri: Code for filtering out large error moved to point2dem
When rounding point clouds, do it proportionally to planet radius
docs: Fix format
Pulled in David's changes to sparse_disp
sparse_disp: changes to rounding to improve compatibility

Older python versions require indices to be integers, replaced some instances of floor() with int()
sparse_disp: Add option to fall back to full-res template match

In some smooth-topography cases, the initial half-resolution search was giving poor results.  Now check whether the half-res search gave an answer, fall back to full-res if not.
sparse_disp: error handling in est_epipolar_vec

Check if there are any values with good correlation before running the epipolar estimate
sparse_disp: Add maximum-disparity-cluster test

This option improves on the maximum-disparity-distance test in previous versions.  Now we identify all matches with C> C_tol and establish how well they are clustered in disparity space based on their minimum disparity difference with a neighbor.  The 90th percentile of the clustering distribution in each direction is used as a filter on the matches with C< C_tol.  This tends to eliminate spatailly isolated blunders with poor correlation score.  This test is enabled by default, disabled by invoking the --Exhaustive option.
sparse_disp: local_epipolar option, cleanup

For very large images, the distortions introduced by rpc mapping can lead to variations in the effective epipolar axis.  This commit breaks the image into large chunks and estimates an epipolar vector for each, falling back to the global epipolar vector if there are not enough high-quality points available.

This commit also removes the ep_vec_initial option, which was mostly unused, and cleans up some sloppy filtering statements in the code.