Add IDW interpolation to rubbersheeting algorithm#239
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xhuang-jpl merged 41 commits intoisce-framework:developfrom Apr 16, 2026
Merged
Add IDW interpolation to rubbersheeting algorithm#239xhuang-jpl merged 41 commits intoisce-framework:developfrom
xhuang-jpl merged 41 commits intoisce-framework:developfrom
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added 30 commits
September 19, 2023 20:40
…o add_idw_rubbersheeting
added 8 commits
March 18, 2026 18:30
…o add_idw_rubbersheeting
…o add_idw_rubbersheeting
xhuang-jpl
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Apr 9, 2026
Co-authored-by: xhuang-jpl <118782850+xhuang-jpl@users.noreply.github.com>
xhuang-jpl
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Apr 9, 2026
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| ''' | ||
| Performs IDW interpolation on a 2D grid containing NaN values. |
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| ''' | |
| Performs IDW interpolation on a 2D grid containing NaN values. | |
| ''' | |
| Performs Inverse Distance Weighting (IDW) interpolation on a 2D grid containing NaN values. |
| eps: float = 1e-12): | ||
| ''' | ||
| Performs IDW interpolation on a 2D grid containing NaN values. | ||
| Missing pixels are filled using inverse distance weighting (IDW) |
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| Missing pixels are filled using inverse distance weighting (IDW) | |
| Missing pixels are filled using IDW |
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| if radius is None: | ||
| dists, idxs = tree.query(qpoints, k=min(k, points.shape[0])) | ||
| # dists: (M, k) | ||
| # idxs : (M, k) | ||
| use_mask = np.isfinite(dists) | ||
| else: | ||
| dists, idxs = tree.query(qpoints, k=min(k, points.shape[0]), distance_upper_bound=radius) | ||
| # For neighbors not found within radius, idxs == points.shape[0], dists == inf |
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need to combine together
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| zero_hit = np.any((dists <= eps) & use_mask, axis=1) | ||
| if np.any(zero_hit): | ||
| row = np.where(zero_hit)[0] | ||
| col = np.argmax(((dists <= eps) & use_mask)[row], axis=1) | ||
| filled_vals[row] = values[idxs[row, col]] |
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might not be neccessary here since the distance in 2D grid is usually > 1
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| w = np.zeros_like(d, dtype=float) | ||
| w[m] = 1.0 / np.maximum(d[m], eps) ** power | ||
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| interpolation_method: idw | ||
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| # Flag to enable the use of subswath mask for the outlier detection | ||
| subswath_mask_apply_enabled: True | ||
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| # IDW interpolation method parameters | ||
| idw_interpolation: | ||
| # Power parameter controlling the distance weighting | ||
| power: 2 | ||
| # Number of nearest neighbors used for interpolation | ||
| number: 100 | ||
| # Maximum search radius in pixels | ||
| radius: 200 | ||
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keep the default to be linear
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| # Number of nearest neighbors used for interpolation | ||
| number: int(min=0, required=False) | ||
| # Maximum search radius in pixels | ||
| radius: num(min=0, required=False) |
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| # Number of nearest neighbors used for interpolation | |
| number: int(min=0, required=False) | |
| # Maximum search radius in pixels | |
| radius: num(min=0, required=False) | |
| # Number of nearest neighbors used for interpolation | |
| number: int(min=1, required=False) | |
| # Maximum search radius in pixels, if not specified, it will use all of the valid pixels in the image | |
| radius: num(min=1, required=False) |
hfattahi
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Apr 10, 2026
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Thank you @xhuang-jpl for addressing the comments we discussed at PR review. This PR is extensively tested and the results are satisfactory. Looks great.
seongsujeong
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Apr 14, 2026
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LGTM. I have a minor suggestion for readability.
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| dists, idxs = tree.query( | ||
| qpoints, k=k, workers=-1, | ||
| **({'distance_upper_bound': radius} if radius is not None else {})) |
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| dists, idxs = tree.query( | |
| qpoints, k=k, workers=-1, | |
| **({'distance_upper_bound': radius} if radius is not None else {})) | |
| kwargs = {} | |
| if radius is not None: | |
| kwargs['distance_upper_bound'] = radius | |
| dists, idxs = tree.query( | |
| qpoints, | |
| k=k, | |
| workers=-1, | |
| **kwargs | |
| ) |
for readability
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This PR is to add the IDW (inverse distance weighting) algorithm to the rubbersheeting algorithm. This is based on our comparison that the IDW performs slightly better than the linear interpolation.