Image Registration and Georeferencing in ENVI (Cavite, Philippines)
Georeferencing and Registration Methodology
The following steps were undertaken for the rectification or georeferencing of the image:1. I have created a subset of the province of Cavite from the Landsat ETM+image that I have downloaded from GLCF . A topographic map of the equivalent subset has been obtained with the following basemap information.
Fig. 1: Basemap information
Scale | 1: 50000.. |
Sheet name | Cavite, Philippines |
Sheet number | 3163 II |
Source | NAMRIA |
2. A scanned 1:50000 topographic map of Cavite appears below.
Fig. 2a: 1:50000 Topographic map of Cavite |
3. An Image-to-map registration was performed using the coordinates on the boundary of the map as ground control points. A total of 10 points are obtained with a total RMSE of 0.396853.
4. An Image-to-Image registration was later performed, yielding 9 GCPs with a total RMS error of 0.970929. The Landsat ETM+ image that I have obtained has already undergone the Level 1G correction hence the warping from the originally downloaded image to the warped image was observed.
4. An Image-to-Image registration was later performed, yielding 9 GCPs with a total RMS error of 0.970929. The Landsat ETM+ image that I have obtained has already undergone the Level 1G correction hence the warping from the originally downloaded image to the warped image was observed.
Fig. 3a: Distribution of ground control points within the Landsat ETM+ image of Cavite |
The following table are the input coordinates in solving for the parameters for transformation.
Fig. 4b : Base and warp image coordinates as ground control points
Base Image | Warp image | ||
x | y | x | y |
2197 | 467 | 4288 | 3435.75 |
2101 | 915.25 | 4255.75 | 3581.5 |
1406.25 | 1293 | 4027 | 3709 |
757.5 | 1964.5 | 3806.25 | 3933.5 |
377 | 256.5 | 3705.16 | 3376.46 |
2831.5 | 1920 | 4491.25 | 3914 |
2869.25 | 638 | 4504.5 | 3487.25 |
1804 | 1168.25 | 4156.94 | 3667.62 |
1797.75 | 797.75 | 4157.56 | 3545.68 |
Fig. 4c : Parameters for 2D Affine transformation
Parameter | Value |
a0 | 3579.92167 |
a1 | 0.32503 |
a2 | -0.00779 |
b0 | 3291.26455 |
b1 | -0.00444 |
b2 | 0.32919 |
Fig. 4d : Parameters for 2D Conformal transformation
Parameter | Value |
a1 | 0.32605 |
a2 | -0.00021 |
a3 | 3569.72659 |
a4 | 3286.96612 |
On different transformation and resampling methods in ENVI
Upon application of the three different transformation methods , the resulting images are warped differently. The figure below compares the amount of warp present in the image after each transformation using different resampling methods.
Fig. 5: Amounts of warping in the Cavite satellite image using RST, 2nd and 3rd order polynomial transformation After geometric corrections and translations, resampling is being performed to produce a better estimate of the DN values for individual pixels. In the nearest neighbor algorithm, the transformed pixel takes the value of the closest pixel in the pre-shifted array. In the bilinear interpolation, the average of the DN values of 4 surrounding pixels is used while cubic convolution averages the 16 closest input pixels. Images resampled using cubic convolution produces the sharpest image. On HOV vs LOOCV Hold-out validation (HOV) uses another set of GCPs for the same image dataset to verify the spatial accuracy of the georeferenced image. |
Fig. 6a: Hold-out Validation using a new set of ground control points of the Cavite Landsat ETM+ Image |
Shown above are the relative loaction and ditribution of the new set of ground control points. Nine (9) GCPs which yield a total RMS error of 0.448321.
Leave-one-out Cross-Validation (LOOCV) - Cross-validation uses all of the data to estimate the trend and autocorrelation models. It removes each data location, one at a time, and predicts the associated data value.
Fig.6b : Sample Leave-one-out Cross Validation |
The figure above shows how LOOCV works. After choosing the GCPs and minimizing the total RMSE to less than 1, the Image to Image GCP list was arranged such that the point with the largest RMS error appears on top of the list. This point was turned off and effectively the total RMS error of the GCPs lowered from 0.970929 to 0.818812. If we hit the predict button in the Ground Control Points Selection dialog,the cross hair on the zoom window will center on the point that will give the lowest total RMS based on the correlation of points of the image.
On Level 1G correction of Landsat images
Upon rectification, distortions caused by platform and surface geometric characteristics can't be easily distingushed since the image obtained has already undergone level1G correction which is a format created by NASA to indicate imagery that is basically ready to use .L1G" is indicative of "Level 1G", meaning the data has been processed to level 1 and is radiometrically and geometrically corrected.
On Image-to-image vs. Image-to-map registration
An image to image registration is a lot easier than image to map registration. You just have to scan the topographic map and georeference it using the graticule values given on the borders of the map. Bias on the map-scaling interpolation is removed because once you georeference the image, the coordinates on the pixel of the desired GCP within the georeferenced image is readily available. The drawback however is on the manner of scanning the topographic map. The resolution of the scanner that will produce the output topo map as well as the current state of topo map upon scanning greatly affects the quality of data one can extract in the scanned map. Folds and cramples or obliterations on the map produces significant distortions on the coordiantes derived from the map.
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