I'm trying to use COSI-Corr to look at movement along slow moving landslides in Central California.

I have NAPP aerial photographs (scanned at 14 microns) from 1989, 1994, and 2002. I also have a LiDAR DEM (gridded to 0.5 meters, flown by NCALM for the EarthScope project) which covers a portion of the study area. I've been using a hillshade from the LiDAR as a master image to set ground control points and rectify the aerial photo. I've typically been using 30 GCP for my orthorectification.

I've been running into some problems which I think are due to the fact that my LiDAR does not completely cover the area I'm interested in using COSI-Corr over.

Here's a snapshot of my DEM:

The landslide I'm interested in is ~90% contained within the LiDAR data, with just the toe cut off. In order to avoid problems with outliers/negative values/etc, I've set all the no data points in my DEM to approximately the mean value of the DEM.

When I attempt to resample my NAPP image using the mapping matrices from the full extent of the LiDAR above, I get a rather blurry image:

(note the hdr file for my resampled image has the line: Kernel: Sinc Sinc Size : 25 Distance Max dx=31.4428 dy=10.5500)

I've done this same procedure on other nearby landslides and had only slightly blurred images:

Here's the other DEM:

And the resulting resampled image (Kernel: Sinc Sinc Size : 25 Distance Max dx=3.1748 dy=5.8180):

So, I'm not really sure why this time around it turned out significantly more blurry than previously, but (following instructions from the above post) I attempted to solve the bluriness problem by computing the resampling distances on a subset of the mapping matrices. This worked to create a much crisper image (dx=1.8044 dy=1.1025), however I noticed that the areas of the image within the extent that I selected for the the subset of the mapping matrices seems to be better rectified than other parts of the image (I've observed trees on stable ground offset by 1-5 meters between the LiDAR and resampled image). Given the geometry of the landslide I'm looking at, the extent of the LiDAR, and the fact that (as far as I can tell) one must use a rectangle to define the subset area, I'm not able to choose a subset such that the entire landslide area and immediate surrounding terrain is included within the subset.

So I guess I have a few questions:

1. Does it seem likely that the blurry resampled images are due to the inclusion of no data regions (replaced by mean values of elevation) in the DEM? (This is what I've assumed is causing the problem, but I don't really know)

2. If so, is there a way to define a polygon as the subset area to compute the resampling distances over instead of a rectangle. This would allow me to select the entire landslide without including any no data values.

3. Does it seem likely that the offset I observed in my non-blurry resampled image is due to the fact that it's outside the spatial extent of the subset of the mapping matrices selected? Or are there other factors which could explain why some parts of my image are near perfectly rectified (the LiDAR and resampled image match) and others are slightly offset?

Please let me know if I need to clarify any of these questions.

Many thanks,

Joel

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I am having difficulty orthorectifying a couple of SPOT IV images at the moment. My GCPs are nicely optimized, and I have a good quality DEM (20m from SPOT), but the resampled image is quite blurred when compared to the original – see attached. This has obvious implications for my next step of correlation.

Does anything spring to mind that might be the cause of this?

By the way I should also say that I have rectified two SPOT V images in exactly the same manner as I do the SPOT IV, and they are co-registered to better than a pixel and the detail is retained perfectly.

Any pointers greatly appreciated!

Best wishes,

Duncan

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My answer:

Dear Duncan, you are experiencing a classical issue in COSI-Corr, but don't worry, there is a way around it. First, the most likely reason for this pb:

The orthorectification mapping generates an association of coordinates between raw image coordinates and ground coordinates. The local deformation and irregularities of this mapping depend primarily of the local perturbation and slope variations of the topography. However, for simplicity, the resampling kernel used does not vary over a given image. This means that we are using a kernel that takes into account the worst deformation possible in the image to resample it (we have an adaptive version of the resampling overcoming this approximation, but we haven't released yet, it will be included in the next release in a few months). Thus, if the topography of your scene has areas of very steep gradients, and areas of flat topography, high frequencies of the image will be lost on areas of flat topography.

However, in most practical cases, the frequency content variations in a medium resolution image are negligible. In your case, because your image (10m) and your DEM (20m) do not have a very high resolution, it is very likely that steep topography gradients are introduced by outliers in your DEM.

The maximum distortion in the ortho-rectification mapping, hence the image frequency content, is determined by the variables dx and dy that are recorded in the header file of your ortho-image. If you edit the header file of your ortho-image, in the description section, you will find a line that looks like this:

"Kernel: Sinc Sinc Size : 25 Distance Max dx=1.2937 dy=1.1723"

Please post this line.

In your case, you must have fairly large dx and dy values. What are these values for your SPOT 5 image?

The possible fixes, by order of likeliness:

1 - Check that you don't have any missing values or outliers in your DEM. In ENVI, you can open your DEM file in a display, then right click on it and "Quick Stat". The min-max values will tell you if all elevations are within a physical range, and they should be. If they're not, they should be replaced and interpolated to be in a physical range. When this is fixed, the ortho + resampling should be recomputed.

2- If no outliers or missing values are detected in the DEM, it is possible to compute de resampling distances dx and dy only on a selected subset of the mapping matrices. You can also estimate them using the tool "matrices interdistances" and force them manually during resampling. Please refer to the COSI-Corr manual for these functions, or we can discuss it here later if needed.

3- You will need an adaptive resampling kernel. In your case, it shouldn't be necessary unless your image has a large incidence angle. We could potentially send you an early beta version of the next COSI-Corr release if this is what you need.

Let us know,

Sebastien

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